Flood Risk andClimate Change
Hokkaido
Joined project of Japan and the Netherlands

Client

RVO, Partners for Water

Flood Risk andClimate Change
Hokkaido
Joined project of Japan and the Netherlands
Final report

Authors
Maaskant, Bob (HKV)
Kolen, Bas (HKV)
Chiba, Manabu (RIC)
Hegnauer, Mark (Deltares)
Uemura, Fumihiko (Docon Co.)
Yamada, Tomohito (Hokkaido University)

PR3983.10
January 2022

Preface
I would like to congratulate all the experts from the participating Japanese and Dutch universities,
research institutes and companies with the performed flood risk analysis for Obihiro City within the
Tokachi river catchment. All the work that has been done and despite of the covid-19 restrictions.
When I returned home from our visit to Hokkaido in November 2018 (on which I still look back with
nice memories), I would not have thought that the further cooperation would be so successful!
A new risk-based approach has been developed by integrating Japanese climate projection models
and Dutch flood risk approach. Japanese and Dutch experts worked closely together on the
different topics, like (i) extreme rain fall and river discharge, (ii) levee failure probabilities and (iii),
flood consequences (damage, life loss) and flood risk. Not only discussing results and methods but
also applying these methods together and really trying to understand the details to generate theresults. By working in this way a mutual understanding and trust is created to have a goodfoundation for the added value of the project results.
For the Netherlands it has been very valuable that our flood risk approach has been ‘reviewed’ by
Japanese high level experts. In depths discussions resulted in useful suggestions for further
improvement. We can learn from Japanese methods and models, like we can learn from theJapanese enormous experience on emergency response and flood fighting as well.
Proposals already have been made for further cooperation next years. Rijkswaterstaat looks
forward to participate in a new research project, for example on how to deal with extreme weather
within a changing climate and flash floods in particular. This summer we experienced a very
unusual flooding along the tributaries of the Meuse river in Limburg, as a result of exceptional
precipitation over a very large region in Belgium, Germany and the Netherlands. This resulted incatastrophic flash floods in Germany and Belgium with many casualties. Since Limburg is lessmountainous, we only experienced a lot of damage fortunately. But we were not well prepared forsuch a (limited) flash flood. And the current version of the precipitation generator in our GRADE
instrument could not reproduce the observed peak flows on the Meuse river.
I believe that the Netherlands can learn much from experiences in Japan especially on flash floods
and I hope we can work further together on flood protection within a changing climate. I lookforward to participate with our Japanese partners in a new research project.
Finally I would like to thank the Netherlands Enterprise Agency for funding this ‘Partners for Water’cooperation.
Durk Riedstra
Senior Advisor Flood Risk Management
Rijkswaterstaat
Ministry of Infrastructure and Water Management

Table of contents
1

Introduction

1

1.1

Climate change is already happening

1

1.2

The need for joined research

5

1.3

Objective of the study

6

1.4

Cooperation between team members in joined project7

2

Introduction in Flood risk management in Japan (Hokkaido) andthe Netherlands

9

2.1

Japanese Flood risk policy

92.2

Dutch Flood risk policy

11

2.3

Challenges with regard to climate change

143

Methodology of the flood risk analyses

17

3.1

General approach

17

3.2

Case study area

19

3.3

Part 1: extreme rainfall and river discharges

20

3.4

Part 2: Failure probability of levees

26

3.5

Part 3: Damage, evacuation, loss of life and flood risk

32

4Main findings

39

4.1

Application of the method

39

4.2

Dealing with uncertainties

44

4.3

Working together in a joined project44

5

Recommendations

47

5.1

Extreme rainfall and discharges

47

5.2

Failure probabilities

48

5.3

Damage calculation

48

5.4

Evacuation and loss of life

48

6

Last words

49

1

Introduction

1.1

Climate change is already happening

The history between the Dutch and Japanese water sector goes a long way back. In previous
century, the Dutch approaches for water management and flood prevention have been adopted by
Japanese water sector. Johannes de Rijke is world famous in Japan because of his work since 1890.
Since then, both countries went their own way and developed and fine-tuned their methods to theirown situation. Until recently when Hokkaido was hit by several typhoons, causing major floods anddamage to people and the agricultural sector. This was reason for Japan to reinvest in the relationand knowledge exchange with Netherlands water experts from Rijkswaterstaat, HKV, Deltares andDelft University.
The consequences of climate change are already happening in the world, and as the predictions of
the IPCC show the consequences of climate change will increase. This results in an increase in floodrisk, but the flood risk can also be mitigated by prevention and flood control measures, smart
urban planning or disaster management. In this chapter we give some example of theconsequences of climate change in Japan and The Netherlands with regard to flooding.

1.1.1

Global, Japan and Asia

According to the 5th Assessment Report by IPCC, there is no doubt of global warming and theworld average temperature has already risen by 0.85 degree, compared to 1850 before Industrial
Revolution. It is certain that the main cause of this is human activity, associated with increase ofgreenhouse gas density, such as CO2. It is forecasted that the temperature will rise further.
Impact of global warming could extend not only to temperature rise, but to increase of frequency
and intensity of extreme events, such as see level rise and heavy rain. It is highly expected by theend of this century to see extreme rainfall more intense and more frequent in most of mid-latitude
land area and humid tropical area.

Figure 1 Climate change

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1

The observed data of the last decades indicates that temporal and intense rainfall will increase inJapan, the target area of this project. Every year record rainfall causes disasters, associated with
Typhoon/Seasonal front with heavier rainfall. Temperature rise leads to higher moisture in the air,and there is a growing concern that the frequency and intensity of rainfall would continue toincrease.
The increasing trend of frequent and intense rainfall is not only occurring in Japan, but also other
Asian countries suffer from heavy rain and severe flood.
Figure 2 Recent floods in different places in Japan

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Figure 3 Assumed Rainfall by Remote Sensing from Satellite (June 1st to July 20th, 2020)
https://earthobservatory.nasa.gov/images/147006/excessive-monsoon-rains-flood-asia

1.1.2

The Netherlands

Also the Netherlands have to face the consequences of climate change. The sea level is expected torise, the average temperature rises, rainfall patterns change which cause more heatwaves anddrought but also more extreme rainfall. In the field of flood risk management there are several
policy lines to deal with climate change, some examples:•

Levee Reinforcement. The levee is designed takin into account the future hydraulic conditions
including climate changes.

•

Delta Program spatial adaptation. This program stimulates the adaption of the build
environment to be less vulnerable for the consequences of climate change.

•

Knowledge program Sea Level Rise. In this program a better understanding is created about
the possible sea level rise for the Netherlands, the impact and possible mitigations strategies.

The need for this research is illustrated by the summer of 2021 in which the area of Limburg faced
a period of extreme rainfall which was related to the impact of climate change. Below is a summaryof the flood event based on the fact finding research which was executed by ENW. A full description
can be found at: https://www.enwinfo.nl/publicaties/.
Precipitation accumulated to 160 to 180 mm in two days over a large region. Similar heavy
precipitation events in this area are rare and furthermore have never been registered in thesummer season. The probability of occurrence is much smaller than can be directly derived fromobservations of past events, although a combination of measurements from past event with a large

PR3983.10 • January 20223

ensemble of model simulations indicates the probability of the Meuse two-day rainfall and peakdischarge is on the order of 1:100 to 1:1000 per year.
Extreme rainfall was experienced simultaneously in a large area that included several tributaries ofthe Meuse in Belgium and the Rhine in Germany and Luxembourg. Due to the hilly terrain, the rain
runoff immediately entered the rivers. Several villages in narrow valleys close to the Vesdre, Ahrand Sauer rivers were heavily affected and partially destroyed. Infrastructure (buildings, bridges,
road, rail) was completely washed away. Hundreds of thousands of inhabitants were affected andmore than 200 people died. The damage is estimated to be in the order of tens of billions of euros.
In Belgium and Germany, damage and losses were the largest and only in these countries were
fatalities recorded. The flood event in these countries was catastrophic, amongst others due toeven larger rainfall amounts, steeper terrain and fast-flowing rivers.
In the Netherlands recorded peak discharges on the Meuse at Eijsden and regional tributaries were
the highest ever recorded. Water levels on the Meuse downstream of Roermond were lower than inprevious peak events and also lower than would be expected based on available model results. The
lower water levels result from implementation of the large scale room for the rivers programme,‘Meuse Works’, as well as a strong flood wave flattening in the downstream parts of the system.
The probability of occurrence of the recorded water level is around 1:200 per year for the Meuse at
Borgharen and decreases to 1:15 per year downstream. The main flood defences along the Meuse
river in the Netherlands performed well and did not breach.
In the tributaries in Limburg the probabilities of occurrence of the recorded water levels vary
widely: at many locations along the rivers Geul, Geleenbeek and Roer, probabilities are estimatedto be between 1:100 and 1:1000 per year.
The floods in the Netherlands led to severe economic damage and losses in the affected area. A
first estimate shows that more than 2.500 houses, more than 5.000 inhabitants and around 600
businesses are affected. Physical damage to houses and businesses, business interruption, damage
to infrastructure and crop losses are the most significant. The observed damage to individual
structures (residential and commercial) is highly variable. The estimated damage in the affectedarea is clearly larger than for the river floods in 1993 and 1995 (converted to 2021 prices: around€ 200 million and € 125 million, respectively, excluding damage due to business interruption). It is
important to note is that the largest damages and losses occurred in the regional rivers, mainly in
the Geul floodplain, while in 1993 and 1995 most damage and losses were recorded in the main
Meuse floodplain.
Around 50,000 people along the flooded rivers have been evacuated. Along the river Geul,
specifically in Valkenburg, most people were not evacuated before the flood arrived, with the
exception of some campgrounds and 193 people in health care institutions. Along the river Meuse,
most people were evacuated before the arrival of the peak discharge. Finally, no fatalities werereported in the Netherlands and the majority of the people evacuated themselves.
The majority of healthcare workers – around two thirds - reported an increase of patients withpsychological complaints (i.e. stress, concerns, and anxiety). The impact of the floods on thedrinking water extraction for the Dunea water supplier was limited, but substantial for suppliers
Evides and WML. The drinking water well of the Maas and Eyserbeek were temporarily closed due
to chemical and microbiological contamination. The reserve capacity was sufficient to prevent
shortages of drinking water supplies, also due to the wet and mild summer. The province Limburg

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had the highest COVID-19 risk ratio among provinces of the Netherlands and also had the largest
increase of new cases after the floods. The flooded municipality Valkenburg aan de Geul showed arisk ratio of >5 (and is within the top 5 of Limburg), suggesting that the floods may havecontributed to SARS-CoV-2 transmission. However, the differences appear to be small and may
have only occurred locally.

1.2

The need for joined research

Japan has been suffering from flood caused by the record heavy rain every year, and it is clear that
we are confronting the impact of climate change. Ministry of Land, Infrastructure, Transport andTourism (later referred to as MLIT) has changed its policy, introducing “River Basin Disaster
Resilience and Sustainability by All,” because of the recent increase of external factors andfrequent flood occurrence associated with climate change, while it used to be satisfactory enoughby maintaining flood control facilities, such as levees and dams.
River Basin Disaster Resilience and Sustainability by All is an approach to reduce flood risk of thewhole basin, by building up the cooperation by all the relevant organizations around the basin. It
requires a quantitative understanding, for instance, time-space distribution of flood and its
probability assessment. This is why a new approach developed by this project, integrating
Hokkaido and Dutch Models, could be an excellent tool to assess quantity of flood risk change due
to climate change.
Most of the surrounding countries have the similar challenges, for example, confronting climatechange and flash floods because of rapid rainfall. It is, therefore, potential to make this case anadvanced example to prevent and reduce damages of frequently occurring floods.
In addition, Europe including the Netherlands experienced unprecedented heavy rainfall disasters inJuly 2021, and the risk of disasters due to increased precipitation and localized heavy rainfall
associated by climate change is increasing.
Japan's wealth of knowledge on how to respond to rapid runoff and disasters caused by localized
heavy rain is highly beneficial to European countries, including the Netherlands.
The flood risk assessment model (hereinafter referred to as Dutch Model), developed by Floris
Project in the Netherlands, is an advanced model to analyze different flood scenarios
comprehensively, assessing the distributions of human risk and economic risk quantitatively andstochastically. Dutch model has a solid reliability, being already introduced all over the Netherlands
as the basis of risk based approach.
On the other hand, the climate projection model developed in Hokkaido, Japan (herein afterreferred to as Hokkaido Model), is extremely elaborate and large data of climate projection, basedon ensemble climate projection data of past 3,000 years, future 3,240 years by 2 degree rise, andfuture 5,400 years by 4 degree rise, physically downscaled to 5km horizontal resolution, by meansof regional climate model with reputation in weather forecast in Japan. This model makes it
possible to reproduce metrological phenomena with high accuracy, such as topographical rainfall
due to the steep topography of Japan. In addition, it enables statistical analysis, since it covers
wide range of physically possible rainfall phenomena in the past and the future.
Hokkaido Model with rich probability information based on physics simulation and Dutch Model withexcellent flood risk assessment are complementary to each other. A new approach developed bythis project, combining both knowledge, is highly potential to apply for many different rivers
worldwide.

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5

Hokkaido has built up a continuous technical exchange with Dutch experts, and based on theknowledge obtained, studies have been initiated with reference to Dutch methods such as fatality
estimation.
In January 2018, Hokkaido members visited the Delta Commission, Rijkswaterstaat, KNMI, HKV,
and others to exchange opinions, which marked the beginning of technical exchange between thesetwo countries. In November 2018, Dutch experts were invited to the Japan-The Netherlands FloodControl Seminar in Hokkaido, and a special session was held at the 63rd Conference on HydraulicEngineering of the Japan Society of Civil Engineers. In June 2019, Hokkaido members paid thesecond visit to the Netherlands and exchange their perspectives again.
Based on the background of these technical exchanges, a consortium of experts from bothcountries was formulated to solve the technical issues mentioned above.
This consortium consisted of an existing network of Japanese and Dutch universities, research
institutes, companies and governments. This network was developed in the last two years after
recent floods in Hokkaido based on the need to cope with climate change. The top experts are
involved in this network, since only they can give the added value to fill the requirement of Japan.
These experts can be called knowledge entrepreneurs. This approach enables Dutch expertsto getconnected to Japan and creates the (once in a lifetime) opportunity for further upscaling.
We have a mix of Dutch and Japanese top experts from universities, research institutes,
consultants and governments:•

Universities (Safety and Security, Civil Engineering): Delft University and Hokkaido University•

Private organizations and research institutes (consultants), with a strong R&D focus on floodrisk management: HKV, Deltares, Docon Co., Ltd, River Center of Hokkaido•

Government as counterparts: 1) Rijkswaterstaat and 2) Hokkaido Regional Development
Bureau as a part of the MLIT

The Dutch partners are complementary to each other. They are all specialized in flood risk analysis
with different focuses. HKV as a consultant will manage the project, is expertized in risk analysis
and has much experience in developing and applying the Dutch methods to flood safetyassessment in the Netherlands. Deltares as a water knowledge institute facilitates access to a large
variety of relevant researches and models, and has much experience in the application of the Dutch
flood risk methods to many countries around the world. TU Delft supports in depth knowledge
development, review and quality control. Through their network in Japan and South East Asia we
can also disseminate the result of the project.
The Japanese partners are also complementary. The River Centre of Hokkaido is responsible for thecoordination of the water safety research, and Hokkaido University has expertise on the projectionsof quantified change of rainfall due to climate change of typhoons and the rainfall. Docon Co., Ltd
has expertise on flood risk and relevant technical development.
The River Centre of Hokkaido, Docon Co., Ltd and Hokkaido University work together to establish a
new flood risk assessment method, by scientifically evaluating flood risk increase based on
projection of quantified change of rainfall due to climate change.

1.3

Objective of the study

The long-term missions of this study are as follows

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1. Technical development of a new flood risk approach in Hokkaido by integrating flood risk
analysis and failure probabilities of both countries. The integration of Dutch and Japaneseapproaches is the first step in the long term ambition to upscale the method to other regions
around the world. The next step after this project aims to adopt the approach in entire Japanand the last step to explore the potential to use in South East Asia.
2. Sustainable relation between Dutch and Japanese researchers and experts resulting in joint
proposals for research programs (as Horizon2020 in the Netherlands), publication of thesis forJapan Society of Civil Engineers, PhD positions, and knowledge development in national
research programs of Rijkswaterstaat (as NKWN, WBI) and technical proposal for MLIT.
To realize these ambitions, roadmaps for business and research will be developed during the
project to upscale the approach.
To make sure of the above opportunities, it is required to achieve the following.
•

Ensure the clear achievement of this project.

•

Clarify technical challenges of this project to make a proposal for the following term with thesolutions.

•

Generalize the result of this project to other rivers in Japan.

•

Share the information and understanding with the stakeholders, who would be involved with
the next project and other rivers to apply its results.

•

Disseminate the results of this project by means of academic conferences.

•

Develop cooperation between the Netherlands and Japan through this project, by building up asustainable structure of technical and human exchange.

1.4

Cooperation between team members in joined projectThis consortium builds on an existing network of Japanese and Dutch universities, research
institutes, companies and government which started in 2018. after recent floods in Hokkaido andthe need to cope with climate change. For the entire consortium the joined project was a key
element to develop a sustainable network and improve methods and insights During the project wehave worked in combined project teams of Japanese and Dutch experts. A project team wasformed to define the hydraulic loads based on the rainfall, a team for assessment of the levees anda team for the risk and loss of life assessment. For the interaction between these topics we hadphysical meeting and, because of COVID-19, online meetings. During these meetings we had in
depths discussion about these topics, and the application of the methods to the case study. During
the physical meetings we also had social activities which contributed to a better understanding ofeach other. The joined project had added value for all partners and individual experts, which forexample was shown in the project team which also had no changes.
The experts in Hokkaido, as part of the consortium but also the Japanese civil engineeringcommunity is open to develop this relation now. The “seminar for Water Management in the
Netherlands and Japan on 29th November 2018” was attended by many civil engineers (>200) andthere was a lot of interest in the Dutch method (see item newspaper). This proposal is the next
step after the joined meetings in 2018 in the Netherlands (January) and Hokkaido (November)which included the open seminar for the Japanese water sector initiated by the Japanese partners.
We have a mix of Dutch and Japanese top experts from universities, research institutes,
consultants and government:•

Universities (Safety and Security, Civil Engineering): Delft University and Hokkaido University

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7

•Private organizations and research institutes (consultants), with a strong R&D focus on floodrisk management: HKV, Deltares, Docon Co., Ltd, River Center of Hokkaido•

Government as counterparts: 1) Rijkswaterstaat and 2) Hokkaido Regional Development
Bureau as a part of the Ministry of Land, Infrastructure, Transport

The consortium partners cover the entire field of flood risk modelling (in depth knowledge of civilengineering, statistics and governance). From early warning and climate change modelling,hydrologic, hydraulic and morphologic processes as well as the assessment of damage, loss of life,
evacuation and the use of cost benefit and multi criteria analyses.
The Dutch parties are complementary to each other. They are all strong in flood risk analyses but
focus on different subjects. HKV as a consultant will manage the project HKV is expert in risk
analyses and has much experience in development and application of the Dutch methods for flood
safety assessment in the Netherland. Deltares as a water knowledge institute facilitates access to alarge variety of relevant research and models and has much experience in the application of the
Dutch flood risk methods in many countries around the world. TU Delft supports in depth
knowledge development, review and quality control.
The Japanese parties are complementary. The River Centre of Hokkaido is responsible for thecoordination of the water safety research, the Hokkaido University has expertise on the projectionsof climate change of typhoons and the rainfall. Docon Co., Ltd has expertise on flood risk andrelevant technical development.
The advantage of involving both Japanese and Dutch parties with a high knowledge level is thatboth parties can learn a lot from the project, and have the availability to apply this knowledge fordifferent goals.

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2Introduction in Flood risk
management in Japan(Hokkaido) and theNetherlands

2.1Japanese Flood risk policy

The current flood control scheme of Japan estimates extreme rainfall events, such as annual
exceedance probability 1/150, by statistically analyzing observed data over the past several
decades. The outliers are excluded from the analysis to express the probability rainfall
deterministically (on the assumption that a rainfall for a given annual exceedance probability isdetermined).
Based on the long-term basic river maintenance policy, dependent on the characteristics of eachriver basin, runoff is calculated for several rainfall sets estimated for a given annual exceedance
probability. This determines the design runoff for each tributary and reference point. This is called
the basic high water, and its guidelines are described in “The Japanese Ministry of Land,
Infrastructure, Transport and Tourism Technical Criteria for River Works” 1). In addition, a river
improvement plan is formulated with specific targets to be achieved within a period of about 20 to
30 years, and river channel excavation, flood control facilities, levees, and other facilities aremaintained for each river to keep the water level below the design high water level during floods.
Figure 4 The flood control scheme of Japan

Source:“New Planning System for River Improvement” Sapporo Development Construction Department, Hokkaido
Regional Development Bureau of MLIT, https://www.hkd.mlit.go.jp/sp/kasen_keikaku/kluhh4000000ft54.html

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Reflecting the history of Japan's flood control policy, Dutch engineers came to Japan in 1870 to
introduced modern river engineering. The target runoff for river maintenance was determinedbased on the observed maximum flood runoff in the past. The first version of “Technical Criteria forRiver Works,” formulated in 1953, established a probabilistic method to set up a standard highwater according to the annual exceedance probability, as described above.
Furthermore, the ensemble climate projection database d4PDF (Mizuta et al, 2017) has recently
been developed in Japan. This database made a large amount of projected information available,
for the past 3,000 years (60 years x 50), for 3,240 years (60 years x 54) by 2°C rise in the future,and for 5,400 years (60 years x 90) by 4°C rise in the future. This information is based on a large
number of detailed physical simulations using supercomputers of past and future possible weather
phenomena (including rainfall events that have never been experienced before), which makes it
more reliable to estimate of probability rainfall.
Though the hazard-based flood control planning according to observed rainfall has been used inJapan, it is required to shift to a flood control planning based on projections, due to the growing
climate change impacts.
Figure 5 The history of High Water Planning in Japan

Adapted from "Historical Study on Institutionalization of Basic High Water", S. Nakamura,https://repository.dl.itc.u-tokyo.ac.jp/records/7541#.YbhbvVlUthE

In 2020, MLIT has announced a shift to "River Basin Disaster Resilience and Sustainability by All,"
which aims to reduce damage through collaboration among all parties involved with the basin, inlight of the increasing frequency and severity of disasters in recent years. In contrast to theconventional river planning, which mainly focuses on river management facilities to keep floods
below the design scale to ensure safety, River Basin Disaster Resilience and Sustainability by All is
a comprehensive and multilayered approach that considerswatershed, river area, and flood prone
area as a single basin in order to reduce damage of floods even beyond the estimated level. River
Basin Disaster Resilience and Sustainability by All also covers tips to live in the flood prone area
and to control flood flows. The risk-based approach is beneficial to quantitatively assessing floodrisk and to understand the effects of measures. The attempt of this study to integrate scientificclimate change projection and probabilistic risk assessment will be an extremely important
knowledge to improve future safety of flood control in Japan.

PR3983.10 • January 202210Figure 6 River Basin Disaster Resilience and Sustainability by All, MLIT,
Source：https://www.mlit.go.jp/river/kokusai/pdf/pdf21.pdf

2.2

Dutch Flood risk policy

The Netherlands is a small country, lying in the delta of several rivers (Rhine, Meuse, Scheldt) andbordering the North Sea. As a result, the Netherlands is prone to flooding, especially because
climate change and the rising sea level will be causing increasingly frequent higher water levels
and more extreme discharges.
Without flood defences, such as levees and storm surge barriers, 60 percent of The Netherlands
would be inundated on a regular basis. These areas accommodate some nine million people andsome seventy per cent of our gross national product is earned here. Consequently, adequate floodrisk management is vitally important.
The flood risk policy is being co-designed and developed within the Dutch National Delta
Programme.
The Dutch National Delta Program (source: Deltaprogramma.nl)
The government seeks to protect the Netherlands, now and in the future, against flooding and tosecure a sufficient supply of fresh water. Furthermore, the government seeks to render our

PR3983.10 • January 202211

country climate-proof and water-resilient. The plans to this end are set down in the Delta
Programme.
The aim of the Delta program is to ensure that our flood risk management, freshwater supply,and spatial planning will be climate-proof and water-resilient by 2050, so that our country willcontinue to be able to cope with the increasing weather extremes. This time around we will try
and prevent a disaster, rather than devise measures on the aftermath.
By 2050, the Netherlands must be climate-proof and water-resilient. This means that our floodrisk management, freshwater supply, and spatial planning must be up to par. Only then will ourcountry continue to be able to cope with the impact of climate change.
Along with its partners, the government has adopted a new approach to working on the delta:•

New flood protection standards have been implemented. Rather than focusing on theprobability of flooding, the government also considers the potential impact of a flood (riskbased approach). The stringency of the standards is determined by the scope of the potential
impact;•More insight will be provided into the availability of fresh water for agriculture, industry, andnature;•Spatial planning in the Netherlands will be more climate-proof and water-resilient.

One of the Delta decisions is on flood risk management. The essence of the Delta Decision on
Flood Risk Management is that by no later than 2050, the probability of fatality due to flooding
must not exceed 1 in 100,000 per annum (or 0.001 %) for every resident living behind the
dykes. This is the so-called tolerable individual risk.
The Delta Decision on Flood Risk Management is underpinned by a risk-based approach. This
enables more targeted investments in the flood protection of the Netherlands. Every resident
living behind the dykes will have the same minimum level of flood protection. At locations where
a flood would have a major impact (e.g., many casualties, great economic damage, and/or
damage to vital infrastructure of national significance), the protection level will be raised.
Between 2015 and 2020, great progress has been made in the implementation of the Delta
Decision on Flood Risk Management. For example, the protection targets have been translated
into standards for the primary flood defences: the dykes, dunes, dams, and storm surge barriers
that protect our country against flooding from the sea, the major rivers, and the major lakes.
These standards have been set down in the Water Act, which came into force on 1 January
2017, Figure 7.
The first National Round of Assessments of these primary flood defences commenced in 2017. A
new set of statutory instruments is available to this end: a set of agreements and methods forthe assessment of the primary flood defences. By no later than 31 December 2022, the Ministerof Infrastructure and Water Management will report to the Senate and the House ofRepresentatives on the condition of the primary flood defences, on the basis of the first National
Round of Assessments. The second National Round of Assessments will run from 2023 to 2034;

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12Figure 7 Dutch Safety Standards

Furthermore, the Flood Protection Programme has been rolled out. In this Programme, the 21
district water boards of the Netherlands and the central government are collaborating on thelargest dyke improvement operation since the Delta Works. The Flood Protection Programme is
aimed at ensuring that the primary flood defences managed by the district water boards willmeet the standards by 2050. This involves nearly 1,300 kilometres of dykes and nearly 500
sluices and pumping stations. Many dyke improvement projects are already under way.

The Dutch National Delta program gives a good overview of the Dutch Policy on flood safety andthe implementation of the risk-based approach on coping with flood risks.

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2.3

Challenges with regard to climate change

2.3.1

Japan

August 2016 Heavy Rain Disaster in Hokkaido triggered Hokkaido Regional Development Bureau,
MLIT, and Hokkaido Prefecture established the expert committee 1), in order to review the disasterand consider future flood prevention measures. The committee concluded that "predictions andconcerns about intensified flood damage due to the impact of climate change have been recognized
as a reality", and that "the impact of climate change should be scientifically predicted," and "flood
control measures should implement specific risk assessments".
In response to this report, climate change impact projections and flood risk assessments based onlarge ensemble climate data 2) were conducted in 2017. This has led in 2018 – 2019 to the study offlood control plan associated with climate change by MLIT,3) and the adaptation measures to besocial implemented for a river basin in Hokkaido.4) The large ensemble climate data covers diverse
meteorological phenomena that can physically occur in past and future climates, allowing
quantitative assessment of extremely rare heavy rainfall events, based on their occurrence
probability. These studies are not only the first of their kind in the country, but have also attracted
global attention, as they were reported at the regular meeting of the United Nations Framework on
Climate Change held in Bonn, Germany on 20 June 2019 5) 6).
Figure 8 Challenges with regard to climate change in Japan

1) Committee on Flood Disaster Prevention Response to 2016 Hokkaido Heavy Rain Disaster, 2017,https://www.hkd.mlit.go.jp/ky/kn/kawa_kei/ud49g7000000f0l0.html
2) Hokkaido Technical Committee for Climate Change Projection (Water Field) , 2018,https://www.hkd.mlit.go.jp/ky/kn/kawa_kei/splaat000000vdyw.html
3) The Expert Group Meeting for Flood Control Plan under Climate Change, 2019,https://www.mlit.go.jp/river/shinngikai_blog/chisui_kentoukai/index.html

PR3983.10 • January 2022

144) The Expert Group Meeting for Flood Control Measures under Climate Change in Hokkaido Region,
2019, https://www.hkd.mlit.go.jp/ky/kn/kawa_kei/splaat000001offi.html

5) Tomohito Yamada, Adaptation measures for extreme floods using huge ensemble of highresolution climate model simulation in Japan, Bonn Climate Change Conference, 2019,https://unfccc.int/sites/default/files/resource/2.5Tomohito_Yamada_presentation_ver1.5plus.pdf6) Tomohito Yamada, United Nations Framework Convention on Climate Change（UNFCCC）The
50th sessions of the UNFCCC Subsidiary Bodies（SB 50）Participation Report」,2019,https://www.hkd.mlit.go.jp/ky/kn/kawa_kei/splaat000001ofky-att/splaat000001pcj9.pdf

2.3.2

Netherlands

Within the National Delta program the Delta decision on flood risk management lead to an updateof the Dutch Safety standards and a more risk based approach on dealing with flood risks. But
challenges remain and circumstances change:•

The sea level is rising (possibly at an increasingly faster rate), while the soil is subsiding;•

Torrential rains are increasing in frequency and intensity;•

The temperature is rising;•

The climate is becoming drier.

Due to all these changes and the economic growth a flood would have a greater impact today than
it would have had in 1953 when the last large flood occurred in the Netherlands. Key knowledge
issues pertain to the rising sea level and extreme rainfall events.
For the rising sea level a Sea Level Rise Knowledge Programme is established and conducts
research into the pace at which the sea level will be rising from the second half of this century
onwards. The program present its final results in 2026.
In the summer of 2021 an extreme rainfall event in the Netherland, Belgium and Germany lead to
extreme discharges on the regional and primary hydraulic systems. In the Netherlands the primarysystem was exposed to extreme water levels but levee breaches did not occur. This was not thecase for the regional system. The regional system was overloaded due to the extreme rainfall andthe rapid discharge to the regional rivers causing water to overflow the regional levees leading tolarge amounts of damage in the southern part of the Netherlands, luckily there were no casualties.
This event lead to new research questions on how the Netherlands will have to deal with these
extreme rainfall events and what measures are possible to deal with these events.

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3

Methodology of the flood risk
analyses

3.1

General approach

Risk 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 calculated.
The consequences (economic damage and loss of life) of failures of the flood defences are also
determined. The failure probability and consequences of a levee breach are not the same at eachpotential failure location within a levee system. Therefore different breach locations are assessed
and the flood probability per breach location is combined with the consequences of those breachlocations. This approach gives the flood risks for the considered area.
Uncertainty plays an important role in calculating the failure probability of flood defences and in
determining the consequences of flooding. For example, It is not known what the maximum load
will be in a given year or what the exact flooded area will be given a levee breach.
It is, however, generally possible to assign probabilities to the possible loads and strengths, on thebasis of statistics and expert judgment. And for the consequences it is possible to generate
different scenarios to determine bandwidths of possible flood consequences.
The failure probability of a flood defence is the overall probability of all combinations of loads andstrengths at which the flood defence will fail. The probabilistic approach allows us to explicitly
address the uncertainties surrounding the actual values for loads and strength properties when
considering the level of safety afforded by the flood defences.
Combining the calculated flood probability and the potential flood consequences lead thedetermination of the flood risk.
Figure 9 Brief overview of risk approach (VNK2, The method in brief)

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3.1.1

Flood scenarios

An area can be flooded from different locations and a breach can occur everywhere. However, it is
not necessary to identify the consequences of flooding for every possible breach location for a
sufficiently accurate risk analysis. A system can be divided into segments where the flood pattern
will be approximately the same, regardless of the precise location of the breach within thatsegment. So based on the characteristics of an area and the hydraulic system different breachlocations are defined for which the failure probability and flood consequences are determined.
Additionally to the selection of the breach locations, choices have to made regarding the shape ofthe discharge wave or the shape of the storm surge set-up. Different shapes will lead to differences
in inflow through a breach at a certain location. So it is important to consider different type ofshapes of the hydraulic loads to get insight in the possible flood extent and flood characteristics.

3.1.2

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 segments with a breach location•

Producing flood propagation models•

Defining flood scenarios•

Consequence estimates for each scenario•

Economic damage•

Evacuation•

Loss of Life

These consequences give an insight in the potential damage that can occur due to a levee breach.

3.1.3

Flood probability

The failure probability of a flood defence is the overall probability of all combinations of loads andstrengths at which the flood defence will fail. The hydraulic loads are derived from extreme rainfall
to discharges in rivers or extreme wind conditions can lead to high water levels and high waves
along the coast. These conditions for different systems are translated into probabilities of
exceedance of certain hydraulic loads.
The probability of exceedance of hydraulic loads are combined with the strength of the levee todetermine the flood probability of a considered segment of the levee system.
For some systems, mainly river systems, breaching of a levee can have an impact on the hydraulicloads for the more downstream situated levees. So there is some dependency between the
upstream and downstream breach locations. Given that a more upstream breach location has failed
the hydraulic loads will decrease and the probability of failure of the more downstream situatedbreach location is getting smaller. This effect has to incorporated in the flood probability calculationin order to prevent an overestimation of the failure probability of the downstream levees.

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3.1.4

Flood Risk

The flood risk is calculated based on the flood probabilities and the flood consequences of eachscenario. Every scenario contributes to the flood risk and the sum of the contributions gives thetotal flood risk.
The flood risk can be expressed and represented in various ways and all measures of risk can becalculated on the basis of the flood probabilities and their flood consequences. The measures of riskare:•Annual expected value of economic damage•

Spatial distribution of the annual expected value of economic damage•

Annual expected value of number of fatalities•

Local individual risk•

Societal risk

3.2

Case study area

- The target area of this study is Obihiro City, around mid-stream of Tokachi River basin in Tokachi
Plain, south-east of Hokkaido.
- The Tokachi River is a first class river with a channel length of 156 km and a basin area of 9,010
km2. It originates from the 2,077-meter-high Tokachi-dake mountain in the Daisetsuzan mountain
range and flows through Obihiro City with numerous tributaries into the Pacific Ocean. The river issurrounded by 2,000-meter-high mountains on the north and west sides of the basin, anddependent on weather conditions, such as typhoons, it can cause large amounts of precipitation
due to orographic rainfall and rapid flush into the river.
Figure 10 Location of Hokkaido and Obihiro City

- The risk assessment of this study is targeted to the urban area of Obihiro city, where the corefunctionalities are concentrated. Obihiro City is a hub of the Tokachi region, known as one of thelargest food supply bases in Japan. Its population is approximately 170,000 people, and 100,000 ofthem live in the city center with many important regional facilities such as the city hall, hospitals,
and centers for commerce, industry, energy and distribution.

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Obihiro urban area is located at the confluence of Tokachi River and Satsunai River, the second
largest tributary of the Tokachi River system, and is, therefore, a highly probable to be flooded.
After experiencing major floods in 1922, 1962, and 1980, the area has been spared frominundation damage, thanks to the progress in river maintenance, though there is a fear forincreasing food risk due to revealing climate change impact.
Figure 11 The target area in Tokachi River Basin- Hokkaido is one of the most advanced regions in Japan for climate change adaptation in the fieldof flood control, and the Tokachi River has more than 10,000 cases of detailed meteorologicalsimulations and flood simulations of physically possible heavy rainfall events in the past and future,
based on the Japanese climate projection database d4PDF (Mizuta et al, 2017). This study
attempted to develop a new risk assessment method by combining a large number of Japanesephysical simulations and Dutch probabilistic flood risk assessment.

3.3

Part 1: extreme rainfall and river discharges

3.3.1

Flood damage estimation based on observation and climate projection
dataset

In August 2016, three typhoons landed in Hokkaido region, Japan within a week. After landing ofthese typhoons, typhoon No. 10 approached near this region. These typhoons caused recordbreaking rainfall in many parts of Hokkaido, resulting in river flooding, landslides, road and bridge

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washouts, and extensive damage to agricultural land, the main industry in the region (Reference;
Ministry of Land, Infrastructure and Transport, Nguyen and Yamada 2017). The research group ledby the leader of Japanese side in this joint research project has conducted large-scale climate
simulations using a regional climate model that covers the period from the middle of the 20th
century to the end of this century using a domestic supercomputer (earth simulator) and developed
climate change projection database which have quite large ensemble members with high spatial
resolution (Yamada 2020, Hoshino et al., 2020). The validity of the scale and occurrence
probability of large-scale heavy rainfall revealed by the predicted information was demonstratedusing mathematical and statistical theory (Shimizu et al., 2020). The advantage of using this datais that the probability of occurrence and intensity of possible extreme rainfall event can be
estimated. In addition, the data enables to estimate the future changes of these phenomena. We
have analyzed the spatio-temporal characteristics of heavy rainfall and associated flood inundation
characteristics based on the results of climate change projections over several thousand years andquantified the risk of human and economic damage associated with potential large-scale floods inthe current climate and in the future climate, when global warming progresses (Yamada 2020).
This research has contributed to a shift from the traditional hazard-based flood control planning
based on past rainfall observations to a new risk-based planning considering extremely large floodsin the future climate.
In the field of flood control, hydrological models (rainfall-runoff and inundation models etc.) that
describe the processes from rainfall-runoff to inundation play a fundamental role in setting thedesign conditions. On the other hand, the parameters of the model are subject to uncertainty due
to the finite number of observed flood event in the past. In “working package 1” of this joint
research, the relation between rainfall and peak discharge expressed by each model is considered
to develop a method for estimation of peak discharges in the Tokachi river basin. The advantage ofthe quantification of rainfall-peak discharge relation enables estimation on uncertainty ofcharacteristics of different hydrological models. Introducing rainfall-peak discharge relation
expressed by models, future change of spatial-temporal characteristics of extreme rainfalls withlow frequency and large-magnitude can be considered, and peak discharge is calculated underthese extreme rainfalls by dynamical simulation and statistical methods. Here, the methodology
constructed in working package 1 is purposed to estimate probability distribution of peak discharge
with return period of design level by using the input from extreme rainfall distribution with thesame level and rainfall-peak discharge relation derived from a hydrological model. Advantages ofthis methodology is that estimated peak discharge as output is subject to update in accordancewith the differences and sophistication of climate models and rainfall-runoff models. This technicalreport describes the methodology and results developed in working package 1.

3.3.2

Estimation method for peak-discharge distribution for each return period

The estimation method for peak discharge frequency for each return period consists of three steps
as shown in Figure 12. The first step is the estimation of rainfall volume frequency for each return
period using ensemble climate datasets with resampling technique. By using this process,frequency distribution of rainfall with arbitrary return period, here, a range of arbitrary probability
distribution is defined as a confidence interval.
The second step is the estimation of the relationship between rainfall volume and peak discharge
by using rainfall-runoff model result. The rainfall-runoff simulations for several rainfall patterns
which are included in the ensemble climate dataset were conducted to calculate peak discharges attarget points. Next, quantile regression was applied to the relationship between rainfall volume andPR3983.10 • January 2022

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peak discharge in order to obtain relative frequency of peak discharge as a function of rainfall
volume.
The third step is the estimation of peak discharge frequency for each return period. By using thefrequency distribution of rainfall with target return period T derived from ensemble climate data,
the occurrence frequency of peak discharge distribution, caused by a given rainfall amount,corresponding to conditional distribution, can be quantified. Then, taking a weighted average of theconditional distribution of peak discharge with T-year rainfall distribution derives probability
distribution of T-year peak discharge. Detail of the method is described below.
Figure 12 Estimation method for peak-discharge frequency for each return period

3.3.2.1

Return period of rainfall

Design rainfall in the current flood control management of Japan is generally calculated byfollowing steps. Firstly, observed annual maximum rainfall was fitted to several probability
distributions which are candidate for estimation on design rainfall. Then, a probability distribution
which has highest level of stability and goodness of fit is selected, and design rainfall as a quantile

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value corresponding to target annual exceedance probability “p” in this selected distribution is
obtained. A probable rainfall corresponding to exceedance probability “p” means that occurrence
probability of rainfall which exceeds this probable rainfall in a year becomes “p”. Also, return period
is defined as reverse inverse number of exceedance probability in an adopted probability
distribution, and becomes an important index in flood control management. Flood control
management of main river basins in Japan adopted a return period ranging from 100 year to 200
year as design level.
However, the observation period of hydrological quantity in major river basins of developed
countries ranges from a few decades to more than a hundred years. It shows the observation
period is shorter than the design return period of flood prevention facilities. In other words, whenusing the annual maximum data, the number of observed extremes ranges from a few dozen to ahundred at most, so that the estimation of design conditions in the conventional hydrologicalfrequency analysis contains large uncertainty (estimation error). Since ensemble climate data
provides a lot of samples of rainfall based on dynamical models which verifies physical feasibility ofcalculated meteorological phenomenon, the uncertainty of the probable rainfall can be quantified asa frequency distribution. To quantify the uncertainty, resampling to ensemble data was conductedin this study.
Resampling of annual maximum heavy rainfall obtained from a large ensemble climate dataset was
conducted to clarify the frequency distribution of the probable rainfall. By applying resampling,
100,000 samples were generated in the climate experiment. For derivation of frequency
distribution of T-year probable rainfall, Gumbel distribution was fitted to each of the 100,000
resampled samples from the past and the 4 degree Celsius warmer climate condition. In otherwords, we estimated 100,000 Gumbel distributions fitted to the maximum annual 72-hour rainfall
for 60 years, which gives 100,000 sets of T-year probable rainfall for a given return period andallows us to estimate its frequency distribution. Because of using a climate model, introduced
resampling method can be interpreted as a physical Monte Carlo experiment. Here, the cumulative
distribution function of the Gumbel distribution is shown in equation (1).

  x −   FX ( x ) = exp  − exp −      (1)

Where, FX(x) is cumulative density distribution of Gumbel distribution, x is annual maximum
rainfall, μ is location parameter, σ is scale parameter．
3.3.2.2

Rainfall-Peak discharge relation

The frequency distributions of peak discharge as a function of rainfall volume were estimated byusing quantile regression. The peak discharges have a range even if the same rainfall volume due
to differences in spatio-temporal rainfall patterns. The relation between rainfall volume and peakdischarge is represented as a function. The peak discharge range is quantified as percentile values
using quantile regression and each quantile value from 1st to 99th percentile were extracted foreach rainfall volume. The obtained 99 discharges were used to make frequency distribution of peakdischarge. By applying quantile regression, the conditional probability distribution of peakdischarge which is caused by an arbitrary amount of rainfall can be calculated.
3.3.2.3

Return period of peak discharge

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The frequency distributions of peak discharge for each return period were estimated. The frequency
distribution of rainfall volume for each return period obtained by step 1 and the frequency
distribution of peak discharge for each rainfall volume obtained by step 2 are used to calculate
frequency distribution of peak discharge for each return period. Each bin (section in histogram) ofrainfall volume has a frequency distribution of peak discharge. The frequency distribution of peakdischarge is expressed as equation (2).

f Qp ( q p , T ) =  f Q p ( q p | r , T ) f R ( r , T ) dr(2)r

where T is target return period, Qp is peak discharge, R is rainfall volume, fQp(qp|r) is theconditional probability density function of the peak discharge that can occur under a given rainfall
volume r, fR(r) is the probability density function of rainfall volume, and fQp(qp) is the probability
density distribution of the peak discharge.
In this study, a Gumbel distribution is fitted to a set of probable rainfall values derived from theresampling method described above to construct a continuous distribution of a probable rainfallfR(r,T) for any given return period T. The Gumbel distribution is also applied to a set of calculated
peak discharge values obtained for a given total rainfall value r, which can be derived from a
relation between total rainfall in arbitrary period and calculated peak discharge, to construct theconditional distribution of peak discharge, fQp(qp|r). Equation (2) is the probability distribution of
the peak discharge fQp(qp,T) under the total rainfall for a given return period T as fR(r,T), which canbe derived by a following process. fQp(qp,T) is weighted average of the conditional distribution ofpeak discharge under given rainfall value fQp(qp|r), which distribution of rainfall fR(r,T) is derived
from d4PDF-5km.
The above method for the probability peak discharge integrates the conditional distribution
expressed by the relationship between the total rainfall and the peak discharge obtained from therainfall-runoff model, and the distribution of the probable rainfall itself from the ensemble climate
data. In other words, the method is capable of updating the probability distribution of the output
peak discharge as the climate model and rainfall-runoff model differ or become more sophisticated.

3.3.3

Large ensemble climate data

In recent years, a large-ensemble climate simulation database (d4PDF) (Mizuta et al, 2017) has
been created and utilized by the research project of the Ministry of Education, Culture, Sports,
Science, Japan (SOUSEI, TOUGOU, SI-CAT, DIAS) and JAMSTEC Earth Simulator Special Promotion
Projects. Using d4PDF and its extensive data on past, current and future climate, it is possible forthe first time to assess the frequency of rare weather phenomena which lead to disasters. The
original d4PDF is consisted of simulations from the atmospheric GCM (AGCM) called theMeteorological Research Institute AGCM, version 3.2 (MRI-AGCM3.2) (Mizuta et al, 2012) with ahorizontal resolution of approximately 60 km (d4PDF-60km), and dynamical downscaling (DDS)
from d4PDF-60km to a horizontal resolution of 20 km using the Regional climate model (RCM)
targeted over Japan (regional experiment of d4PDF) (hereafter, d4PDF-DS20) (Figure 13). The
experimental settings of d4PDF consist of a past climatic condition (hereafter past experiment; 50
ensembles×60 years (1951–2010)) and a 4 ℃ warmer climatic condition (hereafter +4K warmer
experiment; 6 sea surface temperature patterns×15 ensembles×60 years), which has a 4 ℃
warmer global mean air temperature than the preindustrial period. The sea surface temperature

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(SST) used in the past experiment was obtained from the Centennial Observation-Based Estimatesof SST, version 2 (COBE-SST2) (Hirahara et al., 2014). Small perturbations based on SST analysis
error were added to initial conditions of the SST to prepare ensemble members. The SST used in
the +4K experiment consists of the six patterns based on RCP8.5 experiments conducted under thephase 5 of the CMIP5 (Taylor et al., 2012). The details of experimental settings of the d4PDF arepresented in Mizuta et al. (2017). Moreover, dynamical downscaling (DDS) was applied to d4PDFDS20 for conversion of annual maximum rainfall events from d4PDF-DS20 to a horizontal
resolution of 5 km. The target rainfall event was defined as an event in d4PDF-DS20 for each year,
between June 1 and December 1, where rainfall amounts reached the maximum value in 72 h over
the Tokachi river Obihiro reference point basin. This study defined the rainfall events as annual
maximum rainfall events. For DDS, we employed the NHRCM (Sasaki, 2008), which was used tomake the d4PDF-DS20. We set the target area of the DDS to 800 800 km around Hokkaido(Figure 13). Furthermore, we set the number of calculation grids to 161 161 in the horizontal
direction and 50 in the vertical direction. The Kain-Fritsch convective parameterization scheme(Kain et al., 1993) was used for the DDS. The other physical schemes, such as microphysics, land
surface, and boundary layer schemes, were the same as those used in Kawase et al. (2018). The
grid-mean topography was used in this study, while Kawase et al. (2018) used the envelope-type
mountains. This study utilized values from the d4PDF-DS20 to set the initial and boundary
conditions for the calculation. The target period for the DDS was set to 15 days, including theannual maximum rainfall occurrence period in each year. The DDS was performed for a total of3000 events for the past experiment and 5400 events for the +4K experiment. Yamada et al.
(2018) verified the validity of d4PDF-5km in terms of heavy rainfall characteristics.
Figure 13 Downscaling process in d4PDF

3.3.4

Hydrological modelThis section describes the rainfall-runoff models selected by the two countries. The characteristicsof rainfall and peak discharge represented by these physical models allow us to evaluate
uncertainty of model calculation.
3.3.4.1

Wflow_sbm

Wflow_sbm is a physically based bucket-style hydrologic model based on simplified physicalrelationships and it uses kinematic wave surface and subsurface routing for lateral transport. The
wflow_sbm model is a fully distributed hydrological model, allowing the use of high-resolution
spatial input data. This makes the model well suited for the use of high-resolution climate model
output. The wflow model is Open Source and freely available.

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3.3.4.2

MATSIRO

The Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) (Takata et al.,2003) is also used here. MATSIRO can simulate the exchange of water vapor, energy, andmomentum between the land surface and atmosphere on a physical basis. The interaction between
atmosphere and land surface can be simulated by this model with a general circulation model(GCM). MATSIRO has been used for various impact assessments of the hydrological cycle, such as
the impact of human activities (Pokhrel et al., 2017) and groundwater impacts (Koirala et al.,2014). Recently, a system for estimating condition of land surface and river in real time has beendeveloped and operated (Today's Earth). This system consists of MATSIRO and river routing model
CaMa-Flood. Meteorological forecast and observation data are used as input data of the system toestimate the risk of water-related disasters on a global scale. MATSIRO solves the surface and subsurface runoff by considering four runoff types; the base flow, the saturation excess runoff (Dunnerunoff), the infiltration excess runoff (Horton runoff), and the over flow of the uppermost soil layer.
The first three runoff types are calculated by applying a simplified TOPMODEL (Beven and Kirkby,1979), which assumes the subgrid-scale slope profile.
3.3.4.3

RRI model

The RRI model was employed to simulate flood inundation and identify flood-prone areas. The RRI
model is a two-dimensional model that is capable of simulating rainfall–runoff and flood inundation
simultaneously (Sayama et al., 2012, Sayama et al., 2015). At a grid cell in which a river channel
is located, the model assumes that both slope and river are positioned within the same grid cell.
The channel is discretized as a single line along its centerline of the overlying slope grid cell. The
flow on the slope grid cells was calculated with the 2D diffusive wave model, whereas the channel
flow was calculated with the 1D diffusive wave model. The RRI model simulates the lateralsubsurface, vertical infiltration, and surface flows to better represent flood characteristics.

3.4

Part 2: Failure probability of levees

In flood control planning in Japan, calculations are based on the condition that a breach will occur if
the water level exceeds the planned water level. The probability is evaluated as 0 or 1, whether thewater level will be exceeded or not. However, the actual phenomenon of breaching is that thewater level may reach the planned water level but not breach, or the water level may not reach theplanned water level but breach occurs. This is due to various factors such as the mechanism oflevee failure, strength of the levee, and temporal changes in hydraulic conditions. Probabilistic
assessment of levee failure is essential for the correct assessment of flood risk.
In the Netherlands, flood risk has been assessed based on the probability of levee failure using afragility curve, which has been introduced into policy. In this study, the Dutch method of
calculating the probability of breaching is applied to the Tokachi River basin, and a method ofcreating a fragility curve is proposed, with some modifications based on the characteristics offloods in Japan.
There are several factors that can cause levee breaches. In Japan, the factors are generally divided
into overflow, erosion/ scour, and seepage. In 2019, typhoon No. 19 caused floods in Japan andbreaches of levees occurred in many places, including 14 on rivers managed by the national
government and 128 on rivers managed by prefectural governments. According to the results of asurvey on the causes of breaches, overflowing water was the main cause of 86% of the breaches.

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In this study, the probability of levee breakage was calculated for overflowing water, which is oneof the most frequently reported causes of levee breakage in Japan.

3.4.1

Overview of Approach

The method applied in this study is based on VNK22 and the Dutch method for dike safetyassessment, called the BOI3. We did however improve some parts of the method:•

Only the failure mechanism overtopping was considered, as it is considered the most importantfor Obihiro. We applied a cumulative damage approach, to take into account the duration of thehydrographs:•

Not only the probabilities of the peak discharges are considered, but also the shape of thehydrograph. A high water level with a long duration gives a higher failure probability, as it cancause more damage to the revetment.

•

When calculating the total failure probability for the area, we use a tailored approach to takedependencies between different dike sections into account.
1. Failure probabilities are combined conditional to the discharge, as different sections would
fail during the same high discharge conditions.
2. For combining segments into section probabilities, the maximum segment failure probability
per discharge is used, hence, the segments are considered dependent.
3. When combining sections to river probabilities, the reducing effect of potential failure of
upstream sections is taken into account.

Figure 14 Flow of calculation of breach probability considering uncertainty.

WP2

Hydrograph

Seleted by WP3Water level

H-Q relation

Overflow

Levee height

Velocity

at slope

Damge index
failureUncertainty

Generation of random variables
following a normal distribution

Condition of grass

Repeat N times
Number of failure judgments (n) / N

levee failure probability

2 Rijkswaterstaat VNK Project Office, The National Flood Risk Analysis for the Netherlands -FINAL REPORT, 2016,

https://www.helpdeskwater.nl/publish/pages/131663/vnk-rapport-eng-lr.pdf .
3 https://www.helpdeskwater.nl/onderwerpen/waterveiligheid/primaire/beoordelen/nieuwsbrieven/nieuwsbrieflandelijke-beoordeling/nieuwsbrief-boi-oktober-2019/programma-boi-2023/

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3.4.2

Fragility curve

In this study, we focused on the mechanism of levee failure caused by overflowing water andadopted a method to determine the occurrence of levee failure based on the damage caused byerosion of the levee slope by overflowing floodwaters.
Figure 15 Example of calculating damage due to overflow with a cumulative overtopping approach.

3.4.3

Uncertainty

The following image shows the different uncertainties we want to take into account when
considering the failure mechanism overflow: the damage (or critical overtopping velocity, as
described in the last section), the water level and the crest height.
Figure 16 Sketch of overflow with the uncertain variables indicated.

1. Water levels
Uncertainty in river levels was evaluated based on the relationship between water levels calculated
from HQ-relations and water levels observed in the field in the target river. Observations werecollected at Obihiro, Tokachi River (KP56.73) and Nantaibashi, Satsunai River (KP15.00). The data
are available for five years from 2014 to 2018. Observations were carried out once a week orduring runoff. The difference between the observed water level and the water level calculated from
H-Q-relation to the observed flow was calculated. The mean and variance of this difference wereused to evaluate the uncertainty.

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Figure 17 Calculation of water level dispersion at Obihiro point (KP56.73) and Nantaibashi point (KP15.00)
WaterLevel - Discharge relation

SatsunaiRiv. Nantai Bridge KP15.00

83

39

82

H.W.L = 38.33 m

38

81

37

80

36
35
Observed data
Q=a(H+b)^2
ND_Average
ND_95% confidence interval

34
33
32
31

Water level [m]Water level [m]WaterLevel - Discharge relation

Tokachi Riv. Obihiro KP56.73

40

H.W.L = 79.22 m

79
78
77

Observed data
Q=a(H+b)^2
ND_Average
ND_95% confidence interval

76
75
74
73

0

500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000

0

500

1000

Discharge [m3/s]

1500

2000

2500

3000

Discharge [m3/s]

2. Bank Height
The planned high water level set in the river plans for the Tokachi River and the Satsunai River isdefined as the "design bank height. The height of the top of the embankment is organizedlongitudinally based on the ground level data (LP data) obtained from aerial laser surveying, andthe mean value and standard deviation are calculated every 0.2 km in the longitudinal direction of
the embankment to be used as the uncertainty of the embankment height. The levee height and LP
data in the plan were provided by the Hokkaido Development Bureau.
Figure 18 Design bank height and average bank height with uncertainty

3. Critical flow velocities
In the approach described above, provided by Dean et al. (2010), the standard deviations of thecritical flow velocities are given. These can be used to quantify uncertainty on the strength side:•

For “plain grass - good cover”: u_cu_c = 1.80, σ_(u_c ) = 0.38•

For “plain grass - average cover”: u_c = 1.30, σ_(u_c ) = 0.12•

For “plain grass - poor cover”: u_c = 0.76, σ_(u_c ) = 0.04

In our approach, the condition of plain grass is "good cover" because the dykes in the TokachiRiver are properly managed.
The different uncertainties (random variables) can be combines into a single fragility curve. We
used Monte Carlo simulations to calculate a fragility curve that takes into account uncertainty.

3.4.4

failure probability

The failure probabilities of levees in the Tokachi River and the Satsunai River were calculated. The
failure probabilities were calculated at intervals of about 0.2 km. As a result, the failure probabilityof the levees of the Satsunai River ranged from about 10-5 to 10-3. The failure probabilities of thelevees of the Tokachi River were in the range of 10-5 to 10-3, as in the case of the Satsunai River,
although they varied greatly by location.

PR3983.10 • January 2022

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Figure 19 Failure probability per location (KP).

1. Combine segments to sections
Until now, the failure probabilities where calculated for a levee segment. In this section we
combine the failure probabilities from segments, to sections, to the whole of Obihiro. When doing
so, we want to avoid counting the consequences of the same flood scenario multiple times. Thismeans that a failure of one segment, reduces the failure probability of a next section, unless theconditions for which they fail are independent. Combing the failure probabilities thus means wemake a realistic choice between combining probabilities dependent and independent.
•

First of all, we combine the probabilities conditional to the discharge. This means weassume that a single high discharge causes a high water level along all flood defenses. Thismight be a little simplified for the two rivers (Satsunai and Tokachi), but is probably a very
realistic simplification, as the high water levels are caused by the same rain event.

•

Secondly, segments are grouped into sections based on similar consequences. Because
these sections are close to each other, we assume the strength to be dependent. Thismeans that the dike will always fail at the segment with the highest failure probability (forthat discharge).

•

Thirdly, sections are combined per river branch independently (still conditional todischarge), but we take the order of the sections into account. Sections are far enoughapart for the strength of the dike to be considered independent. However, when an
upstream sections has failed, we assume the water level to be reduced such that thedownstream dikes cannot fail anymore during this event.

2. Combining segment probabilities to sections
The result of combining segment probabilities to sections is shown in the figure below. Per
discharge the maximum failure probability is selected, resulting in the black curve. For mostsections a single segment is dominant, only for section KP59.6 (lower left in Figure X) two sections
result in a combined fragility curve.
Figure 20 Fragility curves for all segments (coloured), and the combined section curve (black).

PR3983.10 • January 202230

3. Combining section probabilities per river branch
The result of combining section probabilities per river branch, is shown in the two figures below.
The black curves are the original section curves, independent of upstream sections. The red dashedcurves are after taking potential failure of upstream sections into account. The highest discharges
will certainly lead to upstream failure, reducing the conditional failure probabilities downstream forthese discharges. Therefore, downstream sections will only fail at relatively low discharges,
especially if the section has a higher failure probability.
Figure 21 Fragility curves for all sections of Tokachi river, before and after taking the effect of upstream
sections into account.

3.4.5

Flood probabilities

The flood probability was calculated using the failure probability calculated and the probability ofwater level exceedance calculated in WP1. The flood probability was calculated for each section set
in WP3.
Table 1 Past, average/ Failure probabilities for the sections used in WP3. The scenario probabilities are thefailure probabilities after correcting for upstream failure.
Location

Section failure probability

Scenario probability

Satsunai KP7.0

2.53e-05

2.53e-05

Satsunai KP6.4

5.67e-05

3.79e-05

Satsunai KP5.2

4.62e-06

1.45e-07

Satsunai KP4.2

3.00e-07

6.52e-10

Tokachi KP62.4

1.41e-04

1.41e-04

Tokachi KP61.4

3.36e-04

9.93e-07

Tokachi KP59.6

1.21e-03

5.36e-04

Tokachi KP58.0

6.62e-04

4.49e-05

Tokachi KP56.4

2.04e-02

1.06e-02

Table 2 Future – average/ Failure probabilities for the sections used in WP3.
Location

Section failure probability

Scenario probability

Satsunai KP7.0

8.87e-04

8.87e-04

Satsunai KP6.4

1.42e-03

6.52e-04

Satsunai KP5.2

3.26e-04

2.75e-06

Satsunai KP4.2

6.11e-05

3.37e-08

Tokachi KP62.4

5.46e-03

5.46e-03

Tokachi KP61.4

9.52e-03

5.72e-06

Tokachi KP59.6

1.55e-02

2.07e-03

Tokachi KP58.0

1.37e-02

2.56e-04

Tokachi KP56.4

7.53e-02

1.60e-02

PR3983.10 • January 2022

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3.5

Part 3: Damage, evacuation, loss of life and flood risk

In part 3 the flood consequences for the Obihiro case study are shown. For all aspect, economic
damage, evacuation, loss of life and flood risk an overview of the approach and results are given.
The more extensive analysis and detailed description are included in report, “Flood Risk andClimate Change Hokkaido WP3, 2021”.

3.5.1

Economic damage

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.
For the Obihiro Case study the damages are calculated for the current situation and the situation
including climate change. In the situation with climate change more extreme discharges can occur
leading to an increase in flood consequences. Next to the impact of climate change also the shapeof the discharge wave has an impact on the flooding and the calculated damage. In the figure
below the spatial distribution of the economic damage for a breach at Satsunai River location KP4.2
is shown. The top figures describe the damage for the current “past” situation and the lower figures
for the situation including climate change “future”. The three damage calculations per scenario
show the impact of the difference in discharge wave. “Max discharge” means a discharge wave witha very high peak, the “Min discharge” means that the peak of the discharge wave is slightly above
the levee height and the “Max Volume” is a discharge wave that is relatively wide.
Figure 22 Damage (Satsunai Riv. KP4.2)
Max Discharge

Min Discharge

Max Volume

Past

FutureThe damage calculation has been done for all breach locations used in the flood risk analysis. In
Table 3 the calculated damage per breach location is given. The table gives insight in the extent of
the flood damage and the difference between the different locations and the impact of the different
shape.

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Table 3 Economic damage for “past” and “future” scenarios
Location

Past

FutureMax

Min

MaxVolSatsunai_KP4_2

83200

83200

83200

Satsunai_KP5_2

170400170400170400Satsunai_KP6_4

362700201800362700Satsunai_KP7_0

308000224800308000Tokachi_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

480600226400430700Tokachi_KP56_4

834000129000479400Tokachi_KP58_0

855900294200598300Tokachi_KP59_6

865000483000697000Tokachi_KP61_4

917000583800778000Tokachi_KP62_4

798400532300837400

These economic damages are the potential damages that can occur when a levee breaches. He
impact of climate change on the calculated damage is for the max scenarios a factor 1.5 - 3. For
some breach locations the shape of the hydrograph has a similar effect on the calculated damage
and for some location the impact of the shape of the discharge wave is even larger.

3.5.2

Evacuation

Evacuation is an important factor when determining loss of life, because it gives insight in thelocation of people prior and during a flood event. For the case study the definition of the evacuation
rate is the ratio of the people that reach a safe location. The destinations are not only shelters, butalso other safe places, such as higher ground and relatives / friends' home.
An important factor is the lead time. The lead time describes the time available to reach a safe
location. For the case study the lead time is determined by a standard throughout all available realcases (86), taking into account distance from home to shelters and walking speed, though it shouldhave been better if it were determined per area/river, dependent on water rising rate of eachriver/flood. Definition of lead time to evacuate safely is time after an evacuation information isannounced until a disaster occurs, such as dike failure, overflowing and flood, or until the highest
water level is reached. Lead time required for safe evacuation is estimated by distance between
home and shelters and walking speed while evacuating.

PR3983.10 • January 2022

33

Figure 23 Concept of lead time
Water
Level
overflowing etc.
Lead Time to Evacuate
Evacuation Order or Advisory
Evacuation Preparation Information
Lead TimeTime

Figure 23 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 flooded
area.
Figure 24 Simple event tree based on lead time

3.5.3

Loss of life

There are different models to calculate the loss of life for a flooded area. For instance there is the
LIFESim model developed by the United States Army Corps of Engineers (USACE). This model
determines mortality dependent on water depth, taking into account age of victims (65 years andolder/younger than 65 years). Fatality is estimated by multiplying mortality classified into 3 areas,
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.
Another model is the Dutch model. This model sues mortality functions related to flood
characteristics. The flooded area is divided into three hazard zones (breach zone, zone with rapidly
rising water, remaining zone) and employed mortality function with parameter of water depth ineach zone to estimate mortality in the event of flood. For the Obihiro case study the Dutch loss oflife model is used.

PR3983.10 • January 2022

34

For the different breach locations, the flood extent and flood characteristics are used to determine
the mortality at the flooded area. This is used to determine the mortality at all locations. For
breach location KP61.4 the mortality rate is shown in Figure 25, the mortality varies between lessthan 0,1% to more than 10% in the deeper parts. This can be explained by the fact that, ingeneral, the greater water depths lead to a higher mortality.
Figure 25 Mortality MAX scenario KP61.4

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 (Exp), this is also included in the table.
Table 4 Overview Loss of Life for all scenarios
Location

Past

FutureMax

Min

MaxVol

Exp

0%

26%

80%

Exp

0%

26%

80%

Exp

0%

26%

80%

Satsunai_KP4_2

4076

17

13

3

4076

17

13

3

4076

17

13

3

Satsunai_KP5_2

7830

23

17

5

7830

23

17

5

7830

23

17

5

Satsunai_KP6_4

15594

56

42

11

9358

9

7

2

15594

56

42

11

Satsunai_KP7_0

13930

28

21

6

10445

2

2

0

13930

28

21

6

Tokachi_KP56_4

13411

186

139

37

5985

27

20

5

12427

118

88

24

Tokachi_KP58_0

21555

162

121

32

14200

47

35

9

20769

127

94

25

Tokachi_KP59_6

26884

340

253

68

23204

134

100

27

25533

225

168

45

Tokachi_KP61_4

30421

337

251

67

28893

218

162

44

30283

289

215

58

Tokachi_KP62_4

28880

179

133

36

26558

76

57

15

28880

179

133

36

Satsunai_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

PR3983.10 • January 2022

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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 is larger than the Satsunai river and therefore
leads to a larger flood extent and more people exposed.

3.5.4

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 the risk analysis
the difference in risks between the current situation “past” and the situation including climatechange “future” are shown and also the impact of the shape of the discharge waves on the results.
3.5.4.1

Economic Risk

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.
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 Yen/year. This economic risk isdominated by breach location KP56.4 and is caused by a relatively high probability of failure.
Table 5 Economic Risk for each breach location and the total economic risk for the “past” situation
Location

Flood probability (Pf)

Damage (Yen)

Economic Risk (Yen/yr)

Satsunai_KP4_2

1/1533756680

83200

0.00

Satsunai_KP5_2

1/6906020

1704000.02

Satsunai_KP6_4

1/2638036270013.75

Satsunai_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

Total

3,729,38

When climate change is taken into account, the failure probability and the amount of damage
increases and therefore the economic risk increases as well. The expect value of the economic
damage increases to 20,464 Yen/year. This is an increase by a factor 5.5, 50% of the difference iscaused by the increase in failure probability and 50% by the increase in economic damage.
3.5.4.2

Risk Loss of LifeThe expected value of loss of life gives insight in the average number of fatalities per year due toflooding. To determine the impact of climate change the results are shown for the current situation“past” and the situation including climate change “future”. The expect value for the current (“past”)
situation 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. In all analysis regarding
loss of life the average evacuation percentage of 26 percent is used.

PR3983.10 • January 2022

36

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. 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 caused by the increasein failure probability and 50% by the increase in loss of life.
The local individual risk gives insight in the annual probability that an individual will die in aparticular location, including the effect of evacuation probability. The local individual risk is acombination 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 current “past” situation. The riskier places
are situated in the deeper parts of the case study area and are consistent with economic risks.
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. The flood extent is larger than the “past” situation and also the risks arehigher, this is shown in the right figure.
Figure 26 Local individual risk for the “past” situation (left) and “future” (right)

The local individual risk is in the basic analysis calculated with the average evacuation percentage,
26%. When this percentage increases the local individual risk will decrease. In Figure 27 the local
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.

PR3983.10 • January 2022

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Figure 27 Local individual risk for the “future” situation, 80 percent evacuation

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4Main findings

4.1

Application of the method

In the research we succeeded in compiling discharge statistics based on extreme rainfall,
subsequently determining failure probabilities using fragility curves and mapping the consequencesof a flood in terms of damage, casualties and risk. The method can be applied to other river anddelta systems. In chapter 5 we also discuss some recommendations.
The main challenge in compiling the discharge statistics is dealing with uncertainties in the water
system and rainfall patterns, which leads to a relatively large uncertainty band around the derived
discharge statistics.
This uncertainty also has a direct influence on the determination of failure probabilities by means offragility curves. The analysis determined the failure probability for erosion of the flood defense
system as a result of overflow/overtopping. First, the fragility for the different breach locations
without dependencies between the breach location was determined (black lines in Figure 28) andthen the dependencies between the different breach locations were included (red lines in Figure28). This means that the more downstream breach locations have a smaller chance because an
upstream breach has a reducing effect on the chance of a downstream breach. Due to the inclusionof these dependencies, the probabilities vary between 1/100 per year for the more upstream
breach locations to less than 1/100,000 per year for the more downstream locations.
Figure 28 Fragility curves for one location

To determine the flood risks, the probabilities per breach locations are combined with theconsequences of the floods. For this purpose, both the economic damage and the number ofcasualties per breach location were determined. In addition to the economic risk (euros per year)
and the casualty risk (chance of casualties per year), the local individual risk (LIR) has also been
determined. The LIR describes the chance at a certain location of becoming a victim of a flood. .

4.1.1

Hydrology and Hydraulics

Figure 29 shows the confidence intervals of the probable rainfall in the Tokachi river basin for the
past experiment and the 4K warmer experiment. In this figure, the black dots represent theobserved maximum annual basin-averaged 72-hour rainfall, and the solid blue line is the Gumbel

PR3983.10 • January 2022

39

distribution that these observations are assumed to follow. In addition, the blue and red areas are
the confidence intervals of the probable rainfall for the past and future climate, respectively. The
confidence intervals for the past and future climates are constructed using a physical Monte Carlo
method described in section 3.3.2. Number of calculated maximum annual rainfall in each sample
is the same as observation. The existence of the purple overlapping area between the two intervals
indicates that heavy rainfall events with the same return period, but with different frequency, canphysically occur in both climates where the global average temperature differs by 4K. The resultsof the probability assessment based on ensemble climate data are also supported by statisticaltheory, and research results have been obtained to ensure its scientific validity (Shimizu et al.,2020). In this study, we adopted the probability limit method (Moriguti, 1995), which allows us toderive the statistical threshold of rainfall that can occur under the assumed probability distributionof maximum annual rainfall, and constructed confidence intervals based on this test. The blue andred dotted line in Figure 29. Figure 29 show the width of the confidence interval based on thistheory. The figure shows that the confidence intervals based on the ensemble climate data and theconfidence intervals based on probability limit method coincide with each other very well for both
past and future climates. The coincidence of the confidence intervals constructed independently
from the physical Monte Carlo calculations and the probability limit method supports themathematical validity of the present study.
Figure 29 Left: Confidence interval of probable rainfall derived from d4PDF-5km (Shimizu et al., 2020). Right:
Frequency distribution of 150-year annual maximum 72-hour rainfall in past experiments (blue bins) and theprobability density function f R(r,150) of the Gumbel distribution fitted to the frequency distribution (solid blueline).

The relationship between the rainfall volume and the peak discharge is determined based on
spatio-temporal rainfall patterns and differences of runoff model and its parameters. The scatterplots of rainfall volume and peak discharge from different runoff models are shown in Figure 30.
Note that the runoff models which described in 3.3.4 adopted default parameters and initialconditions. Although the parameters need to be adjusted for practical use, this setting was usedbecause the purpose of this study was to propose a method to realize evaluation based on the
differences in models. The proposed method can estimate peak discharge distribution at each
return period in consideration of multiple runoff models by applying the regression curve to therainfall-peak discharge relationship for each runoff model or for the combining result of runoff
models. In the following part, the return period of the peak discharge is estimated according to therelationship between rainfall volume and peak discharge of single model.

PR3983.10 • January 202240

Figure 30 Relationship between annual maximum 72-hour rainfall and peak discharge estimated by the different
runoff models at the reference point in the Tokachi River basin

The relationship between the annual maximum 72-hour rainfall and the peak discharge for eachexperiment and the estimated quantile regression curves are shown in Figure 31. Figure 31 also
shows derivation process of the probability distribution of the 150-year peak discharge in the pastexperiment and the 4K warmer experiment based on result of single model. Also, Figure 32
comparison of the probability density function of the 150-year peak discharge for the pastexperiment and the 4K warmer experiment. The shape of the probability distribution is shifted
under different climatic conditions. The multiplier of future change is 1.88 for the expected valueand 2.04 for the 95% upper confidence limit of the 4K warmer experiment and the pastexperiment.
The 150-year peak discharge based on this method were calculated at the Obihiro, Satsunai, Bisei
and Otohuke River confluences point. These probable peak discharges can be used as a starting
point for the evaluation of water levels caused by flows equivalent to the planned probability scale,the evaluation of the probability of overtopping and levee breaching, and the quantification ofhuman and economic risks.
Figure 31 Process of deriving the T-year probability peak flow rate in the past experiment (left) and the 4K
warmer experimentPR3983.10 • January 2022

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Figure 32 Comparison of the probability density function of the 150-year probability peak flow rate for the pastexperiment and the 4K warmer experiment.

4.1.2

Failure of Levees

According to the calculation results of the probability of levee failure, the probability of levee failure
near KP60.8 of the Tokachi River is small, about 10-6. This may be due to the fact that this section
includes a bridge and is relatively high compared to the sections before and after it. On the other
hand, the probability of levee failure near KP58.8 of the Tokachi River is relatively higher than thatof the sections before and after it. Although the average height of the embankment is high due tothe inclusion of the bridge, the difference in the height of the embankment between the bridge andthe section before and after the bridge is large, and the uncertainty of the height of theembankment within the section is thought to have influenced the increase in the probability ofembankment failure. This point needs to be accurately evaluated in the future by assessing theuncertainty as only positive variability.
The results of the uncertainty sensitivity analysis indicate that the water level-flow relationship hasa significant effect on the probability of levee failure at the target locations. The relationshipbetween water level and flow is due to the temporal changes in the riverbed topography. Itchanges with each outflow as well as with time during the outflow. This result is considered torepresent the flood characteristics of the target watershed, which is characterized by high flow
velocity and large water level changes.
Three uncertainties are taken into account in the probability of levee failure. It is unclear how muchinfluence this uncertainty has on the risk assessment in WP3. If the impact on the outcome of therisk assessment is small, the uncertainty here may be negligible. On the other hand, if the impact
is large, it may be necessary to reconfirm the factors that cause uncertainty and conduct sensitivity
analysis of the data. In the future, we believe that a sensitivity analysis of the uncertainty of thecalculation results of each WP to the risk assessment results should be conducted.
As for the flood probability, within a section of the same river with the same planned size
(discharge), the results showed that the levee failure probability was larger upstream and smaller
downstream. For example, in the section of the Tokachi River after the confluence of the
Shikaribetsu River (from KP59.6 to KP56.4), the return period was about 1.5×103 years upstream
and about 4.5×106 years downstream. In the Satsunai River, the return period is about 3.0 x 103
years in the upstream side and about 4.9 x 106 years in the downstream side. This result is due tothe fact that the failure probability of the upstream bank is deducted when calculating the failure
probability of the downstream bank.

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The hazard maps published in Japan show inundation damage based on the planned size of theriver and the assumed maximum external force. The condition for a levee failure is an exceedanceof the planned elevation. The results of the inundation analysis are superimposed on the results of
the inundation analysis for all the assumed breach points. In other words, the level of safety oflevees is uniform, and levee breaches are considered as independent phenomena. This method canprovide residents with a safer assessment of the hazard. On the other hand, when quantifying
damage for the purpose of flood risk assessment, there is a possibility of overestimation.
The approach in this study adopts a non-independent approach that expresses the probability oflevee breaches at each location in a probabilistic approach and takes into account the occurrence oflevee failures upstream. As a result, this method expresses the probability of flood occurrence in amore realistic way. In the future, quantitative flood risk assessment will be required to promote
flood control measures in flood plains. By adopting the method proposed in this study, moreaccurate flood risk assessment will be possible.

4.1.3

Damage, evacuation, loss of life and Risk assessment

In chapter 3 the methods and application on the case study are shown. The results show that theflood 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 and especially when high value assets do flood in one scenario and not in another scenario,
this can lead to significant differences in the calculated damage.
It is therefore important to conduct a proper analysis on the damage calculation and how theeconomic damage may be affected by local high value assets.
The average evacuation rate in the Obihiro case study is set to 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 resulting in a potential flash floods. One of characteristics of a flash
flood is that there is relatively little time for evacuation and that is what also is the result in thiscase.
The potential flooding of the Obihiro case study area leads to large water depths with locally highrise rates. This can lead to potentially high numbers of loss of life. In several scenarios the numberof casualties is in the hundreds, even when the expected value of the evacuation rate is taken into
account.
The flood risks are the product of the flood probability 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. The impact of climate change is large, on the one handthe flood extent and flood characteristics increases and on the other hand the flood probability
increases this leads to a significant increase in economic risk.

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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 the
impact 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 annum (or 0.001 %) for every resident living behind thelevees. This is the so-called tolerable individual risk.

4.2

Dealing with uncertainties

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 integrated approach
illustrated the impact of uncertainties and how it influences the final risk. The integrated approachalso created a lot of insight in the consequences of choices made during the modelling. These
consequences because clear because the complete chain of models were discussed during the
project.
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.
In the flood risk analysis for the Obihiro case study the impact of these uncertainties are shown bypresenting the bandwidths of the calculated flood risks and provide insight into the causes of the
bandwidth. The insights in these bandwidths can be used to define the need to do more research to
reduce the bandwidth. But in the case that the occur or lower limit to the bandwidth will not resultin a different decision or measures it can also been concluded that the information is good enoughto make these decisions.

4.3

Working together in a joined project

The greatest added value lies in the fact that the Japanese and Dutch experts were working closely
together on the different topics. This involved collaboration between universities, knowledge
institutes and companies, people from Japan and the Netherlands, and collaboration between thevarious tracks. Because analyses were actually performed and models have been applied, it was
concrete, and it was everyone's responsibility to also solve problems such as imperfect data.
Mutual coordination was also important, because others continued with outcomes that you had
determined. These insights into the impact of each contribution, and to apply the methods has
provided a lot of insight.
The success factor was that results and methods were not only discussed, but also applied thesemethods together and really tried to understand the details to generate the results. Working in thisway creates mutual understanding and trust in order to have a good basis for the added value ofPR3983.10 • January 2022

44the project results in practice. This resulted in an integrated risk framework, based on Japaneseand Dutch experiences, which can be used for risk assessments and further developed.
During the project we, as everybody in the world, were confronted with the COVID-19 pandemic.
This had a great impact on the project because we could not meet any more in Japan or the
Netherlands. During the project however we adapted our way of working together and sharing
information. We succeeded in the project because of the possibility to have online meetings, thecontribution of Maki Masaki san as in interpreter, and the support of the Dutch Embassy withorganising workshops in Japan. Draft results have been presented (digital) in a special session
during the IAHR conference 2020 in Sapporro.
The project also contributed to a sustainable relation between the involved experts resulting inscientific papers, and interest in the way of living and working in both countries.

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5

Recommendations

5.1

Extreme rainfall and discharges

In this case study, not all details of the method could be explored. Therefore, some shortcuts weremade in the approach, that require attention in any follow-up research. The following
improvements and recommendations can be made:•Calibration of the models; The models used in this study were not calibrated. Only a quick
assessment was made of the quality of the models. Based on a simple sensitivity analyses, thebest model parameter set was selected. The quality of the models has impact on the outcomeof WP1, the discharge statistics. Therefore, before using the output from WP1 for any realapplications, the quality of the model needs to be further improved.

•

Impact of initial conditions; An import uncertainty in the model is the initial state of thehydrological models. Since we are running the models on event basis, we also start each eventwith the same initial conditions. In reality, however, the initial conditions can be very different
from event to event. Some events happen after a long time of rainfall, other events happen
after dry conditions. The storage capacity in for example the soil and the reservoirs might becompletely different, affecting the generated runoff. It is recommended to also included theinitial conditions as an uncertain parameter in the simulations and include it in the uncertainty
estimations given to WP2.

•

Impact of reservoir operations; Reservoirs and their operation can have a significant impact on
the downstream discharges. The reservoir operations are for now not included in thehydrological models. This is an important point on which to improve the models. It is also acomplex point, since in many cases the real operations are not well known. More research in
the operation of the reservoirs, and how to include it in the hydrological models, isrecommended.

•

Hydrograph shape; For now, not much focus was given to the derivation of the hydrographshape. This needs more attention, since the shape (and volume) of the hydrograph have an
impact on downstream flood levels and flood risk.
Focus on mountainous areas; For now, a lot of attention was given to the urban areas and theimpact of flooding there. During the many flood events on Hokkaido it was observed that also
in the mountainous areas there was a lot of damage to roads, bridges and other (critical)
infrastructure. Although the direct costs related to these damages is small compared todamages in the urban areas and associated loss-of-life, the cascading effect might also result inlarge additional damages and maybe even loss-of-live due to the fact certain areas can noteasily be reached by emergency services.

•

Also, climate change is most likely going to affect the mountainous areas stronger, resulting in
potentially increasing damages in these areas. It is therefore recommended to also do anassessment of the flood risk in these areas.

Longer-term research is required to look at the impact of sediment behaviour on the flood risk in
the Tokachi River Basin. It is known from earlier experience that sediment input and morphologicalchanges in the riverbed during the event can have large impact on the simulated water levels andstability of the dikes. This effect is not included in the models, but can be added, albeit in a
simplified manner. It is recommended to investigate this topic and think of a way sediment
behaviour can be added to the overall methodology for flood risk assessment in the Tokachi River
Basin and in general.

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5.2

Failure probabilities

In this study, levee failure due to overtopping is the subject of evaluation. In this study, we
focused on levee failure due to water overtopping, but there are some reports of levee failure due
to erosion and piping in Japan. Therefore, the probability of levee failure due to other levee failure
mechanisms should be evaluated in the future.
In this study, flood hydraulics are calculated assuming a model waveform. Flood runoff in Japan ischaracterized by various shapes of hydrographs, which are expected to affect the probability oflevee failure and damage during inundation. Therefore, the flood probability should be evaluatedtaking into account the shape of the hydrograph.

5.3

Damage calculation

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.
A comparison of the two approaches would be useful to see the strengths of both methods. Whatare potential method gaps, where does one model perform better than another and where could
the methods be improved.

5.4

Evacuation and loss of life

The current evacuation method uses only two destinations, people are either at a safe 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.
So an improvement of the evacuation modelling will lead to a much better insight in the location
where people will go 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 a evacuation order isissued. Given the location 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 to the different locations where people evacuate
to, the model can also be improved by incorporating the Japanese type of constructions. The
current Dutch method is based on the Dutch type of constructions (concrete), the Japanesebuilding construction consists mostly of wood which can have an impact on the possibility ofbuilding collapse which will lead to a higher probability of casualties. This is also an aspect that can
improved to the model to make the models more suitable for the Japanese situation.

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6

Last words

I would first like to express my gratitude towards the project “Flood Risk and Climate Change
Hokkaido,” which gave us such a great opportunity to learn about the advanced climate adaptation
approaches of the Netherlands in flood control and to work on developing a new risk assessment
method throughout the joint research between Japan and the Netherlands.
The Netherlands’ climate adaptation measure is quite advanced, valuable and essential for
Hokkaido to improve its flood control. I deeply appreciate the generosity of Dutch experts for theircooperation, and the efforts by all the relevant persons to this study.
It has been long since increasing intensity and frequency of flood due to climate change wasappealed in Japan. In recent years large scale floods occur every year, causing extreme damage,which threatens physical and mental safety of the citizens.
Hokkaido is a region surrounded by a rich natural environment, and many people enjoy thebenefits of food supply nationwide and nature-related tourism.
On the other hand, such a rich nature caused historically also disasters. Especially large scale
floods occur more frequently in recent years, though it used to be less extreme and less frequentcompared to the rest of Japan.
Hokkaido is estimated to sustain the greatest impact by increasing flood risk due to climatechange, in Japan. This is why the discussion has been initiated, based on the following key words.
•

Hokkaido needs to take an initiative to establish new flood control measures for next
generation, because it expects extremer impact by climate change than the rest of Japan.

•

Flood control measures based on risk assessment should be taken, by scientifically estimatedfuture impact of climate change.

Thanks to this project, we have achieved extraordinary results.
Hokkaido will facilitate to further develop this study, and repeat reflecting its results to our society.
We believe this is our mission to challenge the impact of climate change step by step in order tosecure the physical and mental safety of citizens, and eventually this will bring Hokkaido the future
prosperity.
Research never ends. By deepening a research, it is necessary to continuously explore a new wayof solution for upcoming challenges. It would be so encouraging if we could continue with technicalexchange with the Netherlands to open up a new way together to confront such a huge challenge
like climate change.
It has been about 150 years since Hokkaido was developed. Though there are accumulated
challenges in front of us, it would be our great pleasure if we could proceed one step further tosecure physical and mental safety of Hokkaido by improving future flood control.

Kan MATSUBARARiver Planning Officer, River Planning Division, Construction Department, Hokkaido Regional
Development Bureau, Ministry of Land, Infrastructure, Transport and Tourism

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