大規模アンサンブルシミュレーションに基づく気候
変動の不確実性を考慮した洪水リスクの評価
メタデータ

言語: English
出版者:
公開日: 2021-12-09
キーワード (Ja):
キーワード (En):作成者: グエン, タイン

トゥー

メールアドレス:所属:
URL

https://doi.org/10.15118/00010423

Muroran IT
Muroran Institute of Technology

DISSERTATION
Flood risk assessment considering uncertainty in climatechange impacts using large-ensemble simulations

Nguyen Thanh Thu

FLOOD RISK ASSESSMENT CONSIDERING UNCERTAINTY
IN CLIMATE CHANGE IMPACTS USINGLARGE-ENSEMBLE SIMULATIONS

by

Nguyen Thanh Thu
A dissertation submitted in partial fulfillment of the requirements for the degree ofDoctor of Engineering

Examination Committee:

Prof. Makoto Nakatsugawa
Prof. Katsutoshi Kimura
Prof. Mikiharu Arimura

Muroran Institute of Technology
Division of Sustainable and Environmental EngineeringJapan
September 2021

Acknowledgements
This is my third cherry blossom season in the beautiful country, Japan. During
three years, I had a great chance to meet friendly Japanese friends and explore
Japanese culture and traditions. It was the most memorable time for me. Besides,
I also learned a lot of valuable specialized knowledge. Now, I am so proud that thedifficult research path has reached the final stage. I can look back on my study
process, and write this dissertation book. It is my pleasure to acknowledge the rolesof several individuals who supported me too much for the completion of my Ph.D.
research.
At first, the most appreciation I would like to send to my supervisor, Professor.
Makoto Nakatsugawa, for his excellent guidance, caring, patience, motivation,
passion, and providing me with a great study environment for doing research. I
could never finish my research without his kind direction and proper guidance.
I am also grateful to Associate Professor. Tomohito. J. Yamada, and Dr.
Tsuyoshi Hoshino who provide important data to me, support me too much during
three years of research, and have given helpful advice about my major. Without
this precious support, it would not be possible to complete this research.
I would like to express my respect and great thank to Professor. Katsutoshi
Kimura, and Professor. Mikiharu Arimura for their support and attendance as amember of the examination committee. Their insightful comments andconstructive suggestions have helped me to improve the quality of my research.
I would like to express my special thank to Dr. Takehiro Oki, who support me
too much, and encourages me to do my research. His patience, motivation, andencouragement helped me successfully carried out this assignment.
My sincere thanks to the laboratory members, who help me too much not only
in my research but also in all the difficult things I faced when I have lived in Japanfor three years. Especially thanks to Ms. Sakamoto Riko, Mr. Sakuma Kanzu, Mr.
Junya Seki, and Mr. Tanaka Masato for their great kind support, and take care ofme during my Ph.D. course.
Sincere gratitude is extended to the Japanese Government for providing thescholarship for my Ph.D. course. I would like to offer my special thank to theInternational Center for Relation of Muroran Institute of Technology for their great
support while I am living in Japan.
I would like to offer my special thanks to my friend Dr. Try Sophal, whoalways helped me too much in my research, as well as gave me a lot of usefuliadvice for my research. Without his kind support, this study would face many
challenges. I will always remember his kindness and support.
I greatly appreciate my friends Mr. Doan Hoa Khiem, Mr. Le Manh Tien, Mr.
Tran Nguyen Luong, who helps me too much when I have trouble with my research
for three years. Besides, I greatly appreciate Dr. Pham Quang Vuong, Mr. Le Huy
Sinh, Mr. Ta Quy Hung, Ms. Tanya, and other Vietnamese friends, who always
help me too much and share even joy or stress when I live alone in Japan for three
years.
Last but not least, I would like to send my special thank to my soulmate, whoalways beside me and support me too much, and willing to share every moment,
stress, and happiness every day. Thank you too much for everything. One of the
key factors that encourage me too much during my research, I would like to express
my special thanks to my family. Although my family lives in Vietnam, I always
feel that my family beside me. I am deeply grateful to my parents, my brother, mysister-in-law, my nephew, my niece for their encouragement all time. All of thiswork is for them. I think I would never have been able to finish my Ph.D. researchwithout their support and encouragement.

Muroran, 2021
Nguyen Thanh Thuii

Abstract
In recent decades, extreme weather events associated with climate changes have
rapidly become one of the global concerns threatening natural environments and human
life. Floods are the most frequent type of natural disaster on earth, causing massive
damage all over the world. Flood disaster is considered to become more frequent andhigher intensity in the future. In particular, people who live in floodplain areas, or lackearly flood warning systems are the most affected by flooding. In Japan, flood inundation
disasters due to short-term extreme rainfall have occurred frequently and caused
considerable damage in recent years. Notable severe flood events in recent years in Japan
can be mentioned as the large-scale flood event in August 2016 in Hokkaido, or severe
flooding and landslides occurred in many river basins in central-northern parts of Japanin October 2019, causing great loss of life and property.
The northernmost Japanese island, Hokkaido has recorded severe floods and caused
massive damage, such as the flood event in August 1981, and recently in August 2016.
The Ishikari River basin plays a vital role in the socio-economic development of
Hokkaido. The Ishikari Plain occupies most of the basin’s area and is located around thecentral and downstream basin area, which is the most productive agricultural area not
only in Hokkaido, but also entire Japan.
Therefore, this study aims to investigate the change in extreme rainfall and severe
floods associated with climate change, choosing the Ishikari River basin, Hokkaido, Japanas a case study. Change in extreme rainfall and river floods in the Ishikari River basin, as
well as in its main sub-basins is evaluated using the Integrated Flood Analysis System(IFAS) coupled with the large-ensemble rainfall dataset with a super high-resolution of 5km as an input data to the model. In addition, change in flood inundation in the ChitoseRiver basin, a tributary of the Ishikari River is also evaluated using the Rainfall–Runoff–
Inundation (RRI) model and a large-ensemble rainfall dataset of 5 km (d4PDF). Owing
to the topographical characteristics of its low-lying area, the Chitose River basin isfrequent affected by backwater from the Ishikari River and experiences severe floodinundation. The remarkable point of this study is to use the large-ensemble and superhigh-resolution climate simulations for extreme rainfall and severe floods assessment. It
could predict extreme rainfall and severe flood events with return periods equal to or
larger than 100 years. Simulation with large ensemble members could properly verify theuncertainty in the estimation of the probability of extreme events. The results of this study
are expected to provide useful information for river basin management, particularly forclimate change adaptation and flood damage mitigation in floodplain areas.
iii

概要
この数十年で、気候変動による異常気象とそれに起因する自然災害は、自然環境や私たちの生活に対する脅威の一つとして急速に関心を集めるようになった。こうした自然災害の中でも、洪水は世界的に頻発しており、世界各地で甚大な被害を引き起こしている。さらに、洪水による災害は、将来的により頻繁に発生し、より深刻な被害を発生させると考えられている。特に河川の氾濫原の住民や、早期の洪水警報システムが整備されていない地域で生活している人々は、洪水による影響を最も大きく受ける。日本では、近年、短時間での極端豪雨による洪水氾濫が頻発し、大きな被害をもたらしている。顕著な例として、2016 年 8 月には北海道で大規模な洪水が発生した。また 2019 年 10 月には日本の中部から東北地方にかけて多数の河川で深刻な洪水と地すべりが発生した。これらの被害により、尊い人命と資産が失われた。
日本の北端に位置する北海道では、過去には 1981 年 8 月、近年では 2016 年 8 月の洪水において、石狩川流域は甚大な被害を受けた。石狩川流域内は石狩平野が大部分を占め、その中流域から下流域にかけては北海道のみならず全国で最も生産性の高い農業地域となっていることから、北海道の社会経済的発展に重要な役割を果たしている。そのため、本研究では、北海道の石狩川流域を対象とし、気候変動による極端豪雨の降雨量の変化と、それに伴い大規模洪水を引き起こす河川流量の変化を推定することを目的としている。石狩川流域とその主要な支川における不確実性を考慮した極端豪雨のデータとして水平解像度 5kmの大量アンサンブル降雨情報(d4PDF)を用いた。また、石狩川とその主要支川の河川流量の推定には統合洪水解析システム(IFAS)を用いた。さらに、千歳川流域の洪水氾濫の変化についても、d4PDF と降雨-流出-氾濫モデル(RRI)を用いることで、石狩川からの背水影響を考慮した氾濫計算を行った。本研究では、豪雨と洪水の評価に高解像度の大規模アンサンブル
気候シミュレーションを使用したことが特徴である。これは、100 年に一度以上の低頻度で生起する極端豪雨と、それによって発生する深刻な洪水を予測することができる。さらに、大規模アンサンブル気候シミュレーションを用いることにより、極端豪雨時の降雨の不確実性を適切に評価することができる。
本研究において判明した結果は、河川流域管理、特に氾濫原における洪水被害の軽減といった気候変動への適応策に役立てることが期待される。

iv

Contents
Title
Acknowledgements……………………………………………………………..
Abstract…………………………………………………………………………
Abstract (Japanese)……………………………………………………………..
Contents………………………………………………………………………...
List of Figures…………………………………………………………………..

Page
i
iii
iv
v
viii

List of Tables……………………………………………………………………
List of Abbreviations……………………………………………………………

xii
xiii

CHAPTER 1
Introduction……………………………………………………………………
1.1. Overview………………………………………………………………….
1.2. Purposes and objectives of the research…………………………………..
1.3. Thesis outline……………………………………………………………..
1.4. References………………………………………………………………...

1
1
10
11
14

CHAPTER 2
Uncertainty of rainfall patterns……………………………………………….
2.1. Introduction………………………………………………………………...
2.2. Overview of database for Policy Decision making for Future climatechange (d4PDF)…………………………………………………………………
2.3. Summary…………………………………………………………………...
2.4. References………………………………………………………………….
CHAPTER 3
Assessing climate change impacts on extreme rainfall and severe floodingin the Ishikari River basin, Japan…………………………………………….
3.1. Introduction………………………………………………………………...
3.2. Methodology……………………………………………………………….
3.2.1. Overview of the Ishikari River basin………………………………….
3.2.2. d4PDF dataset…………………………………………………………
3.2.3. Overview of the Integrated Flood Analysis System (IFAS) model……
3.2.4. Data inputs…………………………………………………………….

18
18
23
27
28

32
32
34
34
36
38
43
v

TitlePage

3.2.5. Analytical procedure…………………………………………………..
3.3. Results and Discussions……………………………………………………
3.3.1. Hydrological model calibration and validation………………………..
3.3.2. Future changes in extreme rainfall…………………………………….
3.3.3 Future changes in extreme rainfall between the top 20 and top 36 rainfall
events…………………………………………………………………………

44
46
46
49

3.3.4. Future changes in severe river floods between the top 20 and top 36
rainfall events…………………………………………………………………
3.3.5. Time difference prediction…………………………………………….
3.4. Conclusions………………………………………………………………...
3.5. References………………………………………………………………….
CHAPTER 4
Flood inundation assessment in the low-lying river basin considering
extreme rainfall impacts and topographic vulnerability……………………
4.1. Introduction………………………………………………………………...
4.2. Materials and Methods……………………………………………………..
4.2.1. Description of the study area………………………………………….
4.2.2. d4PDF dataset…………………………………………………………
4.2.3. Overview of the Rainfall-Runoff-Inundation (RRI) model……………
4.2.4. Data inputs……………………………………………………………..
4.2.5. Analytical procedure…………………………………………………...
4.3. Results and Discussions…………………………………………………….
4.3.1. RRI model calibration and validation………………………………….
4.3.2. Evaluation of climate change impacts on river flood in the Ishikari
River basin……………………………………………………………………
4.3.3. Evaluation of climate change impacts on flood inundation in the
Chitose River basin…………………………………………………………...
4.3.4. Evaluation of time difference impacts on flood inundation in the
Chitose River basin…………………………………………………………..
4.3.5. Discussions…………………………………………………………….
4.4. Conclusions………………………………………………………………...
4.5. References………………………………………………………………….

50
56
60
61
62

67
67
71
71
73
74
78
85
87
87
90
91
97
98
100
102
vi

TitlePage

CHAPTER 5
Conclusions and Suggestions………………………………………………….
5.1. Introduction………………………………………………………………...
5.2. Summarize of the study findings…………………………………………..
5.3. Proposing flood mitigation measures………………………………………
5.4. Suggestions for future researches………………………………………….

107
107
107
109
113

Appendix……………………………………………………………………….
Published works………………………………………………………………...

114
114

vii

List of Figures
List
Figure 1.1 Annual global average temperature from 1891 to 2017……………..
Figure 1.2. Annual precipitation (over land area only) from 1901 to 2017 for the
globe…………………………………………………………………………….
Figure 1.3 Change in (a) globe average surface temperature and (b) globe
average precipitation for 2081-2100 relative to 1986-2005 under the RCP 2.6
scenarios (left) and RCP 8.5 scenarios (right)………………………………….
Figure 1.4 The city of Hitoyoshi, Kumamoto Prefecture, is seen flooded in July,
2020 after the Kuma River overflowed due to torrential rainfall……………….
Figure 1.5 People using kayaks and paddle boards navigate a residential
neighbourhood inundated with floodwaters as severe flooding affects the suburbof McGraths Hill in Sydney, Australia, March 24, 2021……………….
Figure 1.6 A flooded slum in Manila in August, 2012…………………………..
Figure 1.7 Annual surface temperature over Japan……………………………..
Figure 1.8 Annual number of days with (a) precipitation ≥ 100 mm, and (b)precipitation ≥ 200 mm from 1901 to 2017……………………………………..

Page
1
2

3
4

5
5
6
7

Figure 1.9 Severe flood events in (a) August 2016 in Hokkaido, and (b) July
2018 in western Japan…………………………………………………………..

8

Figure 1.10 Climate change adaptation cycle…………………………………..

10

Figure 1.11 Research outline……………………………………………………

14

Figure 2.1 Global mean temperature anomalies (oF) relative to 1976-2005 for
four RCP scenarios, 2.6 (green), 4.5 (yellow), 6.0 (orange), and 8.5 (red)...…….

19

Figure 2.2 Coarse data from Global Climate Models (GCMs) forms the initialand boundary conditions necessary for Regional Climate Models (RCMs) todynamically downscale to much finer resolution climate data…………………..

22

Figure 2.3 A schematic view of these research activities form aninterdisciplinary community on global warming research………………………

24

Figure 2.4 Descreption of d4PDF……………………………………………….

25

Figure 2.5 Experimental design of d4PDF………………………………………

26

Figure 3.1 Ishikari River basin, Hokkaido, Japan and its main sub-basins……..

35
viii

List

Page

Figure 3.2 +4K future climate simulation for the Tokachi River basin at Obihiro
point station with the rainfall data of 20-km resolution (left) and 5-km resolution(right)……………………………………………………………………………

38

Figure 3.3 Functions of IFAS model…………………………………………….

39

Figure 3.4 Schematic of the IFAS model……………………………………….

41

Figure 3.5 Applicable/Outside the scope Phenomenon for IFAS………………..

42

Figure 3.6 Flowchart of the procedure for flood simulation by the IFAS model…

43

Figure 3.7 Elevation data and Land-use data……………………………………

44

Figure 3.8 Comparison of observed and simulated discharges at Ishikari Ohashi
station for the (a) 1981 flood event, (b) 2001 flood event, (c) 2011 flood event,(d) 2016 flood event…………………………………………………………….

48

Figure 3.9 Relative frequency of annual maximum rainfall (mm/ 72h) in the
Ishikari River basin for the observed rainfall data, and the historical and future
simulations………………………………………………………………….......

50

Figure 3.10 Boxplots of annual maximum rainfall (mm/ 72h) for the top 20 and
top 36 rainfall events in the Ishikari River basin and in its main sub-basins……

53

Figure 3.11 Percentage difference in the median of annual maximum rainfall(mm/ 72h) between the top 20 and top 36 rainfall events………………………

54

Figure 3.12 Spatial distribution of annual maximum rainfall (mm/ 72h)
averaged for the (a) top 20 rainfall events and (b) top 36 rainfall events; (c)percentage difference between the selected historical and future events……….

55

Figure 3.13 Boxplots of the peak runoff depth (mm/h) for the top 20 and top 36
rainfall events in the Ishikari River basin and in its main sub-basins…………..

58

Figure 3.14 Percentage difference in the median of peak runoff depth between
the top 20 and top 36 rainfall events in the Ishikari River basin and in its main
sub-basins……………………………………………………………………….

59

Figure 3.15 Time differnce averaged for the top 20 and top 36 rainfall events ineach of the sub-basin…………………………………………………………….

60

Figure 4.1. Locations of the Ishikari River basin and Chitose River basin……..

72
ix

List

Page

Figure 4.2. Severe flood inundation event in August 1981 in the Chitose Riverbasin. Data source: Hokkaido Regional Development Bureau………………….

73

Figure 4.3. Schematic of the rainfall-runoff-inundation (RRI) model…………..

78

Figure 4.4 Topographic data as inputs for the RRI model………………………

80

Figure 4.5 Land cover classification in the Ishikari River basin for (a) original
Global Land Cover Characterization, and (b) reclassified land cover types………
Figure 4.6 Plot of river width and river depth with contributing area for the
Ishikari River basin………………………………………………………………

82
84

Figure 4.7 Plot of river width and river depth with contributing area for the
Chitose River basin………………………………………………………………

84

Figure 4.8 Plot of river width and river depth future plan with contributing area
for the Ishikari River basin………………………………………………………

84

Figure 4.9 Comparison between the simulated and observed discharges at
Ishikari Ohashi station for (a) the August 1981 flood event, (b) the September
2001 flood event, (c) the September 2011 flood event, and (d) the August 2016
flood event………………………………………………………………………

88

Figure 4.10 Comparison between the simulated and observed inundation areas
for the flood event of August 1981 in the Chitose River basin…………………

90

Figure 4.11 Inundation area averaged for the top 20 rainfall events (historicalsimulation) and top 36 rainfall events (future simulation) with (a) inundation
depth ≥0.5 m, and (b) inundation depth ≥3.0 m…………………………………

92

Figure 4.12 Boxplots of (a) peak inundation depths, (b) inundation volumes
with inundation depth ≥0.5 m, and (c) inundation volumes with inundation
depth ≥3.0 m for the top 20 past and top 36 future rainfall events in the ChitoseRiver basin………………………………………………………………………

93

Figure 4.13. Spatial distribution of the inundation frequency averaged for (a)the top 20 rainfall events (historical simulation), (b) the top 36 rainfall events(future simulation), and (c) inundation frequency difference between the top 20
past and top 36 future rainfall events……………………………………………

95

x

List

Page

Figure 4.14. Spatial distribution of inundation durations averaged for (a) the top
20 rainfall events (historical simulation), (b) the top 36 rainfall events (future
simulation), and (c) inundation duration differences between the top 20 past andtop 36 future rainfall events……………………………………………………..

96

Figure 4.15. Relative frequency of time difference in the Chitose River basin
for the top 20 rainfall events (historical simulation) and top 36 rainfall events(future simulation)………………………………………………………………

98

Figure 5.1. Land treatment for agricultural areas……………………………….

110

Figure 5.2. Structural measures for mitigation flood damage in public works
combined to urban landscape……………………………………………………

111

Figure 5.3. Dams in Hokkaido island…………………………………………..

112

xi

List of Tables
List

Page

Table 2.1 RCP scenarios description (Source: IPCC Fifth Assessment Report
Summary for Policymakers)……………………………………………………
Table 2.2 18 Global climate models (GCMs)…………………………………..

19
20

Table 3.1 Characteristics of the Ishikari River basin and its main sub-basins andreference hydrological stations in each sub-basin………………………………

36

Table 3.2. Model configuration…………………………………………………

41

Table 3.3. Parameter value settings in the IFAS model…………………………

47

Table 3.4 Performance of the IFAS model in the Ishikari River basin………….

49

Table 3.5. Top 20 rainfall events in the Ishikari River basin and in its main subbasins……………………………………………………………………………

51

Table 3.6 Top 36 rainfall events in the Ishikari River basin and in its main subbasins……………………………………………………………………………

52

Table 3.7 Peak discharge results for the top 20 rainfall events in the Ishikari
River basin and in its main sub-basins………………………………………….

56

Table 3.8 Peak discharge results for the top 36 rainfall events in the Ishikari
River basin and in its main sub-basins………………………………………….

57

Table 4.1 Optimal parameter settings of the RRI model………………………..

88

Table 4.2 Statistical indicators of the RRI model performance…………………

89

Table 4.3. Percentage difference of the mean values of the inundation area andinundation volume for inundation depths of ≥0.5 and ≥3.0 m between the top
20 rainfall events (historical simulation) and top 36 rainfall events (future
simulation)………………………………………………………………………

92

xii

List of Abbreviations
IPCC
RCP
IFAS
d4PDF
RRI

Intergovernmental Panel on Climate Change
Representative Concentration Pathway
Integrated Flood Analysis System
Database for Policy Decision making for Future Climate Change
Rainfall-Runoff-Inundation

CMIP5
GCMs
RCMs
SST
SIC
NHRCM
AR4
CMIP3

Coupled Model Intercomparison Project Phase 5
Global Climate Models
Regional Climate Models
Sea-surface temperature
Sea-ice concentration
Meteorological Research Institute Nonhydrostatic Regional Climate
Model
Fourth assessment report
Coupled Model Intercomparison Project Phase 3

WHO
ICHARM
PWRI
DHM
GUI
GTOPO30
GLCC
USGS
MLIT

World Health Organization
International Centre for Water Hazard and Risk Management
Public Works Research Institute
Distributed hydrological model
Graphical user interface
Global digital elevation model
Global land cover characterization
United States Geological Survey
Ministry of Land, Infrastructure and Transport

DEM
DIR
ACC
SRTM
HRDB
IFD
AGCM

Digital elevation model
Flow direction
Flow accumulation
Shuttle Radar Topography Mission
Hokkaido Regional Development Bureau
Inundation frequency difference
Atmospheric general circulation modelxiii

Chapter 1
Introduction

Chapter 1
1.1 Overview
In the world:
In recent decades, human influence on the climate system is clear, and recent
anthropogenic emissions of greenhouse gases are the highest in history. As a result, theatmosphere and ocean have warmed, which results in the amount of snow and ice havediminished, the sea level has risen, and the occurrence of some climate extremes. Since
the 1950s, many observed changes are unprecedented over decades to millennia [1].
Extreme weather events have occurred frequently threatening the natural environmentand human life in recent years. According to the IPCC Fifth Assessment Report [2], on alonger time scale, the global average surface temperature increased by 0.73oC per century.
Figure 1.1 shows the annual global average temperature from 1891 to 2017. The annual
global average surface temperature in 2017 was increased by 0.38oC compared to the
1981-2010 average, and the third-highest since 1891 [3].

Figure 1.1. Annual global average temperature from 1891 to 2017 [3]In addition, precipitation is one of the most critical and useful indicators reflectingchanges in climate conditions [4]. Among many factors, precipitation is the main factor
affecting runoff formation and flow regimes [4, 5]. According to the IPCC Fifth
Assessment Report (2013 [2], precipitation is also predicted to increase significantly inthe future, especially extreme rainfall. According to the Japan Meteorological Agency(2017) [3], annual precipitation (for land areas only) in 2017 was risen 49 mm comparedto the 1981-2010 average (Figure 1.2). Long-term trends are not analyzed because the1Chapter 1
precipitation data for sea areas are not available [3].

Figure 1.2. Annual precipitation (over land area only) from 1901 to 2017 for the globe[3]
Figure 1.3 shows the change in global average surface temperature and global
average precipitation for 2081-2100 relative to 1986-2005 under the Representative
Concentration Pathway (RCP) RCP 2.6 and RCP 8.5 was evaluated [1]. It can be clearlyseen that, the average surface temperature and average precipitation is projected toincrease remarkable in the future.

2

Chapter 1

(a)

(b)

Figure 1.3. Change in (a) global average surface temperature and (b) global average
precipitation for 2081-2100 relative to 1986-2005 under the RCP 2.6 scenarios (left) and
RCP 8.5 scenarios (right) [1]
The effects of climate change are easily observed over the years. These impacts
directly affect human life, the natural environment, and the ecosystem. In particular,changes to water resources can have a big impact on people’s lives. In many regions,floods, drought problems are expected to be worse because of climate change. The riskof natural events, such as flood inundation, landslides, and sediment-related disasters
caused by extreme rainfall, is likely to increase in the future [6,7]. Approximately 8090% of all recorded disasters from natural hazards during the past 10 years have causedby floods, droughts, tropical cyclones, heatwaves, and severe storms [8]. Among thesenatural events, floods are the most frequent type of natural disaster on earth, causing
massive damage all over the world. There are three common types of floods including
flash floods, river floods, and coastal floods [8]. River floods are one of the most frequent
natural hazards and causing enormous damage [9]. Climate change may increase themagnitude and frequency of flooding in the future [9,10,11,12]. Floods can cause
3Chapter 1
widespread devastation, resulting in loss of life and property damages [8]. Floods
accounted for 34% of global natural disasters, causing an annual economic loss of morethan 2.5$ billion and 1254 deaths each year from 1960 to 2014 [13,14]. According to theWorld Health Organization floods affected directly more than 2 billion people worldwide
from 1998 to 2017. People who live in floodplain areas, or non-resistant buildings, or lackearly flood warning systems are the most affected by flooding. Drowning accounts for
75% of deaths in flood disasters. Flood disasters are becoming more frequent and higher
intensity in the future [8]. Some pictures of the effects of climate change on flood risks inthe world in recent years are shown in Figures 1.4, 1.5, and 1.6 below.

Figure 1.4. The city of Hitoyoshi, Kumamoto Prefecture, is seen flooded in July, 2020
after the Kuma River overflowed due to torrential rainfall (Source: Japantimes.co.jp)

4Chapter 1

Figure 1.5. People using kayaks and paddleboards navigate a residential neighborhood
inundated with floodwaters as severe flooding affects the suburb of McGraths Hill in
Sydney, Australia, March 24, 2021 (Source: usnews.com)

Figure 1.6. A flooded slum in Manila in August 2012 (Source: reliefweb.int/)
5Chapter 1
In Japan:
In Japan, a new record has been reached for temperature and precipitation almost
every year in recent times [15]. According to the Japan Meteorological Agency (2017)[3], the annual mean surface temperature over Japan has increased by 1.19oC on a longer
time scale, which is higher than the global average surface temperature increase (0.73oC)(Figure 1.7).

Figure 1.7. Annual surface temperature over Japan [3]Additionally, the annual number of days when precipitation is equal to or greater than
100 mm and 200 mm has increased (statistically significant at a confidence level of 99%)
during the period from 1901 to 2017 in Japan [3]. (Figure 1.8)

6

Chapter 1

(a)

(b)
Figure 1.8. Annual number of days with (a) precipitation ≥ 100 mm, and (b) precipitation≥ 200 mm from 1901 to 2017 [3]Flood inundation disasters due to short-term heavy rainfall have occurred frequently
in Japan and caused considerable damage in recent years. For example, an extreme
rainfall event on 5-6 July 2017 caused severe floods in many parts of Kyushu, Japan, andresulted in extensive damage [16]. One year later, during 5-8 July 2018, severe flood due
to extreme rainfall occurred in western Japan, and also caused the great loss of life andproperty damage [17]. In August, 2018 four typhoon attacked to Hokkaido island within
two weeks, and caused the seveve flood in this area. This disaster reportedly caused
approximately USD 260 million in damage, in addition to agricultural losses over 40,258
ha of land throughout Hokkaido [18]. In this context, the development of climate change
adaptation measures to minimize flood damage should be considered in Japan.
7Chapter 1

(a)

(b)
Figure 1.9. Severe flood events in (a) August 2016 in Hokkaido, and (b) July 2018 inwestern Japan
Literature reviews:
In the context of complex unpredictable changes of extreme weather events,
especially flood disasters caused by extreme rainfall around the world in recent years,
assessment and prediction of their changes have become the most concern of water
scientists, as well as hydrologists. Flood inundation risk assessment and prediction of
extreme rainfall as well as severe river flooding caused by extreme rainfall have been
carried out for many important river basins around the world [19,20,21,22,23]. These
efforts could provide useful information for policymakers to take measures to adapt toclimate change and minimize loss of life and property damage from floods in many
important river basins. For example, Akter et al. (2018) [23] evaluated the change in peak8Chapter 1
flow due to urbanization under current and future climate scenarios of high summer andhigh winter rainfall for 20 sub-basins of the Schijn River, Belgium. The results indicatedthat the peak flow of high summer was significantly higher from 200% to 250% than thatof the current climate conditions in this region. They concluded that climate change
impacts contribute the most to producing peak discharge in the future. In a study by
Shrestha et al. (2017) [19], the change in flood hazard under climate change scenarios
was evaluated in the Yang River basin of Thailand. The results showed that in this basin,
the temperature is expected to be warmer and wetter in the future. In addition, averageannual rainfall is also predicted to increase in the future, higher in near future and lower
in far future. Moreover, the intensity of annual floods is projected to increase for both
RCP 4.5 and 8.5 scenarios. An additional 60 km2 area of this basin is likely to be flooded
with a return period of 100 years. Therefore, assessing climate change impacts on extreme
rainfall as well as severe river flooding, and inundation for the important river basin in
the world is an important task for environmental scientists. Special attention should be
paid to the vulnerable area, low-lying areas with high potential flood inundation.
The information of change in extreme rainfall, as well as severe river floods would
be additional references for water managers in adapting to climate change and making
decisions to develop the flood early warning systems, the flood control constructions suchas dam operations, and pump stations. In addition, it is necessary to raise public awarenessof prevention and evacuation when necessary in order to mitigate the loss of life andproperty damage caused by flood inundation.

9

Chapter 1

Figure 1.10. Climate change adaptation cycle(Source: https://blackfeetclimatechange.com/our-environment/climate-changeadaptation-plan/)
1.2. Purposes and objectives of the research
Investigating the climate change impacts on extreme rainfall and severe floodinundation in vulnerable areas is an important mission in the current period. Therefore,
this research was conducted to evaluate the changes in extreme rainfall as well as severe
flood inundation considering climate change impacts, choosing the most important andlargest river basin in Hokkaido, Japan is the Ishikari River basin as a case study. In
addition, the change in flood inundation was evaluated comprehensively in the ChitoseRiver basin, the downstream low-lying area of the Ishikari River.
This focus was chosen since new records have been reached for temperature andprecipitation almost every year in recent times in Japan [15]. Moreover, the Ishikari River
with a length of 268 km, which is the longest river in Hokkaido, and the third-longest
river in Japan. Additionally, this river has the second largest basin area in Japan, with a10Chapter 1
drainage area of 14,330 km2. The Ishikari Plain occupies most of the basin’s area and islocated around the central and downstream basin area, which is the most productive
agricultural area in Hokkaido. This river basin plays a vital role in the socioeconomic
development of Hokkaido. In addition, the Chitose River basin is one of the main
tributaries of the Ishikari River. Owing to the topographical characteristics of its lowlying area, the Chitose River basin is frequently affected by backwater from the mainriver, causing severe flood inundation. As a result, the Chitose River basin is considereda vulnerable region in the Ishikari River basin. Finally, the most important reason
motivating us to conduct this research is to the best of our knowledge, there have been nostudies focusing on evaluating the impacts of climate changes on severe river floods inthe entire Ishikari River basin, in particular in its main sub-basins comprehensively.
Moreover, there have been no studies assessing the impacts of climate changes on floodinundation hazards in the Chitose River basin comprehensively. As a result, this study canbe considered a pioneering work to evaluate the impacts of climate changes on severe
flood inundation in the target basin.
To achieve the objective of this research, specific objectives need to be addressed asfollow:

Investigating the change in short-term extreme rainfall as well as its impact on severe
river flooding in the Ishikari River basin, as well as in its main sub-basins
comprehensively under various climate change scenarios.

Assessing and investigating the change in flood inundation in the Chitose River basin
considering the extreme rainfall impacts and topographical vulnerability.

1.3. Thesis outline
This research consists of five chapters, the results of the research will be presented
in the next chapters of the study. The main contents of each chapter are presented asfollows:
Chapter 2: Uncertainty of rainfall patterns
This chapter was conducted to review the uncertainty of rainfall patterns from future

projection climate methods. To evaluate the changes in climate variables to adapt to11

Chapter 1
climate change impacts, the future projection climate dataset was established using
various climate change scenarios. Prediction of changes in the flow regime is a challenge,
as it demands climate variables such as rainfall and evapotranspiration from future
projection climate models as input data. However, using future projection climate datasets
have still uncertainties. The uncertainties in input data will lead to inaccurate prediction
results from the hydrological models. In this chapter, we conducted to review of the future
projection climate models and their limitations, especially for the rainfall value. In
addition, we overview the database for Policy Decision making for Future climate change(d4PDF). It is a large-ensemble and high-resolution climate simulations for extreme
rainfall as well as severe floods assessment. It could predict extreme rainfall as well as
severe flood events with return periods equal to or larger than 100 years or more.
Simulation with large ensemble members could reduce the uncertainty in the estimationof the probability of extreme events

Chapter 3: Assessing climate change impacts on extreme rainfall and severe
flooding in the Ishikari River basin, Japan
In this chapter, we conducted to evaluate climate change impacts on extreme rainfall

and severe river flooding caused by extreme rainfall during the summer monsoon season.
The Ishikari River basin located in Hokkaido, Japan is chosen as a case study. Thechanges in the short-term extreme rainfall and the risk of river flooding associated withclimate change in the Ishikari River basin are evaluated. This is the first study to assess
the changes in extreme short-term rainfall and extreme river flooding events during thesummer monsoon season in the Ishikari River basin as well as in its main sub-basins using
an Integrated Flood Analysis System (IFAS) coupled with a large-ensemble rainfall
dataset (d4PDF) with a super high-resolution of 5 km. In addition, considering the dangersof the backwater phenomenon, the time differences between the time of peak discharge
at the reference stations in each tributary and the time of peak water level at the confluence
points in the main river were evaluated. The Student’s t-test was used to find a significant
difference between the two sets of samples (historical simulation and future simulation).
The results from this chapter would indicate which areas should be special paid attention
to mitigate the flood damage in the Ishikari River basin in the future.
12Chapter 1Chapter 4: Flood inundation assessment in the low-lying river basin considering
extreme rainfall impacts and topographic vulnerability
From the result of chapter 3, we indicated that the effect of climate change issignificant in the Chitose River basin, the downstream lowland area of the Ishikari Riverbasin. This river basin is considered to be the most flood-prone in the Ishikari River basin.
Therefore, in this chapter, we aim to focus on evaluating the change in flood inundation
risk comprehensively (including inundation area, inundation volume, peak inundation
depth, inundation frequency, and inundation duration) considering the extreme rainfall
impacts and topographic vulnerability in the Chitose River basin using the RainfallRunoff-Inundation (RRI) model and rainfall data extracted from d4PDF with a superhigh-resolution of 5 km. Owing to the topographical characteristics of its low-lying area,
the Chitose River basin is frequently affected by backwater from the Ishikari River andexperiences severe flood inundation. Moreover, this chapter is conducted to improve thetime difference prediction result considering the dangerous impacts of backwater
phenomenon on flood inundation risk in the Chitose River basin using the RRI model,
which uses a 2D distributed hydrodynamic model with diffusive wave models. The timedifference is evaluated between the peak discharge at the reference station in the ChitoseRiver basin and the peak water level at the confluence point intersecting the Ishikari River.
In addition, the statistical Kolmogorov-Smirnov (K-S) test, which is a nonparametric testof two samples, was used to examine the variation in flood inundation between thehistorical and future simulations.


Chapter 5: Conclusions and suggestions
This chapter summarizes the main results obtained in this research, discusses theresearch targets that have been met, limitations of the study. In addition, we propose somestructural and non-structural measures to mitigate flood risk in vulnerable areas. Finally,suggestions for future research are also recommended in this chapter.

13

Chapter 1

Chapter 1
Introduction
Chapter 2
Uncertainty of rainfall patterns

Chapter 3
Assessing climate change impacts on extreme rainfall
and severe flooding in the Ishikari River basin, Japan
Chapter 4
Flood inundation assessment in the low-lying river basin
considering extreme rainfall impacts and topographic vulnerability
Chapter 5Conclusions and suggestions
Figure 1.11. Research outline
1.4. References
1. IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working
Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC,
Geneva, Switzerland, 151 pp.
2. Intergovermental Panel on Climate Change (IPCC). Climate Change 2013: The
Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D.,
Plattner, G.-K., Tignor, M.M.B., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex,
V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK; New York,NY, USA, 2013; Volume 3, pp. 154–196.
14

Chapter 1
3. Japan Meteorological Agency. Climate Change Monitoring Report 2017. Available
online: https://www.jma.go.jp/jma/en/NMHS/ccmr/ccmr2017_high.pdf (accessed on
20 December 2020).
4. Wu, F.; Wang, X.; Cat, Y.; Li, C. Spatiotemporal analysis of precipitation trends
under climate change in the upper reach of Mekong River basin. Quat. Int. 2013, 392,
137–146. DOI:10.1016/j.quaint.2013.05.049.
5. Willems, P.; Vrac, M. Statistical precipitation downscaling for small-scalehydrological impact investigations of climate change. J. Hydrol. 2011, 402, 193–205.
DOI:10.1016/j.jhydrol.2011.02.030.
6. Hirabayashi, Y.; Kanae, S.; Emori, S.; Oki, T.; Kimoto, M. Global projections ofchanging risks of floods and droughts in a changing climate. Hydrol. Sci. J. 2008, 53,
754–772. DOI:10.1623/hysj.53.4.754.
7. Zenkoji, S.; Oda, S.; Tebakari, T.; Archevarahuprok, B. Spatial characteristics offlooded areas in the Mun and Chi River basins in Northeastern Thailand. J. DisasterRes. 2019, 14, 1337–1345. DOI:10.20965/jdr.2019.p1337.
8. World

HealthOrganization.

2021.

Available

online:https://apps.who.int/iris/bitstream/handle/10665/76224/9789241564526_section_2_
eng.pdf. (accessed on 04 May 2021).
9. Tanoue, M.; Hirabayashi, Y.; Ikeuchi, H. Global-scale river flood vulnerability in thelast 50 years. Scientific Reports. 2016, 6, 1-9. DOI: 10.1038/srep36021
10. Hirabayashi, Y.; Mahendran, R.; Korala, S.; Konoshima, L.; Yamazaki, D.; Watanabe,S.; Kim, H.; Kanae, S. Global flood risk under climate change. Nature Climate
Change. 2013, 3, 816–821. DOI: 10.1038/nclimate1911
11. Arnell, NW.; Lloyd-Hughes, B. The global-scale impacts of climate change on water
resources and flooding under new climate and socio-economic scenarios. Climatic
Change. 2014, 122, 127–140. DOI: 10.1007/s10584-013-0948-4
12. Winsemius, HC.; Aerts, JCJH.; Beek, LPHv.; Bierkens, MFP.; Bouwman, A.;
Jongman, B.; Kwadijk, JCJ.; Ligtvoet, W.; Lucas, PL.; Vuuren, DPv.; Ward, PJ.
Global drivers of future river flood risk. Nature Climate Change. 2016, 6, 381–285.
DOI: 10.1038/nclimate2893
13. Petit-Boix, A.; Arahuetes, A.; Josa, A.; Rieradevall, J.; Gabarrell, X. Are we
15

Chapter 1
preventing flood damage eco-efficiently? An integrated method applied to postdisaster emergency actions. Sci. Total Environ. 2017, 580, 873-881. DOI:
10.1016/j.scitotenv.2016.12.034
14. Zhang, D.; Shi, X.; Xu, H.; Jing, Q.; Pan, X.; Liu, T.; Wang, H.; Hou, H. A GIS-based
spatial multi-index model for flood risk assessment in the Yangtze River Basin, China.
Environmental

ImpactAssessment

Review.

2020,

83,

106397.

DOI:

10.1016/j.eiar.2020.106397
15. Yamada, T.J. Adaptation Measures for Extreme Floods Using Huge Ensemble ofHigh-Resolution Climate Model Simulation in Japan, Summary Report on the
Eleventh Meeting of the Research Dialogue. United Nations Framework Convention
on Climate Change (UNFCCC), Bonn, Germany. 2019, pp. 28–29. Available
online: https://unfccc.int/sites/default/files/resource/RD11SummaryReport.pdf (accessed on 8 April 2020).
16. Shakti, P.; Nakatani, T.; Misumi, R. Hydrological simulation of small river basins inNorthern Kyushu, Japan, during the extreme rainfall event of July 5–6, 2017. J.
Disaster Res. 2018, 13, 396–409, DOI:10.20965/jdr.2018.p0396.
17. River Council for Social Infrastructure Development. Flood Risk Management for
Wide-Area and Long-Lasting Rainfall–Multi-Layered Countermeasures for Complex
Disasters

2018.

Available

online:https://www.mlit.go.jp/river/mizubousaivision/toushin_e/1812_flood_risk_manage
ment_e.pdf (accessed on 20 December 2020).
18. Japan Society of Civil Engineers (JSCE). Onsite Field Investigation Team, Site–
Investigation Report for the Hokkaido Heavy Rainfall Disasters in August 2016; Title
was translated by N. Kimura; JSCE Report; JSCE: Tokyo, Japan, 2017. (In Japanese).
19. Shrestha, S.; Lohpaisankrit, W. Flood hazard assessment under climate change
scenarios in the Yang River basin, Thailand. International Journal of Sustainable
Built Environment. 2017, 6: 285-298. DOI: 10.1016/j.ijsbe.2016.09.006.
20. Try S, Tanaka S, Tanaka K, Sayama T, Hu M, Sok T, Oeurng C. Projection of extreme
flood inundation in the Mekong River basin under 4K increasing scenario using large
ensemble climate data. Hydrological Processes. 2020, 34, 4350-4364. DOI:
10.1002/hyp.13859.
16

Chapter 1
21. Try, S.; Tanaka, S.; Tanaka, K.; Sayama, T.; Lee, G.; Oeurng, C. Assessing the effects
of climate change on flood inundation in the lower Mekong Basin using highresolution AGCM outputs. Prog. Earth Planet. Sci. 2020, 7, 34. DOI:
10.1186/s40645-020-00353-z
22. Yamamoto, K.; Sayama, T. Impact of climate change on flood inundation in a tropicalriver basin in Indonesia. Prog. Earth Planet. Sci. 2021, 8, 5. DOI: 10.1186/s40645020-00386-4
23. Akter, T.; Quevauviller, P.; Eisenreich, SJ.; Vaes, G. Impacts of climate and land usechanges on flood risk management for the Schijn River, Belgium. Environmental
Science and Policy. 2018, 89, 163-175. DOI: 10.1016/j.envsci.2018.07.002

17

Chapter 2
Uncertainty of rainfall patterns

Chapter 2
2.1. Introduction
The Earth’s climate past and future always change in response to both natural andanthropogenic drivers. Human emissions of greenhouse gases such as carbon dioxide(CO2), methane (CH4), and other greenhouse gases overwhelm the impacts of naturaldrivers on the external forcing of Earth’s climate. Even if the present concentration could
be stabilized, the temperature would continue to increase by 0.6 oC over this period,
compared to the period of 1980 – 1999. Over the next few decades, the greenhouse gas
concentration is expected to increase, leading to an increase in temperature from 0.3oC to
0.7oC [1].
When the temperature increases, the amount of snow and ice has diminished, the sea
level has risen, and the occurrence of climate extreme events. The climate extreme eventswould cause enormous damage to nature and humans in the future. In this context, theclimate change scenarios, in which the relationships between human choices, emissions,concentrations, and temperature change were developed. The standard sets of scenarios
were used by the climate modelling community as input to global climate model
simulations [1]. In recent decades, the Representative Concentration Pathways in 2010
(RCPs) was used commonly in the scientific community to predict the change of climate
variables in the future.
The commonly recent set of scenarios, RCPs, builds on these two decades of scenario
development. RCPs are not emissions scenarios; they are radiative forcing scenarios.
Each scenario is tied to one value: the change in radiative forcing at the tropopause by
2100 relative to preindustrial levels. The four RCPs are numbered according to the change
in radiative forcing by 2100: +2.6, +4.5, +6.0, and +8.5 watts per square meter [1]. Table
2.1 shows the RCP scenarios description in more detail. Figure 2.1 shows the global mean
temperature anomalies (oF) relative to 1976-2005 for four RCP scenarios, 2.6 (green), 4.5(yellow), 6.0 (orange), and 8.5 (red). It can be seen that the future highest temperaturecorresponds to the highest future greenhouse gas emissions as indicated by red lines color
in Figure 2.1.

18

Chapter 2
Table 2.1. RCP scenarios description (Source: IPCC Fifth Assessment Report Summaryfor Policymakers)
Representative
concentration
pathway(RCP)

Forcingcompared to1750 (Wm-2)

Climate policy
CO2
asscociatedequivalent
with scenario(ppm)

Projected global
average temperature
increase from1986 – 2005 (oC)

2.6

2.6

Mitigation

475

1.0

4.5

4.5

Stabilization

630

1.8

6.0

6.0

Stabilization

800

2.2

8.5

8.5

None

1313

3.7

Figure 2.1. Global mean temperature anomalies (oF) relative to 1976-2005 for four RCP
scenarios, 2.6 (green), 4.5 (yellow), 6.0 (orange), and 8.5 (red). Each line represents an
individual sidạmulation from the CMIP5 archive. Every RCP-based simulation withannual or monthly temperature outputs available was used here. The values shown here
were calculated in 0.5oC increments; since not every simulation reaches the next 0.5oC
increment before the end of the century, many lines terminate before 2100 [2]19Chapter 2
In recent years, Global Climate Models or General Circulation Models (GCMs) are
the best way to project the possible future changes in climate variables at the Earthsystem’s level [3,4]. The total number of GCMs and the average horizontal spatial
resolution of the models have improved year by year, as computers become more
powerful [1]. There are many global climate models that are commonly used today toevaluate the climate variables for many regions in the world. For example, table 2.2 shows
the 18 GCMs provided by The Coupled Model Intercomparison Project Phase 5 (CMIP5)[5]. Li et al. (2015) used these 18 GCMs with two experiments: the historical simulation(1950-1999) and the future simulation RCP 4.5 scenario (2050-2099) to evaluate thethermodynamic and dynamic contributions to future changes in regional precipitation in
the Southeastern United States [5].

Table 2.2. 18 Global climate models (GCMs) [5]
No

Model

Ensemble members

Years

Historical

FutureHistorical

Future1

BCC-CSM1-1

3

1

50

50

2

CCSM4

6

6

50

50

3

CESM1-BGC

1

1

50

50

4

CESM1-WACCM

4

3

50

50

5

CNRM-CM5

10

1

50

50

6

CSIRO-Mk3.6.0

10

10

50

50

7

FGOALS-s2

3

2

50

50

8

GFDL-CM3

3

1

50

50

9

GFDL-ESM2G

3

1

50

50

10

GFDL-ESM2M

3

1

50

50

11

HadGEM2-CC

3

1

50

50

12

INM-CM4

1

1

50

50

13

IPSL-CM5A-LR

5

4

50

50

14

MIROC5

4

3

50

50

15

MIROC-ESM

3

1

50

50

16

MPI-ESM-LR

3

3

50

50

17

MRI-CGCM3

5

1

50

50

18

NorESM1-M

2

1

50

50

20

Chapter 2
Although, there are many global climate models are used in the world today, such as
18 GCMs shown in table 2.2; however, most have few ensemble members. A GCM
reproduces a single climate scenario or a few scenarios based on some ensemble members[6]. Therefore, with few ensemble numbers, it would not be sufficient to predict theclimate variables for extreme events of return periods 100 years or more [6,7].
Simulations with large ensemble members are necessary to reduce the uncertainty in theestimation of extreme events [6]. In addition, increasing ensemble size could improve theestimation of regional precipitation events [6,8]. Therefore, developing the climate
models with large ensemble numbers could address this issue.
In addition, using GCMs in evaluating and predicting future climate variables still
has limitations. Generally, GCMs has a coarse horizontal resolution; therefore, it is notsuitable for predicting the future climate variables at a regional scale or basin scale. In
addition, it is an inaccuracy of describing rainfall extremes, the direct use of their outputs
in impact studies on basin scales is also limited. There is often a clear deviation in thestatistics of variables generated by GCMs such as rainfall and temperature [4,9,10]. For
example, Rotmans et al. (1994) [11] indicated that the precipitation characteristics change
so much from region to region and locally within regions. Whereas, these precipitation
patterns could be obtained when the scale in the climate models is reduced. The spatial
resolution of typical GCMs (approximately 100-400 km) is too coarse to predict majorhydrological on basin scales [12]. Sun et al. (2006) [13] and Min et al. (2011) [14] also
indicated that low resolution of GCMs tends to prevent simulating realistic extreme
events, and high-resolution modeling is necessary to predict climate extremes underglobal warming. Most available GCMs are only suitable for predicting the hydrologicalimpact of climate change on relatively large watershed or global scales [15,16]. The
super-high-resolution model can reproduce actual climate conditions more precisely thanlower resolution models [12,17,18].

21

Chapter 2

Figure 2.2. Coarse data from Global Climate Models (GCMs) forms the initial andboundary conditions necessary for Regional Climate Models (RCMs) to dynamicallydownscale to much finer resolution climate data [19]GCMs generally have a coarse resolution for simulation on a regional scale; suchcoarse resolution could lead to large uncertainties in the simulation [20,21]. Therefore, tobridge the gaps between the climate model scales and the local scales, high-resolution
climate data obtained through downscaling approaches, such as statistical and dynamical
downscaling, have been investigated in recent years. For example, from a study by Fowler
et al (2007) [22], six Regional Climate Models (RCMs) with a horizontal resolution of 50
km under the SRES A2 emissions scenario were used to evaluate changes in extreme
precipitation over Europe by 2070-2110. The results indicated that RCMs could evaluate
to underestimate 1 day return values but reasonably simulate longer duration (5-10 day)extreme precipitation. In addition, all RCMs project increases in the magnitude of shortand long duration extreme precipitation for most of Europe. This study also showed thatboth the resolution and number of models in the ensemble will impact projections ofchange. In a study by Wichakul et al. (2015) [23], the authors showed that the flood riskin the near-future and far-future projection periods becomes higher in the Chao Phraya
River, Thailand using MRI-AGCM 3.2S with a high resolution of 20 km.

22Chapter 2
2.2. Overview of database for Policy Decision making for Future climate change(d4PDF)
Planning for adaptation to climate change impacts needs to based on impact
assessments of natural disasters, water resources, ecosystems, and so on in each region.
Therefore, the detailed projection of extreme events such as extreme rainfall, drought,
and strong wind is required at the regional and watershed scales [24]. Understanding theimportance of climate projection models in evaluating and predicting the change inclimate variables due to climate change impacts to minimize their damage, a largeensemble climate simulation database, which is known as the database for policy
decision-making for future climate changes (d4PDF) was developed in 2015 [24]. The
main purpose of the database is to become a standard for using in policy-making decisions
at Japanese ministries and agencies, as well as in climate change impacts assessment
studies [25].
The d4PDF consists of large-ensemble climate experiments from the global
atmospheric model with a horizontal resolution of 60 km (AGCM), and regional
downscaling simulations covering the Japan area, with a horizontal resolution of 20 km(RCM). Four sets of experiments are performed by the AGCM. The duration of eachexperiment is 60 years. Each set of experiments has 90-100 ensemble members. The
settings of experiments shown as below:


Historical climate simulation: 1951-2010, 100 members



Non-warming simulation: 1951-2010, 100 members



2K future climate simulation: 2031-2090, 54 members



4K future climate simulation: 2051-2110, 90 members.
The sea-surface temperature (SST), sea-ice concentration (SIC), and sea-ice thickness(SIT) are prescribed as the lower boundary conditions, and global-mean concentrationsof greenhouse gases and three-dimensional distributions of ozone and aerosols as theexternal forcing [24].

23Chapter 2

Figure 2.3. A schematic view of these research activities form an interdisciplinary
community on global warming research [25]The +4K future climate simulation was predicted for a global mean air temperature
4oC warmer than the pre-industrial period. The +4K future climate simulation
corresponded to the representative concentration pathway 8.5 (RCP 8.5) experiments
under phase 5 of Coupled Model Intercomparison Project Phase 5 (CMIP5). For the useof the +4K simulations, climatological SST warming patterns (△SSTs) are added to theobservational SST after removing the long-term trend component. Six CMIP5 models
were selected, and 15 member ensemble experiments are conducted for each of the six△SSTs, giving a total of 90 members.

24

Chapter 2

Figure 2.4. Description of d4PDF (Source: d4PDF English, database for Policy Decision
makingfor
Futureclimatechange)(http://www.mirocgcm.jp/~pub/d4PDF/design_en.html)
Currently, evaluating the change in climate variables, especially extreme events inregional scale or basin scale, the regions were affected by local topography, the lowresolution models are not suitable for simulation [22,24]. In particular in Asia, where themonsoon and tropical cyclones are major causes of natural hazards and water resources;
therefore, changes in these phenomena are the key aspects for regional climate change
risk assessments. To address these problems, the RCM downscaling simulations aredeveloped by the Meteorological Research Institute Nonhydrostatic Regional Climate
Model (NHRCM). The RCM simulations cover Japan, the Korean Peninsula, and theeastern part of the Asian continent [24].
The dynamical downscaling simulations by the RCM are conducted from AGCM.
Using the RCM results is recommended for analyses around the Japan region. Three setsof experiments are performed by the RCM, as shown below:


Historical climate simulation: 1951-2010, 50 members

+2K future climate simulation: 2030-2091, 54 members

+4K future climate simulation: 2051-2110, 90 members
25

Chapter 2

Figure 2.5. Experimental design of d4PDF (Source: Ministry of education, culture, sports,science, and technology et al, the guidance of using d4PDF, 2015.12, http://www.mirocgcm.jp/~pub/d4PDF/design.htmn (Only Japanese available)
The RCM was used in several studies; for example, Miyasaki et al. (2020) [26] used
the Non-Hydrostatic RCM (NHRCM) with a 20 km horizontal resolution [27] toinvestigate heavy precipitation events exceeding the 100 year return period in the Kanto
area and future projections of such events. In another study by Harada et al. (2020) [28],the authors conducted to evaluate climate change impact on flood risk in the Nagara Riverbasin using d4PDF NHRCM with a horizontal resolution of 20 km. The results showedthat the frequency distribution of the calculation result of the peak flood discharge wasoverestimated by about 10% from the frequency distribution of the observed values.
Moreover, the distribution of the annual maximum discharge was well reproduced.
In addition, finer-scale projections have been performed over the Japanese region by
downscaling from RCM to a super high-resolution of 5 km [24,29,30,31]. The highresolution large ensemble results could be used to evaluate the statistical change in veryrare precipitation events [24]. In a study by Yamada et al. (2018) [31], the super highresolution 5 km d4PDF dataset was downscaled from the 20 km resolution data via a nonhydrostatic regional climate model [32]. After downscaling, the rainfall amount andspatiotemporal distribution of rainfall were similar to the observed rainfall events.
Furthermore, heavy rainfall patterns could be better observed after downscaling, and the
5 km resolution results represented the topography of the study area more precisely.

26

Chapter 2
2.3. Summary
Extreme weather events associated with climate changes have rapidly become one of
the global concerns threatening natural environments and human life in recent decades.
Therefore, future climate projection models have been developed under various climatechange scenarios. A large-ensemble climate simulation database, which is known as the
database for policy decision-making for future climate changes (d4PDF), was developed
for climate change impacts adaptations and assessments. The database was established in2015, and it has been growing continuously [25]. It contains the results of large ensemble
simulations conducted over a total of thousands of years for historical and future climate
simulations using a global atmospheric model with a horizontal resolution of 60 km(AGCM) and a regional climate model with a horizontal resolution of 20 km (RCM).
Several sets of future climate simulations are available, in which the global mean surface
air temperature becomes +4K, +2K warmer than the preindustrial climate, corresponding
to the representative concentration pathway 8.5 (RCP8.5) scenario of CMIP5. The sets ofexperiments conducted by the AGCM and RCM are presented in the above contents. The
advent of the d4PDF database is aimed to become a standard for using in devising policies
at Japanese ministries and agencies, as well as applied in the climate change impact
assessments studies. More than 70 papers using d4PDF have been published since 2015.
The database d4PDF with the high resolution and large ensemble climate simulations are
effective for detecting signals of extreme weather events and for practical applications toinvestigations of future climate changes. The database d4PDF have been performed invarious research areas such as natural hazard, hydrology, agriculture, and health.
Furthermore, it has helped in establishing an interdisciplinary research community on
global climate change across Japan [25].
In addition, Japan often suffered human loss and property damage from short-term
extreme rainfall due to Baiu front and typhoons. The short-term extreme rainfall can cause
severe flood inundation in Japan. Therefore, the super high-resolution of 5 km wasdownscaled from the NHRCM with a horizontal resolution of 20 km [24,29,30,31]. The
regional climate model with a resolution of 5 km can reproduce local extreme rainfall
events realistically [33]. As a result, there are several studies have been used the d4PDF
with the super high-resolution of 5 km for climate change impact assessments for27

Chapter 2
watershed scales in Japan. The studies have shown good applicability of d4PDF with highresolution in detecting extreme rainfall events [31,33]. Overall, d4PDF has become an
essential tool for climate change adaptation to mitigate the loss of life and property
damage caused by extreme weather events, especially in Japan in recent years.
2.4. References
1. Hayhoe, K.; Edmonds, J.; Kopp, RE; LeGrande, AN.; Sanderson, BM.; Wehner, MF.;
Wuebbles, J. Climate Models, Scenarios, and Projections. Climate Science Special
Report: Fourth National Climate Assessment. 2017. I, 133-160.
2. Swain, S and Hayhoe, K. 2015: CMIP5 projected changes in spring and summer
drought and wet conditions over North America. Climate Dynamics. 2015, 44, 27372750. Doi: 10.1007/s00382-014- 2255-9
3. Huntingford, C., Hugo Lambert, F., Gash, JHC., Taylor, CM., Challinor, AJ. Aspects
of climate change prediction relevant to crop productivity. Philosophical Transactionsof the Royal Society B: Biological Sciences. 2005, 360: 1999-2009.
4. Rana, A. Climate change effects on urban rainfall and management of urban flooding.
Doctoral thesis, 2013.
5. Li, L and Li, W. Thermodynamic and dynamic contributions to future changes inregional precipitation variance: focus on the Southeastern United States. Climate
Dynamics. 2015, 45, 67-82. DOI: 10.1007/s00382-014-2216-3
6. Try, S.; Tanaka, S.; Tanaka, K.; Sayama, T.; Hu, M.; Sok, T.; Oeurng, C. Projection of
extreme flood inundation in the Mekong River basin under 4 K increasing scenario
using large ensemble climate data. Hydrol. Process. 2020, 34, 4350–4364. DOI:
10.1002/hyp.13859
7. Tanaka, T.; Tachikawa, Y.; Ichikawa, Y.; Yorozu, K. Flood risk curve developmentwith probabilistic rainfall modelling and large ensemble climate simulation data: acase study for the Yodo River basin. Hydrological Research Letters, 12, 28-33. DOI:
10.3178/hrl.12.28
8. Duan, W., Hanasaki, N., Shiogama, H., Chen, Y., Zou, S., Nover, D., Zhou, B., &Wang, Y. Evaluation and Future Projection of Chinese Precipitation Extremes Using
Large Ensemble High-Resolution Climate Simulations. Journal of Climate. 2019, 32,
2169-2183. DOI: 10.1175/JCLI-D-18-0465.1
9. Kay, AL.; Reynard, NS.; Jones, RG. RCM rainfall for UK flood frequency estimation.
I. Method and validation. J. Hydrol. 2006, 318, 151-162.
28

Chapter 2
10. Kotlarski, S. Regional climate model simulations as input for hydrologicalapplications: evaluation of uncertainties. Advances in geosciences. 2005, 5, 119.
11. Rotmans, J.; Hulme, M.; Downing, TE. Climate change implications for Europe: An
application of the ESCAPE model. Global Environmental Change. 1994, 4, 97-124.
12. Sato, Y.; Kojiri, T.; Michihiro, Y.; Suzuki, Y.; Nakakita, E. Assessment of climatechange impacts on river discharge in Japan using the super-high-resolution MRIAGCM. Hydrological Processes. 2013, 27, 3264-3279. DOI: 10.1002/hyp.9828
13. Sun, Y.; Solomon, S.; Dai, A.; Portmann, RW. How often does it rain? Journal ofclimate. 2006, 19, 916-934. DOI: 10.1175/JCLI3672.1
14. Min, SK., Zhang, X., Zwiers, FW., Hegerl, GC. Human contribution to more- intense
precipitation extremes. Nature. 2011, 470, 378-381. DOI: 10.1038/nature09763
15. Vörösmarty, CJ., Green, P.; Salisbury, J.; and Lammers RB. Global water resources:
Vulnerability from climate change and population growth. Science. 2000, 289, 284288. DOI: 10.1126/science.289.5477.284
16. Hirabayashi, Y., Kanae, S.; Emori, S.; Oki, T.; and Kimoto, M. Global projections ofchanging risks of floods and droughts in a changing climate. Hydrological Sciences
Journal. 2008, 53, 754-772. DOI: 10.1623/hysj.53.4.754
17. Kitoh, A., Yatagai, A.; Alpert, P. First super-high- resolution model projection thatthe ancient “Fertile Crescent” will disappear in this century. Hydrological Research
Letters. 2008, 2, 1-4. DOI: 10.3178/hrl.2.1
18. Kusunoki, S., Mizuta, R.; Matsueda, M. Future changes in the East Asian rain bandprojected by global atmospheric models with 20-km and 60-km grid size. Climate
Dynamics. 2011, 37, 2481-2493. DOI: 10.1007/s00382-011 -1000-x
19. Giorgi, F. Regionalization of climate change information for impact assessment andadaptation. WMO Bulletin. 2008, 57, 86-92.
20. Try, S.; Tanaka, S.; Tanaka, K.; Sayama, T.; Lee, G.; Oeurng, C. Assessing the effects
of climate change on flood inundation in the lower Mekong Basin using highresolution AGCM outputs. Progress in Earth and Planetary Science. 2020, 7, 34.
DOI: 10.1186/s40645-020-00353-z
21. Li, G.; Zhang, X.; Zwiers, F.; Wen, QH. Quantification of uncertainty in highresolution temperature scenarios for North America. Journal of climate. 2012, 25,
3373–3389. DOI: 10.1175/JCLI-D-11-00217.1
22. Fowler, HJ., Ekström, M., Blenkinsop, S., Smith, AP. Estimating change in extreme
European precipitation using a multimodel ensemble. Journal of GeophysicalResearch. 2007, 112. DOI: 10.1029/2007JD008619
29

Chapter 2
23. Wichakul, S.; Tachikawa, Y.; Shiiba, M.; Yorozu, K. River discharge assessment
under a changing climate in the Chao Phraya River Thailand by using MRIAGCM3.2S. Hydrological Research Letters. 2015, 9, 84-89. DOI: 10.3178/hrl.9.84
24. Mizuta, R.; Murata, A.; Ishii, M.; Shiogama, H.; Hibino, K.; Mori, N.; Arakawa, O.;
Imada, Y.; Yoshida, K.; Aoyagi, T.; et al. Over 5000 years of ensemble future climate
simulations by 60-km global and 20-km regional atmospheric models. Bull. Am.
Meteorol. Soc. 2017, 98, 1383–1398. DOI: 10.1175/BAMS-D-16-0099.1
25. Ishii, M.; Mori, N. 4PDF: large-ensemble and high-resolution climate simulations for
global warming risk assessment. Progress in Earth and Planetary Science. 2020, 7,
28. DOI: 10.1186/s40645-020-00367-7
26. Miyasaka, T.; Kawase, H.; Nakaegawa, T.; Imada, Y.; Takayabu, I. Future projectionsof heavy precipitation in Kanto and associated weather patterns using large ensemble
high-resolution simulations. SOLA. 2020, 16, 125-131. DOI: 10.2151/sola.2020-022
27. Sasaki, H., K. Kurihara, I. Takayabu, and T. Uchiyama, 2008: Preliminary
experiments of reproducing the present climate using the non-hydrostatic regional
climate model. SOLA. 2008, 4, 25−28, DOI:102151/sola.2008-007
28. Harada, M.; Maruya, Y.; Kojima, T.; Matsuoka, D.; Nakagawa, Y.; Kawahara, S.;
Araki, F. Flood frequency analysis and impact assessment for climate change in the
Nagara river basin. Journal of Japan Society of Civil Engineers, ser B1. 2020, 74, 7986.
29. Kanada, S., Nakano, M.; Kato, T. Projections of future changes in precipitation andthe vertical structure of the frontal zone during the Baiu season in the vicinity of Japan
using a 5-km-mesh regional climate model. J. Meteor. Soc. Japan. 2012, 90A, 65–86.
Doi:10.2151/jmsj.2012-A03
30. Nakano, M., Matsueda, M.; Sugi, M. Future projections of heat waves around Japansimulated by CMIP3 and high-resolution Meteorological Research Instituteatmospheric climate models. J. Geophys. Res. Oceans. 2013, 118, 3097-3109.
Doi:10.1002/jgrc.20375
31. Yamada, T.J.; Hoshino, T.; Masuya, S.; Uemura, F.; Yoshida, T.; Omura, N.;
Yamamoto, T.; Chiba, M.; Tomura, S.; Tokioka, S.; et al. The influence of climatechange on flood risk in Hokkaido. Adv. River Eng. 2018, 24, 391–396. (In Japanese).
32. Sasaki, H.; Murada, A.; Hanafusa, M.; Oh’izumi, M.; Kurihara, K. Reproducibility ofpresent climate in a non-hydrostatic regional climate model nested within an
atmosphere general
10.2151/sola.2011-044

circulation

model. SOLA. 2011, 7,

173–176.

DOI:

30

Chapter 2
33. Kanada, S.; Nakano, M.; Hayashi, S.; Kato, T.; Nakamura, M.; Kurihara, K.; Kitoh
A. Reproducibility of maximum daily precipitation amount over Japan by a highresolution non-hydrostatic model. Scientific Online Letters on the Atmosphere. 2008.
4, 105‒108. DOI:10.2151/SOLA.2008- 027

31

Chapter 3
Assessing climate change impacts on
extreme rainfall and severe flooding in the
Ishikari River basin, Japan

Chapter 3
3.1.Introduction
The issues of climate change and its impacts on human life and natural environments
are the most important challenges for scientists in this century [1]. According to the
Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on
Climate Change (IPCC), severe natural disasters due to extreme climate have become
more frequent since 2000 [2]. Approximately 80-90% of all recorded disasters fromnatural hazards during the past 10 years have caused by floods, droughts, tropical
cyclones, heatwaves, and severe storms [3]. Among them, floods are the most frequent
type of natural disaster on earth, causing enormous damage all over the world. Floods are
considered as extreme weather events that occur frequently and cause severe damage [4,5].
Although several factors contribute to flooding, heavy or prolonged rainfall is considered
the most critical factor that causes floods. Based on global climate model (GCM)
simulations of the Coupled Model Intercomparison Project Phase 3 (CMIP3), it isevaluated that the frequency of heavy rainfall or the total rainfall amount from heavy
rainfall is projected to increase in the 21st century over many regions in the world [2].
Moreover, the same assessment in The IPCC 5th Assessment Report [6], indicated thatrainfall is expected to increase in Asia during future summer monsoon seasons, andextreme rainfall is likely to become more frequent. In the context of future global
warming, the change rate of heavy rainfall amounts will increase more than that of annual
mean rainfall [7,8,9]. Increasing rainfall, especially extreme rainfall, has enhanced therisk of floods in the future [10,11]. According to the World Health Organization (WHO)[3], floods can cause widespread devastation, and it affected more than 2 billion people
worldwide from 1998 to 2017. People who live in floodplain areas, or non-resistantbuildings, or lack warning early flood systems are the most affected by flooding. Flooddisasters are becoming more frequent and higher intensity in the future [3].
In this context, future projections of extreme rainfall, and its impact on severe floodrisk should be investigated for different regions in the world for the purpose of flood
damage mitigation. In recent years, many researchers have attempted to predict extreme
rainfall as well as severe floods for many river basins in the world to minimize flood
damage [12,13, 14,15,16]. For example, in a study by Try et al. (2020) [12], the authors
assessed the impacts of climate change on river flow in the Mekong River basin and flood
32

Chapter 3
inundation in the Lower Mekong basin using the Rainfall-Runoff-Inundation (RRI)
model. The results indicated that flood magnitude in the Lower Mekong Basin will beseverer by the end of the twenty-first century. The impact of climate change on floodinundation in a tropical river basin in Indonesia was also evaluated in a study by
Yamamoto et al. (2021) [16]. The results showed that flooding will increase in this region.
In particular, the flood inundation volume corresponding to a 20 year return period will
increase by 3.3 times.
In Japan, extreme flood events from extreme rainfall have been recorded regularly inrecent years. Japan often suffered enormous damage from short-term heavy rainfall [17].
For example, large-scale flooding due to heavy rainfall occurred during July 5-8, 2018 inwestern Japan, causing extensive damage over numerous prefectures and resulting in 224
deaths, 21,460 collapsed houses and 30,439 inundated houses. In the Oda River and its
three tributaries, levees were breached at eight points due to the “backwater phenomenon”
in which the tributary river floods synchronized with the main river flood [18].
Considering various climate change scenarios, several studies have predicted increased
rainfall in the future [17,19]. Kim et al. (2010) [19] indicated that rainfall in Hokkaido isexpected to increase by 6.1% and 10.6% in the near and extended future, respectively.
Additionally, Yamada (2019) [20] reported that extreme rainfall will be more extensive
under future climate conditions.
Attempts have been made to develop flood adaptation strategies to address the criticaleffects of climate change on the risk of river floods. Several studies have been conducted
for the Japanese river basins [21,22,23]. For example, a study by Tachikawa et al. (2009)[22] indicated that severe rainfall would increase in the Yoshido River basin, and the peakflood discharge would increase to a greater extent in the future. Additionally, the trend of
extreme rainfall events increasing in a short period of time should be considered. Sato etal. (2012) [21] indicated that climate change is projected to change river discharges
significantly, especially in northern Japan.
Therefore, understanding the change in the amount of rainfall, especially considering
future extreme rainfall events, and assessing its effect on the risk of river floods invulnerable basins is necessary to create effective flood control plans. This study
investigates the changes in the risk of river flooding associated with climate change in the33

Chapter 3
Ishikari River basin, a socioeconomically important basin in Hokkaido, Japan. This study
is the first to assess the changes in extreme short-term rainfall and extreme river flooding
events during the summer monsoon season in the Ishikari River basin as well as in its
main sub-basins using an Integrated Flood Analysis System (IFAS) coupled with a largeensemble rainfall dataset (d4PDF) with a high resolution of 5 km [24]. Additionally, thetime differences between the time of peak discharge at the reference stations in each
tributary and the time of peak water level at the confluence points in the main river are
evaluated. The shorter the time difference, the greater the flood risk. These results will
provide additional information about the effect of climate change on the risk of riverfloods, and thus guide climate change adaptation and flood damage mitigation strategies
in vulnerable areas.
3.2. Methodology
3.2.1. Overview of the Ishikari River basin
The study area is the Ishikari River basin, which is located in Hokkaido, Japan.
(Figure 3.1). The Ishikari River originates from Mt. Ishikari-dake (1,967 m above sea
level) of the Taisetsuzan mountain range, flows through the west of Hokkaido, and thenempties into the Ishikari Bay. The river flows through 48 municipalities (including
Sapporo, the prefectural capital, which is located in the lower reaches of the river, andAsahikawa, the second-largest city, is located in the upstream region), accounting forroughly 52% of Hokkaido’s population. At 268 km in length and with a drainage area of
14,330 km2, the river is the longest river in Hokkaido and the second largest in terms of
the basin area in Japan. This river basin plays an important role in socioeconomic in
Hokkaido. The Ishikari plain located around the central and lower regions of the Ishikari
River basin is the most productive agricultural area in Hokkaido. In addition, the Ishikari
River is an important breeding ground for salmon, and salmon has been artificially
hatched along the river for many years [25].
Playing an important role in economics and society in Hokkaido; however, theIshikari River basin is frequently affected by severe floods, causing massive damage
across this basin. For example, the largest flood event in this basin occurred in August
1981 and caused damages approximately JPY 115.2 billion, two fatalities, and 22,500
34

Chapter 3
inundated houses entire Ishikari River basin. Recently, the flood in August 2016 caused
damage of approximately 260 million USD and agricultural losses on 40,258 ha of land[26]. Therefore, the projection of flood risk is significant to reduce future flood damage
in this basin. The Ishikari River basin includes seven main sub-basin: the Toyohira,
Chitose, Yubari, Ikushunbetsu, Sorachi, Uryu, and Chubetsu River basins. Figure 3.1
shows the locations of the Ishikari River basin and its main sub-basins. Table 3.1 shows
the characteristics of the Ishikari River basin and its main sub-basins and referencehydrological stations in each sub-basin.

44o0’0”NRiver basins

142o0’0”E

Total
Area(km2)

Length(km)

Ishikari

14,330

268

Uryu

1,722

177

Chubetsu

1,063

59

Sorachi

2,618

194

Ikushunbetsu

343

59

Yubari

1,417

136

Chitose

1,244

108

Toyohira902

72

143o0’0”E

Japan map

Hokkaido island

Uryubashi

Akatsukibashi

44o0’0”N

141o0’0”E

AkabiraLake Shikotsu
43o0’0”N

Reference station
Kariki

Confluence point

43o0’0”N

NishikawamukaiRiver channel

Kiyohorobashi
Uranosawa

Ishikari Ohashi station
Ishikari river basin
141o0’0”E

142o0’0”E

143o0’0”E

Figure 3.1. Ishikari River basin, Hokkaido, Japan and its main sub-basins

35

Chapter 3
Table 3.1. Characteristics of the Ishikari River basin and its main sub-basins and referencehydrological stations in each sub-basin
River basin

Total Area

Length (km)Hydrological station(km2)
Ishikari

14,330268

Ishikari Ohashi station

Toyohira

902

72

Kariki

Chitose

1,244

108

Uranosawa

Yubari1,417

136

Kiyohorobashi

Ikushunbetsu

343

59

Nishikawamukai

Sorachi

2,618

194

Akabira

Chubetsu

1,063

59

Akatsukibashi

Uryu

1,722

177

Uryubashi

3.2.2. d4PDF dataset
In this study, the climate model used for the experiment was the large-ensemble
climate simulations, which is known as the database for policy decision-making for futureclimate changes (d4PDF). The d4PDF was developed in 2015 for the main purpose is tobecome a standard in climate change risk assessments, thereby helping policymakers andmanagers in devising policies to minimize the damage caused by weather extreme eventsin Japan. In general, a large-ensemble database d4PDF was developed to assess futurechanges in extreme climate events occurring rarely, such as extreme rainfall and tropical
cyclones [27]. The d4PDF consists data from numerous ensemble climate experimentswith 60-km resolution on the global scale [28]. However, the low resolution is not suitableto evaluate the climate variables, especially extreme events in regional scale or basin scale,the regions were affected by local topography [28,29]. To address these problems, theRegional Climate Model (RCM) downscaling simulations are designed by theMeteorological Research Institute Non-hydrostatic Regional Climate Model (NHRCM).
The RCM simulation covers Japan, the Korean Peninsula, and the eastern part of the
Asian continent [28]. The RCM has a 20-km horizontal resolution. Three sets ofexperiments are performed by the RCM, as shown below:

36Chapter 3Historical climate simulation: 1951-2010, 50 members

+2K future climate simulation: 2030-2091, 54 members

+4K future climate simulation: 2051-2110, 90 members
The dataset spans 60 years (1951-2010) and 50 members (total: 3,000 events) for thehistorical climate simulation, and 60 years (2051-2110) and comprises 90 members (total:
5,400 events) for the +4K future climate simulation. The +4K future climate in which theglobal mean surface air temperature becomes 4oC warmer than the preindustrial climate,corresponding to that around the end of the twenty-first century under the representative
concentration pathway 8.5 scenarios of CMIP5 [28].
This study used the d4PDF downscaled rainfall data with 5-km super high-resolution
from a previous study [24]. The 5-km resolution rainfall data were downscaled from 20km resolution data via the non-hydrostatic regional climate model [30]. The target period
for downscaling was set to 15 days of maximum rainfall for each event in Hokkaido for
3,000 historical simulation events and 5,400 future simulation events [24,31]. After
downscaling, the rainfall amount, hourly rainfall intensity, and spatiotemporal
distributions of rainfall were similar to those of the recorded rainfall events (Figure3.2).
Additionally, the downscaled results can represent the topography of the study area more
precisely [24]. These results suggest that the dataset after downscaling can be used toassess the impacts of climate change on a regional scale and basin scale.

37

Chapter 3

Figure 3.2. +4K future climate simulation for the Tokachi River basin at Obihiro point
station with the rainfall data of 20-km resolution (left) and 5-km resolution (right) [24]In this study, we selected the rainfall data for locations within the Ishikari River basin.
Then, we chose the annual maximum rainfall (mm/72 h) for evaluating the change inshort-term extreme rainfall and its impact on river flooding between the historical andfuture simulations in the Ishikari River basin. According to the report of Japan River
Association, (2003) [25], the degree of safety for the Ishikari River in Hokkaido was set
as 1/150, giving a return period of 150 years. Therefore, this study focused on assessing
the severe river flooding events for the top 20 and the top 36 rainfall events out of 3,000
and 5,400 rainfall events corresponding to return period equal to or more than 150 years
for the historical and future simulations, respectively.
3.2.3. Overview of the Integrated Flood Analysis System (IFAS) model
Integrated Flood Analysis System (IFAS) model is developed by the International
Centre for Water Hazard and Risk Management (ICHARM). IFAS is a concise tool kit
with a Graphic User Interface for building Distributed Rainfall-Runoff analysis model.
The IFAS uses a Public Works Research Institute (PWRI) – distributed hydrologicalmodel developed in the 1990s [32] as the runoff simulation engine [33].
38Chapter 3
There are great expectations for flood forecasting and early flood warning systems to
mitigate flood damage, especially in insufficiently gauged river basins. However, there
are several challenges when developing a rainfall-runoff system for these purposes asfollows:

It requires high-level knowledge of civil engineering and computer skills to model a
Hydrological modelIt requires a significant budgetBecause of the lack of long-term historical hydrological data, it is difficult to project
the river discharge.
As a result, the IFAS model was designed with user-friendly graphic interfaces fordata input, data output, model building modules, parameters setting functions, evaluating
flood risk can be possible, even in poorly gauged river basins [33]Figure 3.3. Functions of IFAS model [33]39Chapter 3
IFAS model has features as below [33]:

The outflow from each cell is calculated by non-linear relationships based on the tankmodel philosophy.

Parameters can be estimated by using grid-based global data sets on topography, soil,
geology, land use.

The storage function runoff model enhances flood reproducibility by modifying
saturation rainfall for each flood event. However, it is difficult to forecast saturation
rainfall before the flood. PWRI-DHM adopts a nonlinear 2 or 3 layer tank structure.
This can solve the problem to modify the parameters for each flood event. As a result,
this model does not need to estimate saturation rainfall of future flood events, andthis is best used as a flood forecasting model.



If an actual flood event is reproduced by the storage function method, its
reproducibility of floods is not enough in medium/ small size floods in general. Thisis because the storage function method is a non-linearly 1 layer tank model and it isdifficult to reproduce both heavy and medium/small size floods by the same
parameters since the characteristics of the runoff phenomenon are different from eachother. In PWRI Distributed hydrological model enhances its reproducibility tomedium/small size floods by adopting a nonlinear 2 or 3 layer tanks structure.

For numerical calculation, PWRI-DHM does not use convergence calculation tosolve the differential equation. It uses approximation functions to solve the time
integral equation. For this reason, the system can conduct numerical calculations
smoothly and realize for real-time operation.

To calculate discharge in the river course tank, PWRI-DHM solves the Kinematic
Wave equation. Moreover, PWRI-DHM Ver 2 adopts 2 layer tanks vertically.
The concept of the IFAS model shown in the table below:

40

Chapter 3
Table 3.2. Model configuration [33]ModelFunctionsSurface tank model

Infiltration to unsaturated layer, surface
runoff, surface storage,
evapotranspiration, rapid

Unsaturated tank model

Infiltration to aquifer, subsurface runoff,subsurface storage, low intermediate
outflow

Aquifer tank model

Outflow from aquifer, aquifer loss

River tank modelRiver course discharge(A)

RainfallSurface tankhGroundwater flow

1

Surface flow:

2

Subsurface flow:

1

2(h-Sf1)/(Sf2-Sf1)
3 Infiltration:3

RainfallAquifer tankRiver tank

Surface tank(h-Sfo)/(Sf2-Sfo)
4

4

Runoff coefficient of
unconfined groundwater:A

River tankh

Aquifer tank5
5

Runoff coefficient ofconfined groundwater:(A)
L: length of the cell (m), N: roughness coefficient of surface (m-1/3s), h: height of stored water (m), Sf2: maximum
storage height (m), i: gradient of slope,

regulation coefficient for rapid intermediate flow, A: area of the cell (m2),

fo: final infiltration capacity (cm/s), Sf1: rapid intermediate flow (m), Sfo: height where ground infiltration occurs (m),Au: runoff coefficient of slow intermediate outflow (1/mm/day)1/2, Sg: height where slow intermediate outflow occurs(m), Ag: coefficient of base outflow (1/day) (ICHARM, 2014)

Figure 3.4. Schematic of the IFAS model [33]IFAS is a GUI for building a distributed rainfall-runoff analysis model for any river
basin. IFAS was designed as a concise tool kit for building a Rainfall-Runoff analysis
model able to perform the following functions:41

Chapter 3(1) Import of geospatial data according to cell size and coordinate set function(2) Edition of rainfall data set function(3) Parameter setting function(4) Execution of Rainfall-Runoff analysis function(5) Output result function
The IFAS model can reproduce rainfall distribution and water gathering process on
the surface and underground into river course. The hydrological model does not have ananalysis function to reproduce overtopping from river course and inundation. Therefore,
the distributed tank functioned hydrological model is applicable for Rainfall-Runoff
analysis until overtopping of river flows.

Figure 3.5. Applicable/Outside the scope Phenomenon for IFAS [33]The IFAS model has been used to estimate the flood risk for many river basins
globally and has demonstrated good simulation performance [34,35,36,37]. For example,
in the study by Kimura et al. (2018) [35], the impact of the typhoons on floods andchanges in future floods in the Tokachi River basin, located in eastern Hokkaido using
the IFAS model. The results of the simulation with a simple assumed future climate (withhigher precipitation) indicated that future river floods are expected to increase with anincrease in precipitation in the Tokachi River basin. In another study, an attempt has been
made to develop the nonstructural countermeasures for the downstream reach of the Indus
River, Pakistan to mitigate the flood damage. The authors concluded that the IFAS modelis practically helpful for the flood early warning and to save the lives and properties
damages [34]42Chapter 3
The flowchart of the procedure for flood simulation by the IFAS model is shown in
Figure 3.6 below:

Create project and Set up(a target area and a period)
Import GIS-based data
(Elevation, Land Use)

Create a basin shape, river channel networkand sub-basins
Input rainfall data

Set up parameters(surface, aquifer and river tanks)
Check model inputs and parameters andconduct simulation
Show results (hydrograph)
Figure 3.6. Flowchart of the procedure for flood simulation by the IFAS model
3.2.4. Data inputs
For hydrological simulation, the required IFAS input data are elevation, land-use, andrainfall data. The elevation data were obtained from a global digital elevation model(GTOPO30). GTOPO30 is a global data set covering the full extent of latitude from 90
degrees South to 90 degrees North, and the full extent of longitude from 180 degrees Westto 180 degrees East. The horizontal grid is 30 arc-second (approximately 1 km). In
addition, the land-use data with 30 arc-second (approximately 1 km) resolution is Global
Land Cover Characterization (GLCC). GLCC was provided by the U.S. Geological
Survey (USGS). The elevation data and the land-use data were shown in Figure 3.7.
43Chapter 3
In this study, the flood event in August 1981 was used to calibrate the hydrologicalmodel. The historical flood events in September 2001, September 2011, and August 2016
were chosen to validate the model. These four extreme flood events were large-scale
historical flood events that caused severe damage in the Ishikari River basin. In particular,
the flood event in August 1981 was the largest flood event observed in the target basin.
The rainfall data for the August 1981 flood were obtained from rain gauge stations
provided by the Hokkaido Regional Development Bureau. The rainfall data for the
September 2001, September 2011, and August 2016 flood events, and the observed riverdischarges for the calibration and validation processes were obtained from the Ministryof Land, Infrastructure and Transport, Japan (MLIT) [38]. Additionally, the observed
annual maximum rainfall (mm/ 72h) (1926-2018) for the Ishikari River basin wasprovided by the Sapporo Development and Construction Department, MLIT.

Elevation data

Land-use data

Figure 3.7. Elevation data and Land-use data
3.2.5. Analytical procedure
In this chapter, we assessed the effect of climate change on extreme rainfall andsevere river flooding in the Ishikari River basin using the following steps. First, the IFAS
model was calibrated and validated against historical flood events. Next, future changes
in extreme rainfall were estimated based on a large ensemble rainfall dataset d4PDF witha super high-resolution of 5 km. The Student’s t-test was used to find a significant
44

Chapter 3
difference between the two sets of samples (observed rainfall data and rainfall dataobtained from the historical simulation). The hypothesis of the test is stated as follows.
Ho: 𝜇1 = 𝜇2
H1: 𝜇1 ≠ 𝜇2
Here, 𝜇1 and 𝜇2 are the means of the observed rainfall data and rainfall dataobtained from the historical simulation, respectively. The null hypothesis is rejected if thep-value is less than the significance level α of 0.05. Additionally, the Student’s t-test wasused to find the significant difference between the two sets of samples (historical andfuture simulations); the hypothesis of this test is stated as follows.
Ho: 𝜇2 ≤ 𝜇1
H1: 𝜇2 > 𝜇1
Here, 𝜇1 and 𝜇2 are the means of annual maximum rainfall (mm/ 72h) in thehistorical and future simulations, respectively. The null hypothesis is rejected if the pvalue is less than the significance level α of 0.05.
Then, future changes in extreme rainfall between the top 20 and top 36 rainfall eventswere evaluated for the Ishikari River basin and in its main sub-basins. After that, the IFAS
model was calibrated using the flood event in August 1981. The flood events in September
2001, September 2011, and August 2016 were chosen to validate the model. The
simulations were conducted for an additional period of two weeks prior to the main periodof flooding events for model warmup and to allow time for the water to reach thedownstream area. We calculated the river discharge at the Ishikari Ohashi station, which
is approximately 26.6 km upstream from the river mouth (Figure 3.1).
To quantitatively evaluate the performance of the IFAS for the historical flood events,
we used the Nash-Sutcliffe coefficient (NSE) [39] and the three indices of wave shapeerror (Ew), volume error (Ev), and peak discharge error (Ep) [34,40], defined as follows.

45

Chapter 3

𝑁𝑆𝐸 = 1 −∑𝑛𝑖=1[𝑄𝑀(𝑖) − 𝑄𝐶(𝑖) ]2∑𝑛𝑖=1[𝑄𝑀(𝑖) − 𝑄𝐴𝑉𝐺(𝑖) ]𝑛2

𝑄𝑀(𝑖) − 𝑄𝐶(𝑖)
1
𝐸𝑤 =
∑[
]
𝑛
𝑄𝑀(𝑖)

(1)
2

(2)

𝑖=1

∑𝑛𝑖=1 𝑄𝑀(𝑖) − ∑𝑛𝑖=1 𝑄𝐶(𝑖)𝐸𝑣 =∑𝑛𝑖=1 𝑄𝑀(𝑖)𝐸𝑝 =𝑄𝑀𝑃 − 𝑄𝐶𝑃
𝑄𝑀𝑃

(3)
(4)

Here, QM: observed discharge (m3/s); Qc: simulated discharge (m3/s); n: number of data
points; QAVG: average observed discharge (m3/s); QMP: peak value of the observed
discharge (m3/s); and QCP: peak value of the simulated discharge (m3/s). The simulation
model is acceptable if NS > 0.7; and the smaller the Ew, Ev, and Ep errors are, the betterthe model is.
After validation, the IFAS model was used to estimate the river discharge at 8
reference stations located in the Ishikari River basin, in its main sub-basins, and 7
confluence points (Figure 3.1) for the top 20 and top 36 rainfall events. Changes inextreme river flooding between the top 20 and top 36 rainfall events in the Ishikari Riverbasin and in its main sub-basins were investigated. Finally, the differences between thetime of peak discharge at the reference station in each tributary and the time of peak waterlevel at the confluence point in the main river were evaluated. The average values of thetime differences over all the flood events in each of the sub-basins were estimated andcompared between the top 20 and top 36 rainfall events.
3.3. Results and discussion
3.3.1. Hydrological model calibration and validation
Table 3.3 shows the parameter value settings in the IFAS model after calibration andvalidation.

46

Chapter 3
Table 3.3. Parameter value settings in the IFAS model
Surface tank parameters

Parameter

Final infiltration
capacity (fo, cm/s)

Maximum storage
height (Sf2, m)

1
2
3
4
5

0.00010.00001
0.00001
0.00001
0.000001

0.1
0.05
0.05
0.001
0.05

Parameter

Regulation coefficient of slow
intermediate outflow(Au, (1/mm/day)1/2)

1
2
3
4

0.1
0.11
0.12
0.13

Rapid intermediate
flow (Sf1, m)

0.01
0.01
0.01
0.00050.01
Aquifer tank parameters

HeightRapid
where
Surface intermediate
groundroughnessflow
Initial storage
infiltration coefficient regulation
height (m)occurs (N, m-1/3s) coefficient(Sfo, m)
(αri)
0.005
0.005
0.005
0.0001
0.005

Coefficient of base outflow (Ag, 1/day)

0.7
2
2
0.1
2

0.8
0.6
0.5
0.9
0.5

0
0
0
0
0

Initial storage
Height where slow
height ofintermediate outflow occurs
groundwater(Sg, m)tank (m)

0.003
0.003
0.003
0.003
River tank parameters

2
2
2
2

2
2
2
2

Manning
InitialConstant Constant
Infiltration Coefficient Coefficient Coefficient Coefficientroughness water table
Coefficient ofof theof theof aquifer of crossof crossof crossof crossParameter
coefficient of rivercross shape
Resume Resume
tank
shape
shape
shape
shape
of channel channel(RLCOF)
Law (c) Law (s)(1/day)
(RHW)
(RHS)
(RBH)
(RBET)(n, m-1/3s)
(m)
1
2
3

7
7
7

0.5
0.5
0.5

0.035
0.035
0.035

0.2
0.2
0.2

0
0
0

9999
9999
9999

1
1
1

0.5
0.5
0.5

0.05
0.05
0.05

1.4
1.4
1.4

* Note: Surface tank: 1,2,3,4,5 - Classification of Land-use
Aquifer tank: 1,2,3,4 - Classification of Geology
River tank : 1,2,3 – The number of upper cells (n)1: (n ≤ 4), 2: (5 < n < 64), 3: (n ≥ 65)

Figure 3.8(a) compares the simulated discharges and observed discharges for theflood event of August 1981 after calibration. Figure 3.8 (b), (c), and (d) compare thesimulated discharges and observed discharges in the validation process for flooding
events in September 2001, September 2011, and August 2016, respectively. The model
calibration was performed using a “trial and error” process. As shown in Figure 3.8, the
IFAS closely reproduced the flood duration and peak discharge in most cases. For theAugust 1981 flood, the simulated discharges showed close agreement with the observed
discharges, as indicated by a high NSE value of 0.95, an Ew of 0.08, Ev of 0.04, and Ep of-0.01.
47

Chapter 3
In the validation process, the simulated discharges matched well with the observed
discharges for September 2001, September 2011, and August 2016 floods, with high NSE
values of 0.96, 0.92, and 0.90, respectively. The simulated peak discharges were evaluatedto be slightly lower than the observed peak discharges for the September 2011 and August
2016 flood events. However, the statistical performance indices suggested good
performance for all cases. The detailed indicators are shown in Table 3.4. These results
suggest that the IFAS model can perform reasonably well for the Ishikari River basin.

Figure 3.8. Comparison of observed and simulated discharges at Ishikari Ohashi station
for the (a) 1981 flood event, (b) 2001 flood event, (c) 2011 flood event, (d) 2016 flood
event

48

Chapter 3
Table 3.4. Performance of the IFAS model in the Ishikari River basin
Nash
Flood events

coefficient

Wave shape

Volume Error

Peak Dischargeerror (Ew)

(Ev)

Error (Ep)(NSE)
Calibration August 1981

0.95

0.08

0.04

-0.01

September 2001

0.96

0.38

-0.16

-0.05

Validation September 2011

0.92

0.08

0.03

0.09

August 2016

0.90

0.11

-0.01

0.05

3.3.2. Future changes in extreme rainfall
Figure 3.9 shows the relative frequency of annual maximum rainfall (mm/ 72h) in
the Ishikari River basin for the observed rainfall data, the historical and future simulations.
The results indicate that the annual maximum rainfall (mm/ 72h) in the historicalsimulation and the rainfall amount (mm/ 72 h) from the observation data have simularfrequencies. The result from Student’s t-test demonstrates a p-value of 0.214, which
exceeds α. This result implies that the mean of the observed rainfall data is similar to themean of rainfall data obtained from the historical simulation.
Addition, the rainfall amount is projected to increase significantly in the future. The
mean value of the rainfall amount in the future simulation is 86.3 (mm/ 72h), which is
1.13 times that in the historical simulation (76.6 mm/ 72h). The Student’s t-test resultshows that the p-value is less than 2.2 x 10-6, i.e. much smaller than 0.05. This adequately
proves that validity of H1. Therefore, the frequency and magnitude of extreme rainfall are
expected to increase in the future.

49

Chapter 3
0.8
0.7

Observed (N=93)

Historical simulation (N=3000)
Min

Mean

Max

Observed

31.30

81.30

282.20

Historical simulation

9.57

76.55

371.99Future simulation

5.73

86.28

454.48Relative frequency

0.6
0.5
0.4

Future simulation (N=5400)

0.3
0.2
0.1
0
0-50

50-100100-150 150-200 200-250 250-300 300-350 350-400 400-450 450-500

Annual maximum rainfall (mm/72h)

Figure 3.9. Relative frequency of annual maximum rainfall (mm/ 72h) in the Ishikari
River basin for the observed rainfall data, and the historical and future simulations
3.3.3. Future changes in extreme rainfall between the top 20 and top 36 rainfall events
Table 3.5 and Table 3.6 show the annual maximum rainfall (mm/ 72h) for the top 20
and top 36 rainfall events in the Ishikari River basin and in its main sub-basins.

50

Chapter 3

Table 3.5. Top 20 rainfall events in the Ishikari River basin and in its main sub-basins

Annual maximum rainfall (mm/72h)
No. Name of the event Ishikari Toyohira Chitose
River
River
River
basin
basin
basin
1 HPB_m043_2006 372*
545*
621*
2 HPB_m004_1957
346
346
525
3 HPB_m063_1968
314
343
382
4 HPB_m010_1976
303
236
124
5 HPB_m089_2002
254
127
336
6 HPB_m065_2007
244
211
259
7 HPB_m086_1988
238
296
156
8 HPB_m045_1957
237
226
290
9 HPB_m004_2000
234
268
330
10 HPB_m007_1970
233
330
397
11 HPB_m005_2005
221
242
228
12 HPB_m084_1955
220
344
243
13 HPB_m088_1955
218
246
280
14 HPB_m006_1994
216
246
209
15 HPB_m061_1964
214
194
256
16 HPB_m064_1987
211
175
209
17 HPB_m024_1996
208
148
173
18 HPB_m025_1962
207
105
167
19 HPB_m046_1980
206
287
282
20 HPB_m043_1995
204
227
209

Yubari
River
basin
423
514*
415
218
362
299
354
347
234
274
255
213
237
222
246
238
248
209
236
261

Ikushunbetsu
River basin
394
413*
397
337
364
268
402
290
187
263
277
196
256
241
230
204
198
200
172
232

Sorachi
River
basin
258
418*
335
276
276
249
251
282
242
209
252
166
166
220
206
283
231
194
190
219

Uryu
River
basin
406*
148
293
229
124
176
60
142
222
167
113
253
235
188
182
144
208
190
143
196

Chubetsu
River
basin
228
349
242
480*
301
267
366
217
174
191
254
145
184
185
238
267
225
256
229
203

Note: (*) The maximum rainfall event (mm/72h)

51

Chapter 3

Table 3.6. Top 36 rainfall events in the Ishikari River basin and in its main sub-basins

No.

Name of the event

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

HFB_MP_m112_2062
HFB_MI_m108_2094
HFB_GF_m110_2052
HFB_GF_m104_2072
HFB_MR_m102_2062
HFB_HA_m102_2065
HFB_CC_m114_2085
HFB_MI_m103_2103
HFB_MI_m106_2059
HFB_MP_m101_2067
HFB_MI_m106_2083
HFB_CC_m107_2084
HFB_HA_m102_2067
HFB_MR_m105_2091
HFB_GF_m112_2053
HFB_HA_m102_2105
HFB_MR_m111_2066
HFB_MI_m107_2060
HFB_MP_m103_2078
HFB_MI_m114_2085
HFB_MP_m114_2070
HFB_MI_m113_2099
HFB_GF_m110_2066
HFB_MI_m115_2063
HFB_MI_m102_2060
HFB_GF_m108_2052
HFB_HA_m115_2103
HFB_GF_m111_2109
HFB_MI_m113_2110
HFB_GF_m112_2102
HFB_MR_m102_2074
HFB_MI_m101_2083
HFB_HA_m104_2082
HFB_HA_m101_2066
HFB_HA_m105_2083
HFB_CC_m110_2051

Annual maximum rainfall (mm/72h)
Ishikari Toyohira Chitose Yubari
SorachiIkushunbetsu
River
River
River River
River
River basin
basin
basin
basin basin
basin
454*
184
418 719*
656
567
454*
454
400
644
751*
707*
386
242
454
706
505
525
373
208
375
401
479
368
356
276
279
273
455
350
345
248
394
462
495
333
331
357
380
517
406
400
325
560
426
152
230
144
315
392
449
308
304
282
311
483
420
325
336
318
305
262
321
367
387
317
304
188
243
338
340
290
301
131
163
251
340
349
300
894*
723* 222
241
187
296
351
341
308
338
254
288
430
462
349
317
180
287
358
295
356
330
338
279
122
326
502
351
387
271
665
537
241
213
312
270
248
319
296
352
332
270
378
382
322
336
270
269
275
311
131
306
238
266
418
334
260
235
229
259
315
567
394
456
206
258
106
219
360
274
285
256
117
353
418
421
301
255
323
377
349
223
253
254
37
44
102
146
212
248
142
118
222
338
276
244
101
324
358
392
235
242
279
280
260
322
238
241
300
326
299
451
348
239
394
368
265
269
158
238
304
368
330
253
262
236
305
291
247
236
188
234
201
226
305
295
248

Uryu
River
basin
343
112
130
353
332
328
169
600*
333
234
229
330
229
220
272
368
136
91
218
201
145
264
253
69
218
86
193
338
187
232
200
80
230
160
278
174

Chubetsu
River
basin
487
502*
432
358
419
298
329
135
254
251
278
311
368
131
269
156
290
360
178
186
264
287
224
187
286
306
227
376
452
195
208
217
139
161
190
239

Note: (*) The maximum rainfall event (mm/72h)

Figure 3.10 shows the boxplots of annual maximum rainfall (mm/ 72h) for the top
20 and top 36 rainfall events in the Ishikari River basin and in its main sub-basins. In
addition, the percentage difference in the median value of annual maximum rainfall (mm/72h) between the top 20 and top 36 rainfall events for the Ishikari River basin and in its
52

Chapter 3
main sub-basins is shown in Figure 3.11. It is shown that the median 72-h value in the
Ishikari River basin is projected to be 21% higher in the future. It can be seen that extreme
rainfall is expected to increase significantly not only in the Ishikari River basin but also
in its main sub-basins. The extreme rainfall is projected to increase the most in the Chitose,
Ikushunbetsu, and Yubari River basins located in the southern part of the Ishikari Riverbasin, with an estimated increase of 35%, 30%, and 39%, respectively. Moreover, thespatial distributions of annual maximum rainfall (mm/ 72h) averaged for the top 20 and
top 36 rainfall events, and the percentage difference between the selected historical andfuture events are shown in Figure 3.12. These results also indicate that the increase in
extreme rainfall is expected to be particularly significant in the Ikushunbetsu and ChitoseRiver basins.

Figure 3.10. Boxplots of annual maximum rainfall (mm/ 72h) for the top 20 and top 36
rainfall events in the Ishikari River basin and in its main sub-basins

53

Chapter 3

141o0’0”E

142o0’0”E

143o0’0”E

LegendUryu River basin

44o0’0”N

44o0’0”N

Ishikari River basin

Chubetsu River basin
Sorachi River basin

21.4%

Ikushunbetsu River basin
Yubari River basin

21.2%

15.5%

Toyohira River basin

29.9%

43o0’0”NRiver channel

43o0’0”N

Chitose River basin

14.1%

28.6%
18.7%

34.8%

141o0’0”E

142o0’0”E

143o0’0”E

Figure 3.11. Percentage difference in the median of annual maximum rainfall (mm/ 72h)
between the top 20 and top 36 rainfall events

54

Chapter 3
LegendIshikari River basin
Annual maximum rainfall (mm/72h)
averaged for the top 20 rainfall events
140-170
170-200
200-220220-250250-270270-300
300-320320-350
350-370

LegendIshikari River basin
Annual maximum rainfall (mm/72h)
averaged for the top 36 rainfall events
140-170
170-200
200-220220-250250-270270-300
300-320320-350
350-370
370-400
400-420420-460

LegendIshikari River basin
Percentage difference(%)-3.0 - 2.5

Uryu

2.5 - 8.0
8.0 - 13
13 - 18

Ikushunbetsu

18 - 23
23 - 29

Sorachi

29 - 34
34 - 39
39 - 45

Chubetsu

Toyohira
ChitoseYubariFigure 3.12. Spatial distribution of annual maximum rainfall (mm/ 72h) averaged forthe (a) top 20 rainfall events and (b) top 36 rainfall events; (c) percentage difference
between the selected historical and future events
55

Chapter 3
3.3.4. Future changes in river floods between the top 20 and top 36 rainfall events
Table 3.7 and Table 3.8 show the results of peak discharge for the top 20 and top 36
rainfall events at 8 reference stations in the Ishikari River basin and in its main sub-basins.

Table 3.7. Peak discharge results for the top 20 rainfall events in the Ishikari River basin
and in its main sub-basins
Peak discharge Qmax (m3/s)
No. Name of the event
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

HPB_m043_2006
HPB_m004_1957
HPB_m063_1968
HPB_m010_1976
HPB_m089_2002
HPB_m065_2007
HPB_m086_1988
HPB_m045_1957
HPB_m004_2000
HPB_m007_1970
HPB_m005_2005
HPB_m084_1955
HPB_m088_1955
HPB_m006_1994
HPB_m061_1964
HPB_m064_1987
HPB_m024_1996
HPB_m025_1962
HPB_m046_1980
HPB_m043_1995

Ishikari Toyohira Chitose
River
River
River
basin
basin
basin
13,9052,629
3,575*
19,386* 1,076
3,447
13,4631,244
1,580
18,926822
237
11,566190
1,453
12,143560
972
10,157
1,690
426
8,381
510
1,214
10,791
1,106
2,774
12,007
2,162
2,814
6,900
738
845
8,982
2,982*
1,365
10,615
1,526
2,057
9,894
585
670
7,641
521
1,687
6,547
271
457
9,309
265
503
9,947
205
685
8,023
1,453
1,676
6,707
543
956

Yubari
SorachiIkushunbetsu
River
River
River basin
basin
basin
2,038
643
1,936
3,664*
965
5,726*
2,859
940
3,667
539
564
3,652
1,543
1,007
2,692
2,184
446
3,306
1,331
1,142*
2,984
1,826
489
2,835
1,095
204
2,385
1,799
760
2,898
806
482
2,039
731
231
1,146
1,468
612
1,745
869
415
2,727
873
292
1,679
735
192
2,647
1,426
224
3,375
1,125
303
2,172
951
173
1,540
1,012
320
1,728

Uryu Chubetsu
River
River
basin
basin
2,614*
552
800
959
1,600
630
1,678
2,607*
354
1,093
1,073
1,179
208
1,457
708
637
1,413
391
776
781
393
607
2,344
309
1,937
700
1,181
483
958
680
595
788
1192
883
1,940
797
649
800
901
418

Note: (*) The greatest peak discharge event (m3/s)

56

Chapter 3
Table 3.8. Peak discharge results for the top 36 rainfall events in the Ishikari River basin
and in its main sub-basins

No.

Name of the event

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

HFB_MP_m112_2062
HFB_MI_m108_2094
HFB_GF_m110_2052
HFB_GF_m104_2072
HFB_MR_m102_2062
HFB_HA_m102_2065
HFB_CC_m114_2085
HFB_MI_m103_2103
HFB_MI_m106_2059
HFB_MP_m101_2067
HFB_MI_m106_2083
HFB_CC_m107_2084
HFB_HA_m102_2067
HFB_MR_m105_2091
HFB_GF_m112_2053
HFB_HA_m102_2105
HFB_MR_m111_2066
HFB_MI_m107_2060
HFB_MP_m103_2078
HFB_MI_m114_2085
HFB_MP_m114_2070
HFB_MI_m113_2099
HFB_GF_m110_2066
HFB_MI_m115_2063
HFB_MI_m102_2060
HFB_GF_m108_2052
HFB_HA_m115_2103
HFB_GF_m111_2109
HFB_MI_m113_2110
HFB_GF_m112_2102
HFB_MR_m102_2074
HFB_MI_m101_2083
HFB_HA_m104_2082
HFB_HA_m101_2066
HFB_HA_m105_2083
HFB_CC_m110_2051

Peak discharge Qmax (m3/s)
Ishikari Toyohira Chitose Yubari
Sorachi Uryu Chubetsu
Ikushunbetsu
RiverRiver River River
River River
River
River basin
basin
basin
basin
basin
basin
basin
basin
32,677*392
2,559 6,633
2,943
8,067 3,026
1,874
23,0291,960 1,601 3,915
2,810
9,925
439
2,224
28,213501
2,820 10,544*
2,321
11,503* 484
2,280
25,018368
1,493 2,268
1,384
5,259 3,454
1,749
15,462659
1,208 1,536
906
5,197 2,535
1,452
19,876
1,077 1,820 3,091
1,799
5,003 2,981
863
12,1052,450 2,943 4,076
723
4,796
924
1,212
20,2802,185 1,664
369
231
1,161 11,235*
234
18,4802,096 3,185 2,105
532
3,763 3,364
791
12,4412,074 1,827 1,542
584
3,468 1,402
697
15,311580
1,286 2,090
1,251
4,453 1,735
853
11,153
298
730
1,403
442
2,750 1,536
779
13,480
204
462
1,138
812
4,026 1,205
1,049
9,454
6,281 4,854
661
227
1,791 1,795
384
18,0903,458 2,895 2,880
1,533
4,017 3,569
1,598
13,2992,482 2,899 1,707
604
1,321 4,332
310
13,5991,698 1,705 2,304
535
4,602
725
735
14,931131
1,608 5,481
1,748
5,290
332
1,476
12,446 7,770* 5,724* 1,845
495
1,654 2,134
1,141
9,827
998
1,714
967
745
3,756 1,289
356
11,362
1,553 1,396 1,546
794
2,996
601
890
17,1191,632 1,589
358
1,532
8,106 2,959
2,305
9,586
2,261 2,084
916
267
1,997 1,611
455
11,564933
4,054 1,904
1,121
1,698
224
392
12,406
135
670
2,045
647
2,578 1,579
1,014
23,284199
1,999 4,057
3,014*
7,421
427
2,724*
11,4772,491 2,904 2,092
272
3,677 1,054
930
10,610
59
85
319
226
1,663 2,865
801
8,057
775
412
643
456
2,023
891
1,118
16,536174
2,670 3,641
1,440
4,426 2,044
763
10,223
563
881
936
315
2,286 1,466
652
12,225
1,645 2,008 2,189
1,618
7,646
289
1,685
13,6973,760 3,003 2,079
724
1,804 2,571
396
10,2501,162 1,557 1,539
353
3,085 1,211
524
14,5492,792 2,491 1,537
505
2,050 3,046
556
9,888
523
901
1,569
669
2,166
918
432

Note: (*) The greatest peak discharge event (m3/s)

Figure 3.13 shows boxplots of the peak runoff depth (mm/h) for the top 20 and top
36 rainfall events in the Ishikari River basin and in its main sub-basins. Figure 3.14 shows
the percentage difference in the median of the peak runoff depth between the top 20 and
top 36 rainfall events in the Ishikari River basin and in its main sub-basins.
57

Chapter 3

Figure 3.13. Boxplots of the peak runoff depth (mm/h) for the top 20 and top 36 rainfall
events in the Ishikari River basin and in its main sub-basins

58

Chapter 3
141o0’0”E

142o0’0”E

143o0’0”E

LegendUryu River basin

44o0’0”N

44o0’0”N

Ishikari River basin

Chubetsu River basin
Sorachi River basin

53.8%

Ikushunbetsu River basin

Yubari River basin

15.9%

33.3%

53.5%

Toyohira River basin

55.9%

43o0’0”NRiver channel

43o0’0”N

Chitose River basin

52.6%
43.7%

37.0%

141o0’0”E

142o0’0”E

143o0’0”E

Figure 3.14. Percentage difference in the median of peak runoff depth between the top 20
and top 36 rainfall events in the Ishikari River basin and in its main sub-basins
These results indicate that the river floods were projected to increase significantly in
the Ishikari River basin and in its main sub-basins. In particular, the Ikushunbetsu, Yubari,and Toyohira River basins were more likely to experience extremely large river flooding
events with peak runoff depths exceeding 25 mm/h.
The percentage difference in the median of the peak runoff depth between the top 20
and top 36 rainfall events indicated that the peak runoff depth is projected to increase by
33% in the Ishikari River basin. The Ikushunbetsu, Uryu, Sorachi, and Yubari river basinsare expected to undergo remarkable increases of 56%, 54%, 54%, and 54%, respectively,in peak runoff depths. In particular, the Ikushunbetsu and Yubari river basins located in
the southern part of the Ishikari River basin are expected to experience a significant
increase in extreme rainfall and river floods. However, the river floods from extreme
rainfall will increase to a greater extent.
59Chapter 3
3.3.5. Time difference prediction
The difference between the time of peak discharge at the reference station in each
tributary and the time of peak water level at the confluence point in the main river was
estimated for the top 20 and top 36 rainfall events. Figure 3.15 shows the time difference
averaged for the top 20 and top 36 rainfall events in each of the sub-basins.

141o0’0”E

140o0’0”E

Toyohira11.1 km

Chitose15.0 km

Yubari

9.8 kmIkushunbetsu

7.0 kmSorachi17.5 km

Chubetsu

18.1 km Historical: 9.05 h
6.0 km Future: 7.75 h

Confluence point

Historical: 9.40 h
Future: 8.56 h

Ishikari Ohashi station

Historical: 2.30 h
Future: 3.00 h

Historical: 8.20 h
Future: 6.06 h

Historical: 5.55 h
Future: 4.53 h

Lake Shikotsu
Time difference:hour (h) = t_confluence point - t_reference station
Peak discharge at the reference station (m3/s)
Peak water level at the confluence point (m)140o0’0”E

141o0’0”E

142o0’0”E

43o0’0”N

Reference station

Historical: 3.25 h
Future: 3.44 h

Time (hour)

Water level (m)43o0’0”N

Uryu

44o0’0”N

Distance from the reference
station to the confluence point

River discharge (m3/s)

44o0’0”N

Sub_basins

142o0’0”E

143o0’0”E

Figure 3.15. Time difference averaged for the top 20 and top 36 rainfall events in each of
the sub-basin
The results show a slight increase of 0.19 h and 0.70 h in the future timedifference in the Uryu and Sorachi River basins, respectively. Conversely, the timedifferences in the Ikushunbetsu, Yubari, Chitose, and Toyohira River basins are expected
to decrease by 1.30, 2.14, 1.02, and 0.84 h, respectively. The time difference in the
Chubetsu river basin was almost unchanged with a slight decrease of 0.05 h. The shorterthe time difference, the greater the flood risk. These results can serve as a useful additionalreference for flood damage mitigation strategies in vulnerable areas. Particular attention
60

Chapter 3
should be paid to the Chitose River basin because it is a lowland area prone to flood
damage.
3.4. Conclusions
The following conclusions were drawn from this study:

Following validation, the IFAS model can provide reasonable simulations of riverdischarges in the Ishikari River basin.

Severe rainfall and river flooding events are expected to increase significantly in the
Ishikari River basin and in its main sub-basins in the future. Additionally, the riverflooding resulting from extreme rainfall would increase to a greater extent than theincrease in extreme rainfall in the Ishikari River basin and in its main sub-basins. The
effect of climate change is significant in the sub-basins located in the southern partof the Ishikari River basin, where extreme rainfall is expected to increase by 29%35%, whereas the river discharge is likely to increase drastically by 37%-56%. The
difference between the time of peak discharge at the reference station and the time ofpeak water level at the confluence point in the main river is expected to decrease by
1.02 h to 2.14 h in these regions.

Special attention should be paid to the Chitose River basin, as it is located in alowland area of the Ishikari River basin that is prone to flood damage.
These results will provide additional information about the effect of climate changeon the risk of river floods for establishing climate change adaptation and flood damage
mitigation strategies in vulnerable areas.
We have obtained useful findings; however, we have some limitations that need toaddressed in further study. Because our approach focused on natural hazards, we ignored
future changes in land-use activities and populations. Additionally, we did not consider
the existing flood control facilities in the basin. However, we predict that short-term
extreme rainfall events will have much greater effects on river flooding than the
aforementioned factors. In addition, in this chapter, we assessed the change in timedifference between the time of peak discharge at the reference station and the time of peakwater level at the confluence point in the main river using the IFAS model. This effort
aims to provide useful information for early flood warning systems to minimize flood
61

Chapter 3
damage. However, in this chapter, we used the IFAS model, which uses a kinematic wave
for river channel flow simulation. The IFAS model does not have an analysis function to
reproduce overtopping from river course and inundation. Therefore, the prediction might
not accurately reflect the predicted results. The time difference prediction will be
improved in further studies using the hydrodynamic model.
3.5. References
1. Giorgi, F. Regionalization of climate change information for impact assessment andadaptation. WMO Bulletin. 2008, 57, 86-92.
2. Intergovernmental Panel on Climate Change (IPCC). 2012. Managing the Risks ofExtreme Events and Disasters to Advance Climate Change Adaptation; A Special
Report of Working Groups I and II of the IPCC; Field CB, Barros V, Stocker TF, Qin
D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK (eds).
Cambridge University Press, Cambridge, UK, New York, USA.
3. World
HealthOrganization.
2021.
Available
online:https://apps.who.int/iris/bitstream/handle/10665/76224/9789241564526_section_2_
eng.pdf. (accessed on 04 May 2021).
4. Doocy, S.; Daniels A.; Murray, S.; Kirsch, TD. The human impact of floods: A
historical review of events 1980-2009 and systematic literature review. PLoS currents.
2013, 5. DOI: 10.1371/currents.dis.f4deb457904936b07c09daa98ee8171a
5. Hirabayashi, Y.; Kanae, S. First estimate of the future global population at risk offlooding. Hydrological Research Letters. 2009, 3, 6-9. DOI: 10.3178/hrl.3.6
6. Intergovernmental Panel on Climate Change (IPCC). 2013. The physical science basis
in the fifth assessment report of the intergovernmental panel on climate change. IPCC.
Cambridge University Press, Cambridge, UK; 1535.
7. Kitoh, A.; Endo, H. Changes in precipitation extremes projected by a 20 km meshglobal atmospheric model. Weather and Climate Extremes. 2016. 11, 41-52. DOI:
10.1016/j.wace.2015.09.001
8. Sun, Y., Solomon, S., Dai, A., Portmann, RW. How often will it rain? Journal ofclimate. 2007. 20, 4801–4818.
9. Sillmann, J., Kharin, VV., Zwiers, FW., Zhang, X., Bronaugh, D. Climate ex- tremes62Chapter 3
indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J.
Geophys. Res. Atmos.2013.118, 2473–2493. Doi: 10.1002/ jgrd.50188
10. Higashino, M.; Stefan, HG. Variability and change of precipitation and flood
discharge in a Japanese river basin. Journal of Hydrology: Regional Studies. 2019,21, 68-79. DOI: 10.1016/j.ejrh.2018.12.003.
11. Hirabayashi, Y.; Kanae, S.; Emori, S.; Oki, T.; Kimoto, M. Global projections ofchanging risks of floods and droughts in a changing climate. Hydrological Sciences
Journal. 2008, 53, 754-772. DOI: 10.1623/hysj.53.4.754.
12. Try, S.; Tanaka, S.; Tanaka, K.; Sayama, T.; Lee, G.; Oeurng, C. Assessing the effects
of climate change on flood inundation in the lower Mekong Basin using highresolution AGCM outputs. Prog. Earth Planet. Sci. 2020, 7, 34. DOI:
10.1186/s40645-020-00353-z.
13. Fujita, M., Mizuta, R., Ishii, M., Endo, H., Sato, T., Okada, Y., Kawazoe, S.,
Sugimoto, S., Ishihara, K., Watanabe, S. Precipitation changes in a climate with 2 K
surface warming from large ensemble simulations using 60 km global and 20 km
regional atmospheric models. Geophysical Research Letters. 2018, 46, 435-442. DOI:
10.1029/2018GL079885
14. Sato, Y.; Kojiri, T.; Michihiro, Y.; Suzuki, Y.; Nakakita, E. Assessment of climatechange impacts on river discharge in Japan using the super-high-resolution MRIAGCM. Hydrological Processes. 2013, 27, 3264-3279. DOI: 10.1002/hyp.9828
15. Miyasaka, T.; Kawase, H.; Nakaegawa, T.; Imada, Y.; Takayabu, I. Future projectionsof heavy precipitation in kanto and associated weather patterns using large ensemble
high-resolution simulations. SOLA. 2020, 16, 125-131. DOI: 10.2151/sola.2020-022
16. Yamamoto, K.; Sayama, T.; Apip. Impact of climate change on flood inundation in a
tropical river basin in Indonesia. Prog. Earth Planet. Sci. 2021, 8, 5. DOI:
10.1186/s40645-020-00386-4
17. Kitoh, A.; Ose, T.; Kurihara, K.; Kusunoki S, Sugi, M. Projection of changes in future
weather extremes using super-high-resolution global and regional atmosphericmodels in the KAKUSHIN Program: Results of preliminary experiments.
Hydrological Research Letters. 2009. 3, 49-53. DOI: 10.3178/HRL.3.49
18. River Council for Social Infrastructure Development. 2018. Council Report Flood
Risk Management for Wide-area and Long-lasting Rainfall – Multi-layered63

Chapter 3
countermeasuresforcomplexdisasters.
https://www.mlit.go.jp/river/mizubousaivision/toushin_e/1812_flood_risk_manage
ment_e.pdf. Last access July 1, 2020.
19. Kim, S.; Nakakita, E.; Tachikawa, Y.; Takara, K. Precipitation changes in Japan underthe A1B climate change scenario. Annual Journal of Hydraulic Engineering, JSCE.
2010, 54, 127-132.
20. Yamada TJ. 2019. Adaptation measures for extreme floods using huge ensemble ofhigh-resolution climate model simulation in Japan. Summary report on the eleventhmeeting of the research dialogue. United Nations Framework Convention on Climate
Change(UNFCCC),
Bonn,
Germany:2829.https://unfccc.int/sites/default/files/resource/RD11SummaryReport.pdf.
Lastaccess April 8, 2020.
21. Sato, Y.; Kojiri, T.; Michihiro, Y.; Suzuki, Y.; Nakakita, E. Estimates of climatechange impact on river discharge in Japan based on a super-high-resolution climate
model. Terrestrial,

Atmospheric and Oceanic Sciences. 2012, 23, 527-540.

DOI:10.3319/TAO.2012.05.03.02(WMH).
22. Tachikawa, Y.; Takino, S.; Ichikawa, Y.; Shiiba, M. Study on the impact of climatechange on river flow regime in the Mogami and Yoshino River basins. Annual
Journal of Hydraulic Engineering. 2009, 53, 475-480 (in Japanese).
23. Hoshino, T.; Yamada, TJ. Assessment of climate change effect on precipitation
amount within river domains of Hokkaido. Proceedings of 2017 Annual Conference,Japan

SocietyofHydrology

and

Water

Resources.

2017,

12.

DOI:

10.11520/jshwr.30.0_12.
24. Yamada, TJ.; Hoshino, T.; Masuya, S.; Uemura, F.; Yoshida, T.; Omura, N.;
Yamamoto, T.; Chiba, M.; Tomura, S.; Tokioka, S.; Sasaki, H.; Hamada, Y.;
Nakatsugawa, M. The influence of climate change on flood risk in Hokkaido.
Advances in River Engineering. 2018, 24, 391–396 (in Japanese)
25. Japan

River

Association.

2003.

The

IshikariRiver.

http://www.japanriver.or.jp/EnglishDocument/DB/file/002%20Hokkaido%2003.pdf.
Last access May 20, 2019.
26. Japan Society of Civil Engineers (JSCE). Onsite Field Investigation Team, Site–
Investigation Report for the Hokkaido Heavy Rainfall Disasters in August 2016; Title
was translated by N. Kimura; JSCE Report; JSCE: Tokyo, Japan, 2017. (In Japanese).
64Chapter 3
27. Ishii, M.; Mori, N. 4PDF: large-ensemble and high-resolution climate simulations for
global warming risk assessment. Progress in Earth and Planetary Science. 2020, 7,
28. DOI: 10.1186/s40645-020-00367-7
28. Mizuta, R.; Murata, A.; Ishii, M.; Shiogama, H.; Hibino, K.; Mori, N.; Arakawa, O.;
Imada, Y.; Yoshida, K.; Aoyagi, T.; et al. Over 5000 years of ensemble future climate
simulations by 60-km global and 20-km regional atmospheric models. Bull. Am.
Meteorol. Soc. 2017, 98, 1383–1398. DOI: 10.1175/BAMS-D-16-0099.1
29. Fowler, HJ., Ekström, M., Blenkinsop, S., Smith, AP. Estimating change in extreme
European precipitation using a multimodel ensemble. Journal of GeophysicalResearch. 2007, 112. DOI: 10.1029/2007JD008619
30. Sasaki, H.; Murada, A.; Hanafusa, M.; Oh’izumi, M.; Kurihara, K. Reproducibility ofPresent Climate in a NonHydrostatic Regional Climate Model Nested within an
Atmosphere General Circulation Model. SOLA, 2011, 7, 173–176. DOI:
10.2151/sola.2011-044
31. Hoshino, T.; Yamada, TJ.; Kawase, H. Evaluation for characteristics of tropical
cyclone induced heavy rainfall over the sub-basins in the central Hokkaido, northern
Japan by 5-km large ensemble experiments. Atmosphere. 2020, 11, 435. DOI:
10.3390/atmos11050435.
32. Yoshino, F.; Yoshitani, J.; Sugiura, M. A conceptual distributed model for large-scale
mountainous basins. In Lang H, Musy A (eds). Hydrology in Mountainous Regions.
I − Hydrological Measurements; the Water Cycle; IAHS, Lausanne, Switzerland.
1990, 193, 685–692.
33. International Centre for Water Hazard and Risk Management (ICHARM). 2014.
IFAS
ver
2.0
Technical
manual.
http://
www.icharm.pwri.go.jp/research/ifas/ifas_2.0_top.html. Last access October 20,
2018.
34. Aziz, A.; Tanaka, S. Regional parameterization and applica‐ bility of Integrated Flood
Analysis System (IFAS) for flood forecasting of upper-middle Indus River. Pakistan
Journal of Meteorology. 2010, 8, 21–38.
35. Kimura, N.; Kiri, H.; Kitagawa, I. The Impact of Multiple Typhoons on Severe Floodsin the Mid-Latitude Region (Hokkaido).

Water. 2018, 10, 843. DOI:

10.3390/w10070843
36. Shrestha, BB.; Okazumi, T.; Miyamoto, M.; Nabesaka, S.; Tanaka, S.; Sugiura, A.
Fundamental Analysis for Flood Risk Management in the Selected River Basins of65

Chapter 3Southeast Asia. Journal of Disaster Research. 2014, 9, 858-869. DOI:
10.20965/jdr.2014.p0858
37. Kimura, N.; Kiri, H.; Kanada, S.; Kitagawa, I.; Yoshinaga, I.; Aiki, H. Floodsimulations in mid-latitude agricultural land using regional current and future extreme
weathers. Water. 2019, 11, 2421. DOI: 10.3390/w11112421
38. Ministry of Land, Infrastructure, Transport and Tourism (MLIT). 2020. Water
Information System. http://www1.river.go.jp/. Last access September 20, 2020
39. Nash, J.; Sutcliffe, J. River flow forecasting through conceptual models part I-A
discussion of principles. Journal of Hydrology. 1970, 10, 282-290. DOI:
10.1016/0022-1694(70)90255-6.
40. Japan Institute of Construction Engineering (JICE). 2011. Outflow analysis system,
analytical technique manual Ver 2.3. http://www.jice.or.jp/. Last access April 15,
2019.

66

Chapter 4
Flood inundation assessment in the low-lying
river basin considering extreme rainfall
impacts and topographic vulnerability

Chapter 4
4.1. Introduction
In recent decades, extreme weather events associated with climate changes have
rapidly become one of the global concerns threatening natural environments and human
life. According to the IPCC Fifth Assessment Report [1], on the longer time scale, theglobal average surface temperature increased by about 0.73oC per century, and this trend
is likely to continue into the new millennium. In addition, precipitation is one of the mostcritical and useful indicators reflecting changes in climate conditions [2]. The IPCC Fifth
Assessment Report (2013) [1] also indicated that rainfall is expected to increase in the
future, especially in extreme rainfall patterns.
Among many physical factors influencing river flooding, precipitation is the main
factor affecting runoff formation and flow regimes [2,3]. Additionally, other factors
caused by human interactions such as land-use changes and rapid development of socioeconomic activities also promote floods [4]. Due to the complex nature of these factors
on river flooding, all of them are considered a challenging task for hydrologists. However,assessing flood risk by considering the main factors affecting river floods is possible foreach river basin’s characteristics.
The risk of severe natural events, such as flood inundation, landslides, and sedimentrelated disasters caused by extreme rainfall, is likely to increase in the future [5,6]. During
summer, severe flood risk from extreme rainfall events is a common phenomenon,
particularly throughout Asia [7]. Moreover, large-scale damage caused by floodinundation has been reported around the world in recent years [8,9]. Therefore, many
attempts have been made to evaluate and predict flood inundation risk along many
important river basins in the world to minimize damage due to loss of life and property[4,10,11,12,13,14,15,16]. For example, evaluating the river flood risk in small andungauged river basins is currently one of the most common challenges for hydrologists.
Hence, several studies have been conducted to investigate and map the flood hazards forthese river basins to minimize flood damage [13,14,15].
In Japan, a new record has been reached for temperature and precipitation almost
every year in recent times [17]. According to the Japan Meteorological Agency (2017)[18], on a longer time scale, the annual mean surface temperature over Japan has
increased by 1.19 per century, which is higher than the global average surface temperature
67

Chapter 4
increase (0.73oC). Additionally, the annual number of days when precipitation is equal toor greater than 200 mm has increased (statistically significant at a confidence level of99%) during the period from 1901 to 2017 in Japan [18]. Flood inundation disasters due
to extreme rainfall have occurred frequently in Japan and caused considerable damage inrecent years. For example, an extreme rainfall event on 5-6 July 2017 caused severe
floods in many parts of Kyushu, Japan, and resulted in extensive damage [19]. One year
later, during 5-8 July 2018, a widespread flood disaster caused by torrential rainfall
occurred in western Japan. The Oda River and its three tributaries exhibited the“backwater phenomenon”, where the tributary river floods intersected with the main riverfloods causing levees to break at eight points [20]. This backwater phenomenon is
projected to occur anywhere across Japan because of the presence of several river systems
with similar conditions. In the northernmost Japanese island, Hokkaido, extreme rainfall
caused severe flood inundation in August 2016; this disaster reportedly caused
approximately USD 260 million in damage, in addition to agricultural losses of over
40,258 ha of land throughout Hokkaido [21]. Most recently, on 1-13 October 2019, severe
flooding and landslides occurred in many river basins in the central northern parts ofJapan, causing great losses to life and properties [22]. In this context, to mitigate waterrelated disasters due to climate changes, especially flood inundation disasters, many
researchers have attempted to predict extreme rainfall and severe river flooding events
along many important river basins in Japan [23,24,25,26]. These attempts could provide
useful information to policymakers for climate change adaptations as well as flood
mitigation strategies.
Prediction of changes in the flow regime is a challenge [10,27] as it demands futureclimate datasets under various climate change scenarios. Popular methods of future
projection are the general circulation models (GCMs), whose rainfall andevapotranspiration data could be used as inputs to hydrological models to assess changes
in river floods. However, GCMs generally have a coarse resolution for simulation on aregional scale; such coarse resolution could lead to large uncertainties in the simulations[10,28]. Therefore, high-resolution climate data obtained through downscaling
approaches, such as statistical and dynamical downscaling, have been investigated inrecent years. For example, in a study by Sato (2012) [29], the impact of climate change
68Chapter 4
on river discharge was investigated for several major river basins in Japan using the latest
version of an atmospheric GCM (AGCM) with a 20 km horizontal resolution. In a study
by Yamada et al. (2018) [30], the super-high-resolution 5 km d4PDF dataset was
downscaled from the 20 km resolution data via a non-hydrostatic regional climate model[31]. After downscaling, the rainfall amount and spatiotemporal distribution of rainfall
were similar to the observed rainfall events. Furthermore, heavy rainfall patterns could
be better observed after downscaling, and the 5 km resolution results represented the
topography of the study area more accurately [23,30]. This suggests that the 5 km
downscaled results may be appropriate for estimating changes in heavy rainfall as well as
flood inundation on the basin scales.
The hydrological model is a fundamental approach to further understand the processof river flow. In recent years, numerous hydrological models have been developed andapplied to simulate and predict potential river floods along many river basins worldwide,such as the HEC-HMS [32], IFAS [33], TOPMODEL [34], and SWAT models [35].
However, these models cannot simulate flood inundation along the river basin and must
be combined with hydraulic models, such as HEC-RAS [36], FLO-2D [37], and MIKE
21 [38], which are popular. The 1D models cannot simulate the inundation risk infloodplains, while 2D flood models require a long simulation time. Therefore, both thesetypes of models were integrated for comprehensive flood inundation simulations andreduced computation time. Some popular integrated 1D–2D models are SOBEK [39] andthe Rainfall–Runoff–Inundation (RRI) model [40–42]. In a study by San et al. (2020) [43],the performances of RRI and SOBEK models were compared in terms of user-friendliness,cost, type of output, and correlation between simulated and observed data in the Bago
River basin. The results indicated that although the SOBEK model could provide moreaccurate results, the RRI model is preferable considering the calculation time, cost, anduser interface friendliness. In addition, the RRI model is more applicable to the near-realtime inundation mapping and the flood early warning system. Therefore, due to theseadvantages, flood inundation risk was assessed using the RRI model in this study. It cansimulate rainfall–runoff and inundation processes simultaneously in both the floodplain
and mountainous zones [40–42]. Several studies have used this model to estimate floodinundation for many river basins globally and have demonstrated its good performance69

Chapter 4[10,11,44–48].
From the results of chapter 3, as well as in a study by Nguyen [23], changes inextreme rainfall and changes in river discharges were evaluated in the Ishikari River basin,
Japan, as well as its sub-basins using the IFAS model coupled with a large-ensemble
rainfall dataset (d4PDF) with 5 km resolution. The results indicated that extreme rainfall
was projected to increase by 21.2% in the Ishikari River basin and 34.8% in the ChitoseRiver basin, whereas the river discharge could increase drastically by 33.3% in the
Ishikari River basin and 37% in the Chitose River basin. The effect of climate change issignificant in the Chitose River basin, the downstream lowland area of the Ishikari Riverbasin. This river basin is considered to be the most flood-prone in the Ishikari River basin.
In addition, the time difference between the peak discharge at the reference station in each
tributary and the peak water level at the confluence point for the seven Ishikari River
main sub-basins using the IFAS model was also predicted. However, since the IFAS
model uses a kinematic wave for river channel flow simulation [33], the prediction might
not accurately reflect the predicted results.
Considering the aforementioned information, this study aims to assess the change inflood inundation risk comprehensively (including inundation area, inundation volume,
peak inundation depth, inundation frequency, and inundation duration) considering theextreme rainfall impacts and topographic vulnerability in the Chitose River basin using
the RRI model and rainfall data extracted from d4PDF with a high-resolution of 5 km.
Owing to the topographical characteristics of its low-lying area, the Chitose River basin
is frequently affected by backwater from the Ishikari River and experiences severe floodinundation. Moreover, this study is conducted to improve the time difference prediction
result considering the dangerous impacts of backwater phenomenon on flood inundation
risk in the Chitose River basin using the RRI model, which uses a 2D distributedhydrodynamic model with diffusive wave models. The time difference is evaluatedbetween the peak discharge at the reference station in the Chitose River basin and thepeak water level at the confluence point intersecting the main river. With this effort, we
believe that the result might provide more reliability compared to previous studies of the
Chitose River basin. Flood inundation is more severe when the time difference is shorter.
The study results are expected to provide useful information for developing climate
70

Chapter 4change adaptation measurements and mitigating future flood inundation damage in thefloodplain areas.
4.2. Materials and Methods
4.2.1. Description of the study area
The study area considered herein is the Ishikari River basin, which is located in
Hokkaido, Japan (Figure 4.1). At a length of 268 km, this is the longest river in Hokkaido
and the third-longest river in Japan. In addition, this river has the second largest basin
area in Japan, with a drainage area of 14,330 km2. The river originates from Mt. Ishikaridake (1967 m above sea level), passes through the west of Hokkaido, and empties into
the Sea of Japan. It flows through 48 municipalities (including Sapporo, the prefectural
capital, and Asahikawa, the second-largest city), accounting for 52% of the population of
Hokkaido. The Ishikari Plain occupies most of the basin’s area and is located around thecentral and downstream basin area, which is the most productive agricultural area in
Hokkaido. The Ishikari River basin plays a vital role in the socioeconomic developmentof Hokkaido. The mean annual precipitation in this basin is 1300 mm; the hydrologic
peaks generally occur from March to May during the snow-melt period and from August
to September during the rainy season [49]. The Chitose River basin is one of the main
tributaries of Ishikari River (Figure 4.1). Owing to the topographical characteristics of its
low-lying area, the Chitose River basin is frequently affected by backwater from the mainriver, causing severe flood inundation. Therefore, the Chitose River basin is considered avulnerable region in the Ishikari River basin. The Chitose River has a length of 108 km
and a total basin area of 1244 km2. The main parts of the New Chitose Airport belong tothis basin area [50].
In the past, the Ishikari River basin has experienced large-scale historical flood eventsand severe damage. For example, the largest flood event in this basin occurred in August
1981, and severe damage was recorded in the target basin. Figure 4.2 shows the floodinundation from August 1981 in the Chitose River basin. The backwater phenomenon wasobserved when the water levels at the confluence point surged higher and water from themain river flowed back into the tributary, which combined with inland water and caused
severe flood inundation. This flood event caused damages amounting to approximately
71

Chapter 4
JPY 115.2 billion, two fatalities, and 22,500 inundated houses throughout the Ishikari
River basin. For the Chitose River basin, 192 km2 of land was inundated and 2,700
inundated houses were recorded for this serious flood event [49,50].

Figure 4.1. Locations of the Ishikari River basin and Chitose River basin
72

Chapter 4

Figure 4.2. Severe flood inundation event in August 1981 in the Chitose River basin.
Data source: Hokkaido Regional Development Bureau
4.2.2. d4PDF Dataset
This chapter used the large-ensemble rainfall dataset from the database of Policy
Decision making for Future climate change (d4PDF) [55], which consists of largeensemble climate experiments from the global atmospheric model, with a horizontal
resolution of 60 km, and regional downscaling simulations covering the Japan area, witha horizontal resolution of 20 km. The d4PDF consists of historical climate simulations(1951–2010) with 50 ensembles (historical simulation: 50 ensembles × 60 years = 3000
events), and the +4 K future climate simulations (2051–2110) with 90 ensembles,73

Chapter 4
including six sea surface temperature (SST) patterns and 15 ensembles for each SST (+4K
future simulation: 6 SST × 15 ensembles × 60 years = 5400 events). The +4 K futureclimate was predicted for a global mean air temperature of 4 °C warmer than thepreindustrial period. The +4 K future climate simulation corresponded to therepresentative concentration pathway 8.5 (RCP8.5) experiments under phase 5 of theCMIP5 [56]. This study used the d4PDF rainfall data with a 5 km resolution, which were
downscaled from the 20 km resolution data [23]. The target rainfall data were definedfrom 1 June to 1 December, where the annual maximum rainfall (mm/72 h) was obtained
over the target basin for each of the 3000 historical and 5400 future simulation events.
The rainfall data with a high resolution of 5 km reflect the topography of the study area
more accurately [23,30]. In this chapter, we used the top 20 and top 36 rainfall eventsfrom the results of chapter three to evaluate changes in flood inundation risk
comprehensively in the Chitose River basin using the RRI model.
4.2.3. Overview of the Rainfall-Runoff-Inundation (RRI) model
The RRI model was employed to simulate flood inundation and identify flood-prone
areas. The RRI model was developed by the International Center for Water Hazard and
Risk Management (ICHARM). The RRI model is a two-dimensional model that is
capable of simulating rainfall–runoff and flood inundation simultaneously [40–42]. At agrid cell in which a river channel is located, the model assumes that both slope and riverare 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 wascalculated with the 2D diffusive wave model, whereas the channel flow was calculatedwith the 1D diffusive wave model. The RRI model simulates the lateral subsurface,vertical infiltration, and surface flows to better represent flood characteristics. The lateralsubsurface flow, which is more important in mountainous regions, was treated in termsof the discharge–hydraulic gradient relationship, including both saturated subsurface andsurface flows, whereas the vertical infiltration flow was evaluated using the Green–Amptmodel. The details of the model are shown below [41].
A storage cell-based inundation model [51] was used to calculate lateral flows on
slope grid cells. The governing equations for the 2-dimensional unsteady flow model74

Chapter 4
include the mass balance equation (Equation (1)) and momentum equations (Equations(2) and (3)):+ 𝜕𝑥𝑥 +
𝜕𝑡

𝜕𝑞𝑦

𝜕𝑞𝑥

+

𝜕𝑣𝑞𝑥

+

𝜕𝑣𝑞𝑦

𝜕ℎ

𝜕𝑡
𝜕𝑞𝑦
𝜕𝑡

𝜕𝑞

+

𝜕𝑢𝑞𝑥

+

𝜕𝑢𝑞𝑦

𝜕𝑥

𝜕𝑥

= 𝑟 − 𝑓

𝜕𝑦

𝜕𝑦

(1)
𝜕𝐻

𝜏

= −𝑔ℎ 𝜕𝑥 − 𝜌𝑥

𝜔

𝜕𝑦

𝜏𝑦

𝜕𝐻

= −𝑔ℎ 𝜕𝑦 − 𝜌

𝜔

(2)
(3)

where h is the water level from the local surface; 𝑞𝑥 and 𝑞𝑦 are the unit widthdischarges in the x and y directions, respectively; 𝑢 and 𝑣 are the flow velocities in thex and y directions, respectively; 𝑟 is the rainfall intensity; 𝑓 is the infiltration rate; H is
the water level from the datum; 𝑝𝜔 is the density of water; 𝑔 is the gravitational
acceleration; and 𝜏𝑥 and 𝜏𝑦 are the shears stresses in the x and y directions, respectively.
The second terms of the right side of Equations (2) and (3) were calculated with
Manning’s equation.
𝜏𝑥

=

𝜌𝜔
𝜏𝑦

=

𝜌𝜔

𝑔𝑛2 𝑢√𝑢2 +𝑣 2

(4)

ℎ1/3
𝑔𝑛2 𝑣√𝑢2 +𝑣 2

(5)

ℎ1/3

where n is the Manning’s roughness parameter.
The RRI model spatially discretized the mass balance (Equation (1)) as follows:
𝑑ℎ𝑖,𝑗
𝑑𝑡
𝑖,𝑗

+

𝑖,𝑗−1

𝑞𝑥

𝑖,𝑗

− 𝑞𝑥

∆𝑥

𝑖−1,𝑗

+

𝑞𝑦

𝑖,𝑗

− 𝑞𝑦

∆𝑦

= 𝑟 𝑖,𝑗 − 𝑓 𝑖,𝑗

(6)

𝑖,𝑗

where 𝑞𝑥 , 𝑞𝑦 are the x and y direction discharges from a grid cell at (𝑖, 𝑗), respectively.
Equations (7) and (9) describe the saturated subsurface flow based on the Darcy law,while Equations (8) and (10) describe the combination of the saturated subsurface flow
and the surface flow. For the kinematic wave model, the hydraulic gradient was assumed
to be equal to the topographic slope, whereas the RRI model assumed the water surface
slope as the hydraulic gradient.
75

Chapter 4
𝜕𝐻

𝑞𝑥 = −𝑘𝑎 ℎ 𝜕𝑥 ,

5

1

(7)

(ℎ ≤ 𝑑𝑎 )
𝜕𝐻

𝜕𝐻

𝜕𝐻

𝑞𝑥 = − 𝑛 (ℎ − 𝑑𝑎 )3 √| 𝜕𝑥 | 𝑠𝑔𝑛 ( 𝜕𝑥 ) − 𝑘𝑎 ℎ 𝜕𝑥 ,𝜕𝐻

𝑞𝑦 = −𝑘𝑎 ℎ 𝜕𝑦 ,

(8)
(9)

(ℎ ≤ 𝑑𝑎 )51(𝑑𝑎 < ℎ)

𝜕𝐻

𝜕𝐻

𝜕𝐻

𝑞𝑦 = − 𝑛 (ℎ − 𝑑𝑎 )3 √| 𝜕𝑦 | 𝑠𝑔𝑛 ( 𝜕𝑦 ) − 𝑘𝑎 ℎ 𝜕𝑦 ,(𝑑𝑎 < ℎ)(10)where 𝑘𝑎 is the lateral saturated hydraulic conductivity and 𝑑𝑎 is the effective porosityof the soil depth times.
Equations (11) and (12) were used to simulate the effect of unsaturated subsurface
flows, saturated subsurface flows, and the surface flows with the single variable of ℎ.
ℎ

𝛽 𝜕𝐻

𝑚

𝜕𝑥

−𝑘𝑚 𝑑𝑚 (𝑑 ),𝜕𝐻(ℎ ≤ 𝑑𝑚 )𝜕𝐻−𝑘𝑎 (ℎ − 𝑑𝑚 ) 𝜕𝑥 − 𝑘𝑚 𝑑𝑚 𝜕𝑥 ,

𝑞𝑥 =
1

{

5

𝜕𝐻

(𝑑𝑚 < ℎ ≤ 𝑑𝑎 )

𝜕𝐻

𝜕𝐻

(11)

𝜕𝐻

− 𝑛 (ℎ − 𝑑𝑎 )3 √| 𝜕𝑥 | 𝑠𝑔𝑛 ( 𝜕𝑥 ) − 𝑘𝑎 (ℎ − 𝑑𝑚 ) 𝜕𝑥 − 𝑘𝑚 𝑑𝑚 𝜕𝑥 , (𝑑𝑎 < ℎ)
ℎ

𝛽 𝜕𝐻

𝑚

𝜕𝑦

−𝑘𝑚 𝑑𝑚 (𝑑 )𝜕𝐻,(ℎ ≤ 𝑑𝑚 )𝜕𝐻−𝑘𝑎 (ℎ − 𝑑𝑚 ) 𝜕𝑦 − 𝑘𝑚 𝑑𝑚 𝜕𝑦 ,

𝑞𝑦 =
1

{

5

𝜕𝐻

𝜕𝐻

(𝑑𝑚 < ℎ ≤ 𝑑𝑎 )
𝜕𝐻

(12)

𝜕𝐻

− 𝑛 (ℎ − 𝑑𝑎 )3 √| 𝜕𝑦 | 𝑠𝑔𝑛 ( 𝜕𝑦 ) − 𝑘𝑎 (ℎ − 𝑑𝑚 ) 𝜕𝑦 − 𝑘𝑚 𝑑𝑚 𝜕𝑦 , (𝑑𝑎 < ℎ)Infiltration is an important hydrological process. For relatively flat areas, the verticalinfiltration process during the first period of rainfall has more impact on large-scale
flooding. Therefore, the vertical infiltration can be treated as loss for event-based
simulation. In RRI, infiltration loss 𝑓 is calculated with the Green–Ampt infiltration
model. The Green–Ampt infiltration model is a simplified physical model and based on
the Richard equation. It relates the rate of infiltration to measurable soil properties suchas the porosity, hydraulic conductivity, and soil water content of a particular soil column[41,52].
𝑓 = 𝑘𝑣 [1 +(∅ − 𝜃𝑖 )𝑆𝑓
]
𝐹

(13)
76

Chapter 4
where 𝑘𝑣 is the vertical saturated hydraulic conductivity; ∅ is the soil porosity; 𝜃𝑖 is
the initial water volume content; 𝑆𝑓 is the suction at the vertical wetting front and 𝐹 isthe cumulative infiltration depth.
The flow interactions between the river channel and slope were estimated using
different overflow formulas [41].
(1) Flow from slope to river under the normal condition: when the river water level islower than the ground level, the discharge from the slope into the river is defined by
Equation (14):𝑞𝑠𝑟 = 𝜇1 ℎ𝑠 √𝑔ℎ𝑠

(14)

where 𝑞𝑠𝑟 is discharge from slope to river; 𝜇1 is the constant coefficient (=0.544), andℎ𝑠 is the water depth on a slope cell.
(2) No water exchange between slope and river: when the river water level is higher than
the ground level and both the river and slope water levels are lower than the leveeheight.
(3) Overtopping flow from the river to slope: when the river water level is higher thanthe levee height and the slope water level:
𝑞𝑟𝑠 = 𝜇2 ℎ1 √2𝑔ℎ1(15)

where 𝑞𝑟𝑠 is discharge from the river to slope; 𝜇2 is the constant coefficient (=0.35),
and ℎ1 is the difference between the river water level and the levee height.
(4) Overtopping flow from slope to the river: when the slope water level is higher thanthe levee height and river water level, and the river water level is higher than the leveeheight. The flow exchange was calculated by Equation (15), with ℎ1 presenting thedifference between the two water levels and 𝑞𝑟𝑠 being replaced with 𝑞𝑠𝑟 .
To solve the diffusive equations, the RRI model uses the fifth-order Runge–Kutta
method with adaptive time-step control. A detailed explanation is given by Sayama et al.
(2012) [41]. The RRI model provides the output of river discharge, river water level,
inundation area, and inundation depth simultaneously. A schematic of this model is shownin Figure 4.3. This model has been applied to several important river basins to evaluate
river floods as well as flood inundation, and good simulation results have been obtained[10,11,44–48].
77

Chapter 4

Figure 4.3. Schematic of the rainfall-runoff-inundation (RRI) model
4.2.4. Data inputs
 Topographic data:
The topography data, including digital elevation model (DEM), flow direction (DIR),
and flow accumulation (ACC), were obtained from the HydroSHEDS project provided
by the U.S. Geological Survey (USGS) [53]. HydroSHEDS is derived primarily fromelevation data of the Shuttle Radar Topography Mission (SRTM) data. HydroSHEDS
offers a suite of geo-referenced datasets in raster and vector format, including stream
networks, watershed boundaries, drainage directions. The HydroSHEDS supports
regional and global watershed analyses, hydrological modelling, and freshwater
conservation planning. Available resolutions of HydroSHEDS are 5 min, 30 arc-seconds,
15 arc-seconds, and 3 arc-seconds (approximately 10 km, 1 km, 500 m, and 90 m at theequator, respectively). The topography of the HydroSHED data is available at78

Chapter 4
https://www.hydrosheds.org/ (accessed on 1 February 2021). The higher DEM resolution
could provide a finer resolution of inundation mapping. However, this requires greater
computational power compared to the lower DEM resolution. The HydroSHEDS data at
3 arc-seconds (approximately 90 m) were used in this study. However, to reduce
computational time due to the large study area, a topography data scale-up algorithm wasused in the RRI model to convert the original topography data to 24 arc-seconds(approximately 720 m) for the entire IRB, and to 9 arc-seconds (approximately 270 m)
for the Chitose River basin. The DEM, DIR, and ACC data were converted to ASCII
format, which is the required format for the RRI model. Topographic data as inputs for
the RRI model was shown in Figure 4.4.

79

Chapter 4

Figure 4.4. Topographic data as inputs for the RRI model80

Chapter 4 Land Cover data
Global Land Cover Characterization (GLCC-V2) data with 1 km resolution provided
by the USGS were used in this study. Land cover has a crucial impact on the hydrologicalprocess, and the original land cover data for the target basin has 14 distinct land cover types.
However, these 14 types are too detailed to assign different parameters in the RRI model;
therefore, similar land cover types were merged and three land cover types, namely forest,
sparse vegetation, and cropland/floodplain, were finalized using the ArcGIS function. The
reclassified land cover data were resampled to the same resolution as the topography data
to input the RRI model. Land cover classification in the Ishikari River basin of original
GLCC-V2 with 14 land cover types, and reclassified land cover types were shown in Figure
4.5 (a) and (b), respectively.

81

Chapter 4

Figure 4.5. Land cover classification in the Ishikari River basin for (a) original Global
Land Cover Characterization, and (b) reclassified land cover types
82

Chapter 4
 River cross-section data:
The river cross-section (river width and depth) is an important parameter to input the
RRI model to minimize uncertainties in the output of the hydrologic simulation. In the RRI
model, a rectangular cross-section is applied to the cross-section of the river channel. The
river width (W) and river depth (D) was approximated by the following equations:
𝑊 = 𝐶𝑊 𝐴 𝑆𝑊
𝐷 = 𝐶𝐷 𝐴

𝑆𝐷

(16)
(17)

where A is the upstream contributing area (km2), W is the channel width (m), and D is thechannel depth (m). Cw, Sw, CD, and SD are geometrical parameters. The parameters of theequations were estimated by regression analysis with the cross-section data at 42 locations
spanning the Ishikari River basin. In addition, the parameters of the equations were
estimated at 33 locations across the CRB. The obtained parameters for the Ishikari Rivercross-section are Cw = 1.73, Sw = 0.60, CD = 0.18, SD = 0.45 and the obtained parameters
for the Chitose River cross-section are Cw = 14.45, Sw = 0.34, CD = 0.022, and SD = 0.80.
Plot of river width and river depth with contributing area for the Ishikari River basin wasshown in Figure 4.6. Plot of river width and river depth with contributing area for the
Chitose River basin was shown in Figure 4.7.
In addition, to increase the reliability of future flood inundation hazard assessments
under the various climate change scenarios in the vulnerable areas, the Ishikari River andChitose River cross-sections of future plans were provided by the River Management
Division of the Sapporo Development and Construction Department, Hokkaido Regional
Development Bureau and future flood inundation simulations were applied in this study.
The obtained parameters for the Ishikari River cross-section plan are: Cw = 1.68, Sw = 0.60,
CD = 0.17, and SD = 0.46. The Chitose River cross-section future plan remained unchanged
compared to the present river cross-section; therefore, the Chitose River cross-section
future plan parameters are Cw = 14.45, Sw = 0.34, CD = 0.022, and SD = 0.80. Plot of riverwidth and river depth with contributing area for the Ishikari River basin future plan wasshown in Figure 4.8.

83

Chapter 4

Plot of river depth with contributing area for Ishikari River

Plot of river width with contributing area for Ishikari River
16600
y = 1.73x0.60
R² = 0.52

400
300
200

y = 0.18x0.45
R² = 0.76

14River depth (m)River width (m)

500

100

12
10
8
6

0

4
0

2000

4000 6000 8000 10000 12000 14000 16000
Upstream contributing area (km2)

0

2000

4000 6000 8000 10000 12000 14000 16000
Upstream contributing area (km2)

Figure 4.6. Plot of river width and river depth with contributing area for the Ishikari River
basin

Plot of river width with contributing area for Chitose River

Plot of river depth with contributing area for Chitose River
8200
y = 14.45x0.34
R² = 0.55

160
140
120
100

y = 0.022x0.80
R² = 0.89

7River depth (m)River width (m)

180

6
5
4
3

80

2
400 500 600 700 800 900 1000 1100 1200 1300 1400
Upstream contributing area (km2)

400

500

600

700

800

900 1000 1100 1200 1300 1400

Upstream contributing area (km2)

Figure 4.7. Plot of river width and river depth with contributing area for the Chitose River
basin

Figure 4.8. Plot of river width and river depth future plan with contributing area for the
Ishikari River basin
84

Chapter 4
 Gauged Rainfall and Runoff data:
In the past, the Ishikari River basin experienced severe large-scale historical flood
events. In particular, the flood event of August 1981 was the largest in the target basin.
Notably, during this flood event, the Chitose River basin was severely affected—
backwater phenomenon integrated inland flooding was observed and caused tremendous
damage. Therefore, in this study, we used this large-scale flood event to calibrate the RRI
model. Additionally, three severe historical flood events in September 2001, September
2011, and August 2016 were chosen to validate the model.
To evaluate the performance of the RRI model, a comparison between the observed
and simulated river discharges was employed at the Ishikari Ohashi station,
approximately 26.6 km upstream from the river mouth (Figure 4.1). In addition, theinundated area in the Chitose River basin was evaluated and compared with the observed
inundated area for the flood event from August 1981 to calibrate the RRI model.
The gauged rainfall data and observed river discharge for the August 1981 flood
event were provided by the Hokkaido Regional Development Bureau (HRDB). The
observed inundated area in the Chitose River basin for the August 1981 flood event wasalso provided by the HRDB. The gauged rainfall for September 2001, September 2011,
and August 2016 flood events and the observed river discharges for the validation process
were obtained from the Water Information System managed by the Ministry of Land,
Infrastructure, and Transport, Japan (MLIT) [54].
4.2.5. Analytical procedure
This study was conducted to assess changes in severe river flooding in the Ishikari
River basin as well as changes in flood inundation in the Chitose River basin using the
top 20 and top 36 rainfall events from the d4PDF dataset from the results of chapter 3 aswell as from the study by Nguyen et al (2020) [23] as the input rainfall data to the RRI
model. This chapter was performed according to the following steps. First, the RRI model
was calibrated to a large historical flood event in August 1981 using the “trial and error”
method. The RRI model calibration and validation process performances were evaluatedusing four statistical indicators, including the Nash-Sutcliffe (NSE), coefficient ofdetermination (R2), relative volume error (VE), and peak discharge error (Ep). Four
85

Chapter 4
statistical indicators were defined as follows:2

NSE = 1 -

2

R =

∑𝑛
𝑖=1[𝑄𝑆𝑖(𝑖) −𝑄𝑂𝑏(𝑖) ]2∑𝑛𝑖=1[𝑄𝑆𝑖(𝑖) −𝑄𝑂𝑏 ][∑𝑛𝑖=1((𝑄𝑂𝑏(𝑖) −𝑄𝑂𝑏 )×(𝑄𝑆𝑖(𝑖) −𝑄𝑆𝑖 ))]
2

(18)
2

𝑛
∑𝑛
𝑖=1(𝑄𝑂𝑏(𝑖) −𝑄𝑂𝑏 ) × ∑𝑖=1(𝑄𝑆𝑖(𝑖) −𝑄𝑆𝑖 )

VE =

2

(19)

𝑛
∑𝑛
𝑖=1 𝑄𝑆𝑖(𝑖) −∑𝑖=1 𝑄𝑂𝑏(𝑖)

Ep =

∑𝑛
𝑖=1 𝑄𝑂𝑏(𝑖)(20)

𝑄𝑀𝑂𝑏 − 𝑄𝑀𝑆𝑖

(21)

𝑄𝑀𝑂𝑏

where 𝑄𝑆𝑖 is the simulated discharge (m3/s); 𝑄𝑂𝑏 is the observed discharge (m3/s);
𝑄𝑀𝑂𝑏 is the peak observed discharge (m3/s); 𝑄𝑀𝑆𝑖 is the peak simulated discharge(m3/s); and n is the total number of measurements.
Then, the optimal parameters were used for the validation process of the flood eventsin September 2001, September 2011, and August 2016. The optimal parameters after
calibration and validation were applied to the RRI model to evaluate future changes insevere river discharges in the Ishikari River basin and flood inundation in the ChitoseRiver basin. To evaluate the flood inundation in the Chitose River basin, the time seriesof water levels at the confluence point (Figure 4.1) was used as a boundary condition in
the RRI model. The simulations were conducted for an extra period of two weeks before
the main period for model warmup and also for initial moisture conditions in the target
basin. Changes in flood inundation in the Chitose River basin were investigatedcomprehensively in terms of the inundation area, inundation volume, peak inundation
depth, inundation frequency, and inundation duration. The statistical Kolmogorov–
Smirnov (K–S) test, which is a nonparametric test of two samples, was used to examine
the variations in flood inundation between the top 20 rainfall events (historicalsimulation) and top 36 rainfall events (future simulation). The value of the test statistic
for the two samples is defined by86

Chapter 4
𝐷 = 𝑠𝑢𝑝|𝐹1 (𝑥) − 𝐹2 (𝑥)|(22)

where F1 and F2 are the empirical distribution functions based on the two samples, andsup is the supremum function. The hypothesis of the test is as follows:
H0: The two samples have no significant differences in their cumulative distributionfunctions.
H1: Two samples have different trends in the cumulative distribution functions.
The null hypothesis is rejected if the p-value is less than the significance level α of 0.05.
Finally, the time difference prediction results between peak discharge at the reference
station in the Chitose River basin and the peak water level at the confluence pointintersecting the Ishikari River were improved compared to the results of chapter 3 using
the RRI model.
4.3. Results and discussions
4.3.1. RRI model calibration and validation
The RRI model was calibrated for the largest flood event in the Ishikari River basin
in August 1981. The optimal parameters of the RRI model after calibration are listed inTable 4.1. The simulation results are shown in Figure 4.9. As shown in Figure 4.9, theRRI model closely reproduced the wave shape and flood duration in most cases. Figure
4.9(a) compares the observed and simulated discharges at the Ishikari Ohashi station for
the flood event of August 1981 after calibration. The simulated discharges show close
agreement with the observed discharges. This result is determined by reasonablestatistical indicators NSE, R2, and VE with values of 0.96, 0.98, and −0.006, respectively.
The observed peak discharge (11,330 m3/s) has good synchronization with the simulatedone (11,310 m3/s), as indicated by an Ep value of 0.002. The optimal parameters after thecalibration process were used for the validation process of the flood events in September
2001, September 2011, and August 2016, as shown in Figure 4.9 (b), (c), (d), respectively.
It can be seen that the simulated discharges matched well with the observed discharge inmost cases, as indicated by the reasonable NSE, R2, and VE values. However, thesimulated peak discharges were found to be slightly higher than the observed peakdischarges for these flood events. This result is demonstrated by the Ep values of −0.234,−0.1, and −0.17 for the flood events in September 2001, September 2011, and August
87Chapter 4
2016, respectively. The detailed statistical indicators are listed in Table 4.2. Overall, theseresults suggest that the RRI model can perform reasonably well for further analysis in thenext sections.
Table 4.1. Optimal parameter settings of the RRI modelParameters

Notation

Unit

Mountains

PlainsManning’s coefficient for rivernriver(m-1/3s)

0.035

0.035

Manning’s coefficient for slopen(m-1/3s)

0.15

0.15

Soil depth

d

(m)

1.0

1.0

Soil porosity

Φa

(-)

0.12

0.472

Vertical hydraulic conductivity

kv(cm/h)-

0.06

Wetting front soil suction head

Sf

(-)

-

0.273

Lateral saturated hydraulic conductivity

ka

(m/s)

0.17

-

Unsaturated porosity

Φm

(-)

0.03

-

Parameter of unsaturated hydraulicconductivity

β

(-)

8.0

-

Figure 4.9. Comparison between the simulated and observed discharges at Ishikari Ohashi
station for (a) the August 1981 flood event, (b) the September 2001 flood event, (c) the
September 2011 flood event, and (d) the August 2016 flood event88Chapter 4
Table 4.2. Statistical indicators of the RRI model performance

Calibration

Validation

Flood events

NSE

R2

Ep

VE

August 1981

0.96

0.98

0.002

-0.006

September 2001

0.81

0.99

-0.2340.246

September 2011

0.88

0.91

-0.10

0.0007

August 2016

0.78

0.89

-0.17

-0.002

Figure 4.10 shows the simulated maximum flood inundation depth and inundation
areas for the August 1981 flood event in the Chitose River basin. As shown in Figure 4.10,
the simulated inundation area defined with a water depth threshold of 0.5 m overlaps withthe observed inundation area for the flood event in August 1981 in the downstream areaof the Chitose River basin. Owing to the limitations of the observed inundation data,
inundation depth data were not available. A comparison between the simulated andobserved inundation areas was conducted using the basic photo provided by the Hokkaido
Regional Development Bureau. The observed flood depth data were not available;
therefore, in this study, we selected the threshold of water depth is 0.2 and 0.5 m toclassify the inundated and non-inundated areas. According to the simulation results, thesimulated inundation areas are 252 and 240 km2 for the 0.2 and 0.5 m water depth
thresholds, respectively. Additionally, the observed inundation area was 192 km 2 [50].
The simulated inundation area for the threshold of 0.5 m water depth was more similar tothe observed ones compared with the simulated inundation area for the threshold of 0.2
m water depth. Additionally, the simulated inundation area and peak inundation depth for
the August 1981 flood event are quite close to the simulated results estimated by Oki etal. (2017) [50] of 209 km2 for the inundated area. Furthermore, the peak inundation depth
is similar to the value simulated by Oki et al. (2017) [50]. As a result, the threshold of 0.5
m water depth was chosen to classify the inundated and non-inundated areas in the
Chitose River basin in the following sections of this study.

89

Chapter 4

Figure 4.10. Comparison between the simulated and observed inundation areas for theflood event of August 1981 in the Chitose River basin
4.3.2. Evaluation of climate change impacts on river flood in the Ishikari River basin
The optimal parameters of the RRI model after calibration and validation were used
to evaluate the changes in severe river floods in the Ishikari River basin, as well as theflood inundation in the Chitose River basin using the top 20 and top 36 rainfall events
extracted from the d4PDF rainfall dataset as input data to the RRI model. The simulations
were conducted for an additional period of 72 h before the main period as an initialcondition for model warmup.
Changes in severe river flooding were examined at three stations along the main riverof the Ishikari River basin. The locations of the three stations are shown in Figure 4.1,
including the Osamunai station (located in the upstream river), Naieoohashi station(located in the middle river), and Ishikari Ohashi station (located in the downstream river).
From the simulation results at three stations along the main river, the percentage
differences in the means of the peak discharge between the top 20 and top 36 rainfall
events indicated that severe river floods are expected to increase by 21–24% in the target
basin. These results indicate that severe river flooding is projected to increase by a similar
extent throughout the entire Ishikari River basin. The greatest increase of 24% is observedat the Naieoohashi station, which is located in the middle of the Ishikari River.
90

Chapter 4
4.3.3. Evaluation of climate change impacts on flood inundation in the Chitose River
basin
Future changes in severe flood inundation were evaluated for the Chitose River basin.
To further understand the changes in the flood inundation risk in the Chitose River basin,
the flood inundation area, and flood inundation volume was evaluated for an inundation
depth equal to or larger than 0.5 and 3.0 m in this study. Figure 4.11 shows the inundation
area averaged for the top 20 rainfall events (historical simulation) and top 36 rainfall
events (future simulation) for flood depths equal to or larger than 0.5 (Figure 4.11a) and
3.0 m (Figure 4.11b). Table 4.3 shows the percentage difference of the mean values of theinundation areas and inundation volumes for an inundation depth ≥0.5 m and inundation
depth ≥3.0 m between the top 20 rainfall events (historical simulation) and the top 36
rainfall events (future simulation).
The results indicate that flood magnitudes are projected to increase significantly inthe future in the Chitose River basin. For flood depths ≥0.5 m, the inundation area, andinundation volume are expected to increase by 24.5 and 46.5%, respectively. As shownin Figure 4.11a, the downstream area of the Chitose River basin is the area with more
severe flood inundation in the future. In addition, flood inundation was observed on themain sub-basins of the Chitose River basin. Severe flood inundation events for flood
depths ≥3.0 m are expected to increase drastically in the Chitose River basin by 124.6 and134.2% for the inundation area and inundation volume, respectively. In particular, aninundation depth equal to or larger than 3.0 m was observed only in the downstream areaof the target basin. Owing to the low-lying characteristics, this area is the most vulnerable
area in the Chitose River basin. The results of the K–S tests of the inundation area withinundation depths equal to or larger than 0.5 and 3.0 m demonstrate significant differences
between the top 20 and top 36 rainfall events, with p-values of 0.0003 and 0.0001 (i.e.,much smaller than alpha of 0.05), respectively. A similar K–S test result for the inundation
volume with the inundation depths equal to or greater than 0.5 and 3.0 m also revealed
remarkable differences between the top 20 and top 36 rainfall events, with p-values of
0.0006 and 0.0001, respectively.

91

Chapter 4
Table 4.3. Percentage difference of the mean values of the inundation area and inundation
volume for inundation depths of ≥0.5 and ≥3.0 m between the top 20 rainfall events(historical simulation) and top 36 rainfall events (future simulation)
Inundation Area

Inundation Volume

Inundation depth ≥0.5 m

24.5

46.5

Inundation depth ≥3.0 m

124.6

134.2

Figure 4.11. Inundation area averaged for the top 20 rainfall events (historical simulation)and top 36 rainfall events (future simulation) with (a) inundation depth ≥0.5 m, and (b)inundation depth ≥3.0 m
Figure 4.12 shows the boxplots of the peak inundation depth (Figure 4.12a),inundation volumes with an inundation depth ≥0.5 m (Figure 4.12b), and inundation
volumes with an inundation depth ≥3.0 m (Figure 4.12c) for the top 20 past and top 36
future rainfall events in the Chitose River basin. In addition to the significant increase ininundation area and inundation volume, the peak inundation depth is predicted tosignificantly increase by a factor of 13.8% in the future. Notably, the peak inundation
depth can reach 8.45 m in the future in the Chitose River basin. This inundation depth isa very serious level of classification based on the risk categories for inundation depths inJapan. In addition, there was a huge variation in peak inundation depth in the Chitose92

Chapter 4River basin. The first quartile value of the peak inundation depth for the future simulationis 6.02 m, which was greater than the third quartile value for the historical simulation (6.0m). Similarly, the inundation volume with the flood depth ≥ 0.5 m for the future
simulation is 0.55 km3, which was equal to the third quartile value from the historicalsimulation. For the flood depth ≥3.0 m, the first quartile value for the inundation volume
is 0.13 km3, which is greater than the third quartile value from the historical simulationof 0.12 km3.
In addition, the median inundation volume with a flood depth ≥0.5 m is expected toincrease significantly by 52.9% in the Chitose River basin, whereas a drastic incrementof 330% was projected in the Chitose River basin for the flood depth ≥3.0 m. The K–S
test also shows a significant difference for the peak inundation depth between the top 20
past and top 36 future rainfall events at a significance level of 5% (p-value = 0.0001). A
similar K–S test result for the inundation volume with inundation depths equal to or
greater than 0.5 and 3.0 m also revealed remarkable differences between the top 20 and
top 36 rainfall events.

Figure 4.12. Boxplots of (a) peak inundation depths, (b) inundation volumes withinundation depth ≥0.5 m, and (c) inundation volumes with inundation depth ≥3.0 m forthe top 20 past and top 36 future rainfall events in the Chitose River basin

93Chapter 4
Figure 4.13 shows the spatial distribution of the inundation frequency for the top 20
past and top 36 future rainfall events, and the inundation frequency difference (IFD)
between these rainfall events. As shown in Figure 4.13a,b, the downstream area of the
Chitose River basin is clearly seen to be the most severe flood inundation. This result issuitable for the terrain characteristics of this basin. It can be seen that flood inundation is
likely to occur in this area in the future with a frequency of 90–100%. Moreover, the floodinundation was observed in the tributaries of the Chitose River for both historical andfuture simulations. This would increase the risk of flood inundation in the downstream
area of the Chitose River basin, in which the inland flooding integrated to the backwater
phenomenon from the main river. In addition, in Figure 4.13c, the IFD indicates a positive
value for almost all areas in the Chitose River basin. The proportion for the area with aninundation frequency smaller than 0 is only 4.2%. Therefore, 95.8% of the area had aninundation frequency larger than 0, in which the area proportional to 0 < IFD ≤ 0.3 is74.7%, and the area proportional to 0.3 ≤ IFD ≤ 0.6 is 21.1%. On the other hand, thisresult clearly indicates that the flood-prone areas downstream of the Chitose River basin
will be more widely spread in the future. Hence, this area should be specially consideredin the future to mitigate severe flood inundation damage.

94

Chapter 4

Figure 4.13. Spatial distribution of the inundation frequency averaged for (a) the top 20
rainfall events (historical simulation), (b) the top 36 rainfall events (future simulation),and (c) inundation frequency difference between the top 20 past and top 36 future rainfall
events.
In addition to evaluating the inundation area, inundation volume, peak inundation
depth, and inundation frequency, the future changes in inundation duration were also
investigated for the top 20 past and top 36 future rainfall events in this study. Figure 4.14
shows the spatial distribution of the inundation duration for the top 20 past and top 36
future rainfall events as well as for the difference between them. This study focused on
assessing the changes in extreme flood inundation during the short-term period (72 h).
Each event from the top 20 past and top 36 future rainfall events is the annual maximum
rainfall (mm/72 h) [23].
It can be seen that the longer durations of inundation were observed in thedownstream area of the Chitose River basin for both historical and future simulations.
The inundation in most tributaries lasts for a short duration of 0.03 to 10 h, whereas theinundation duration in the downstream area of the Chitose River basin lasts from 20 to 40
95

Chapter 4
h (approximately 1–2 days) for these rainfall events. Overall, the increment of floodinundation was observed at the tributaries and floodplain areas in the Chitose River basin.
The proportion of the area with an inundation duration difference of more than 0 h is
99.7% in the entire Chitose River basin, and only 0.3% of the areas will remain unchanged
in terms of inundation duration in the future. The results indicate that the inundation
duration is expected to increase by approximately 5–10 h in vulnerable areas in the
Chitose River basin, as shown in Figure 9c. Additionally, the results reveal that the areawith high inundation frequency corresponds to the long inundation duration and viceversa in this basin.

Figure 4.14. Spatial distribution of inundation durations averaged for (a) the top 20
rainfall events (historical simulation), (b) the top 36 rainfall events (future simulation),and (c) inundation duration differences between the top 20 past and top 36 future rainfall
events96

Chapter 4
4.3.4. Evaluation of time difference impacts on flood inundation in the Chitose Riverbasin
Owing to the topographical characteristics of its low-lying area, the Chitose Riverbasin is frequently affected by backwater from the main river. The basin has experienced
severe flood inundation events caused by extreme short-term rainfall events. The
backwater phenomenon integrated inland flooding, which has caused serious damage inthe past, was observed. When the water level increases at the confluence point, thebackwater phenomenon can occur in this basin. Furthermore, flood inundation is more
severe when the time difference is shorter.
Therefore, considering the dangerous impacts of the backwater phenomenon on floodinundation risk in the Chitose River basin, the differences between the time of peakdischarge at the reference station in the Chitose River basin and that of peak water levelat the confluence point intersecting the Ishikari River was investigated using the RRI
model. Figure 4.15 shows the frequency of the time differences in the Chitose River basin
for the top 20 past and top 36 future rainfall events. The results reveal that the timedifference has a large range from 0 to 50 h in the future, whereas the time difference fromthe historical simulation has a range from 0 to 30 h in the target basin. However, it can beobserved that the short time difference from 0 to 10 h is projected to significantly increasein the future. This result indicates that the flood inundation risk due to the backwater
phenomenon is likely to be more severe in the future in the Chitose River basin. In
addition, the results indicate that the time difference from 10 to 20 h does not change inthe future, whereas the time difference from 20 to 30 h is predicted to increase remarkably
in the future. These results will help policymakers enact proactive measurements tomitigate flood inundation damages in vulnerable areas in the target basin.

97

Chapter 4

Figure 4.15. Frequency of time difference in the Chitose River basin for the top 20 rainfall
events (historical simulation) and top 36 rainfall events (future simulation).
4.3.5. Discussions
This study investigated the impact of climate change on potential river flood hazards
along the Ishikari River basin, and in particular flood inundation risk in the Chitose Riverbasin, a tributary of the Ishikari River, using a large-ensemble rainfall dataset with a highresolution of 5 km d4PDF as input data to the RRI model. The RRI model performancewas examined by a comparison between the simulated and observed river discharges forfour large-scale flood events in the Ishikari River basin. In addition, although the floodinundation depth data were not available in the Chitose River basin, an attempt was madeto reproduce the greatest historical flood event of August 1981 in this basin. In addition
to appropriate river discharge simulation results, the simulated inundation results also
indicated that the flood inundation area was similar to the observed inundation area forthis flood event. In addition, the simulated results were close to the calculated floodinundation areas estimated by Oki et al. (2017) [50]. Therefore, this study indicated that
the RRI model could provide reasonable results not only for river discharge along the
Ishikari River basin but also flood inundation along the Chitose River basin.
The prediction results of future river floods from this study clearly indicated that theriver discharge in the Ishikari River basin was significantly affected by climate change
98Chapter 4
impacts. The extreme river flood is expected to increase significantly by 21–24% along
the Ishikari River basin. This result is in close agreement with several studies conductedon the Ishikari River basin. In a result of chapter 3, the river peak discharge was evaluatedin the Ishikari River basin, as well as in its main sub-basins using the IFAS model. The
river peak discharge is expected to increase by 33.3% in the entire of this basin. Duan etal. (2017) [57] evaluated the impacts of climate change on the hydroclimatology of theupper Ishikari River basin using the downscaled large-scale Hadley Centre Climate
Model 3 Global Circulation Model A2 and B2 scenarios data. They found out that theannual mean river discharge is predicted to increase for all three periods (2020–2039),(2050–2069), and (2080–2099), except the 2090s under the A2a scenario, and the largest
increase is approximately 7.56%. However, a study by Sato et al. [29] evaluated theimpacts of climate change on river discharge in several major river basins in Japan using
the super high-resolution atmospheric general circulation model (AGCM) with aresolution of 20 km for the present climate (1980–1999) and future climate (2080–2099).
The results showed that although the high temperature is projected to increase in the
Ishikari River basin, the monthly river discharge will not change significantly.
The Chitose River basin is the main tributary located in the downstream low-lying
area of Ishikari River. Hence, this river basin is considered to be a flood-prone area of the
Ishikari River basin. In the past, the Chitose River basin has experienced severe floodinundation events due to short-term heavy rainfall and caused serious damage. Therefore,
assessment of flood inundation risk in this basin is a crucial target for flood damage
mitigation. To the best of our knowledge, there have been no studies evaluating theimpacts of climate changes on flood inundation hazards in this basin area. As a result, thisstudy can be considered a pioneering work to comprehensively assess the impacts ofclimate changes on flood inundation risk in the Chitose River basin. According to thesimulation results, the flood inundation magnitude would be more severe in the ChitoseRiver basin in the future compared to the historical climate simulations. The inundation
magnitude is expected to increase significantly by 24.5, 46.5, and 13.8% for theinundation area, inundation volume, and peak inundation depth, respectively. In addition
to the investigation changes in the inundation area, inundation volume, and peak
inundation depth in this area, the inundation frequency, and inundation duration were99

Chapter 4
assessed for the historical and future simulations. The results indicate that the area withthe high inundation frequency corresponds to a long inundation duration and vice versa
in the target basin. The downstream area of this basin with low-lying characteristics is themost flood-prone area and should be specially considered to reduce flood damages in the
future.
In addition, compared to the results of chapter 3, chapter 4 is conducted to improve
the predicted time difference between the time of peak discharge at the reference stationin the Chitose River basin and that of peak water level at the confluence point intersecting
the Ishikari River using the RRI model. The RRI model uses a 1D diffusive wave for theriver channel and a 2D diffusive wave for the slope, which provides more reliable timedifference results in the Chitose River basin. The result indicated that the short timedifference from 0 to 10 h is predicted to increase significantly in this area. Hence, theflood inundation risk due to the backwater phenomenon is likely to be more severe in the
future.
4.4. Conclusions
This chapter presents the impacts of climate change on severe river discharges along
the Ishikari River basin and in particular the flood inundation in the Chitose River basin,a tributary of the Ishikari River, considering the extreme rainfall impacts and topographic
vulnerability. We used a large-ensemble rainfall dataset with a high resolution of 5 km
(d4PDF) as the input rainfall data for the RRI model. The following results were found:

Extreme river flooding in the Ishikari River basin is significantly affected by climatechanges. River discharge is expected to increase by 21–24% in this basin. The
greatest increase was observed in the middle of the Ishikari River.



The flood inundation is expected to be severe and higher in the Chitose River basin,with increments of 24.5, 46.5, and 13.8% for the inundation area, inundation volume,and peak inundation depth, respectively. The downstream area of this basin with lowlying characteristics is the most flood-prone area and should be specially considered
for minimizing flood damages in the future. This area is also likely to experience
flood inundation with a frequency of 90–100% in the future. The inundation duration
is expected to increase by approximately 5–10 h. In addition, the results indicate that100

Chapter 4
the area with high inundation frequency corresponds to long inundation durationsand vice versa in the Chitose River basin.
The predicted time difference is improved in the Chitose River basin using the RRI
model compared to that of the previous study. The short time difference from 0 to 10
h is predicted to increase significantly in this basin, which indicates that the floodinundation risk due to the backwater phenomenon is likely to be more severe in the
future in this basin.
Overall, these results are expected to provide useful information for river basin

management, particularly for climate change adaptation and flood damage mitigation infloodplain areas.
However, there still remains considerable uncertainty in this study. We focused on
assessing the changes in natural flood inundation risk in the target basin considering
extreme rainfall impacts and topographic vulnerability. Therefore, existing flood control
facilities such as dam operations, pump stations, and retarding basins were not
implemented. Furthermore, land-use activities as well as the socio-economic rapid
development were not considered. The impacts of these factors should be consideredalong the target basin in future studies as the rapid development would cause uncertainty
in the flood inundation prediction. In addition, the effectiveness of flood control facilities
should also be considered for flood inundation hazards in the Ishikari River basin as wellas in the Chitose River basin to obtain more reliable projections. This study was
conducted to assess changes in the future flood inundation hazards using the top 20 pastand top 36 future rainfall events (corresponding to return periods equal to or larger than
150 years) [23] as input rainfall data for the RRI model. Therefore, assessing the future
flood inundation risk based on yearly rainfall data for 3000 historical and 5400 futureclimate change scenarios extracted from the d4PDF dataset would provide more reliable
future projections in the target basin.

101

Chapter 4
4.5. References
1.

Intergovermental Panel on Climate Change (IPCC). Climate Change 2013: The
Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D.,
Plattner, G.-K., Tignor, M.M.B., Allen, S.K.; Boschung, J.; Nauels, A.; Xia, Y.; Bex,
V.,Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK; New York,NY, USA, 2013; Volume 3, pp. 154–196.
2. Wu, F.; Wang, X.; Cat, Y.; Li, C. Spatiotemporal analysis of precipitation trends
under climate change in the upper reach of Mekong River basin. Quat. Int. 2013, 392,
137–146. DOI:10.1016/j.quaint.2013.05.049.
3. Willems, P.; Vrac, M. Statistical precipitation downscaling for small-scalehydrological impact investigations of climate change. J. Hydrol. 2011, 402, 193–205.
DOI:10.1016/j.jhydrol.2011.02.030.
4. Ongdas, N.; Akiyanova, F.; Karakulov, Y.; Muratbayeva, A.; Zinabdin, N.
Application of HEC-RAS (2D) for Flood Hazard Maps Generation for Yesil (Ishim)
River in Kazakhstan. Water 2020, 12, 2672. DOI:10.3390/w12102672.
5. Hirabayashi, Y.; Kanae, S.; Emori, S.; Oki, T.; Kimoto, M. Global projections ofchanging risks of floods and droughts in a changing climate. Hydrol. Sci. J. 2008, 53,
754–772. DOI:10.1623/hysj.53.4.754.
6. Zenkoji, S.; Oda, S.; Tebakari, T.; Archevarahuprok, B. Spatial characteristics offlooded areas in the Mun and Chi River basins in Northeastern Thailand. J. DisasterRes. 2019, 14, 1337–1345. DOI:10.20965/jdr.2019.p1337.
7. Shrestha, B.; Okazumi, T.; Miyamoto, M.; Nabesaka, S.; Tanaka, S.; Sugiura, A.
Fundamental analysis for flood risk management in the selected river basins ofSoutheast Asia. J. Disaster Res. 2014, 9, 858–869. DOI:10.20965/jdr.2014.p0858.
8. Doocy, S.; Daniels, A.; Murray, S.; Kirsch, T.D. The human impact of floods: A
historical review of events 1980-2009 and systematic literature review. PLoS Curr.
2013, 5. DOI:10.1371/currents.dis.f4deb457904936b07c09daa98ee8171a.
9. Jonkman, S.N. Global perpectives on loss of human life caused by floods. Nat.
Hazards. 2005, 34, 151–175. DOI:10.1007/s11069-004-8891-3.
10. Try, S.; Tanaka, S.; Tanaka, K.; Sayama, T.; Lee, G.; Oeurng, C. Assessing theeffects of climate change on flood inundation in the lower Mekong Basin using highresolution AGCM outputs. Prog. Earth Planet. Sci. 2020, 7, 34.
DOI:10.1186/s40645-020-00353-z.
11. Yamamoto, K.; Sayama, T. Impact of climate change on flood inundation in a
tropical river basin in Indonesia. Prog. Earth Planet. Sci. 2021, 8, 5.
DOI:10.1186/s40645-020-00386-4.
12. Ashgar, M.; Ushiyama, T.; Riaz, M.; Miyamoto, M. Flood and inundation forecastingin the sparsely gauged transboundary Chenab River basin using satellite rain and102

Chapter 4
coupling meteorological and hydrological models. J. Hydrometeorol. 2019, 20,2315–2330. DOI:10.1175/JHM-D-18-0226.1.
13. Annis, A.; Nardi, F.; Petroselli, A.; Apollonio, C.; Arcangeletti, E.; Tauro, F.; Belli,C.; Bianconi, R.; Grimaldi, S. UAV-DEMs for Small-Scale Flood Hazard Mapping.
Water. 2020, 12, 1717. DOI:10.3390/w12061717.
14. Młyński, D.; Wałęga, A.; Ozga-Zielinski, B.; Ciupak, M.; Petroselli, A. New
approach for determining the quantiles of maximum annual flows in ungauged
catchments using the EBA4SUB model. J. Hydrol. 2020, 589, 125198.
DOI:10.1016/j.jhydrol.2020.125198.
15. Grimaldi, S.; Nardi, F.; Piscopia, R.; Petroselli, A.; Apollonio, C. Continuous
hydrologic modelling for design simulation in small and ungauged basins: A step
forward and some tests for its practical use. J. Hydrol. 2020, 125664.
DOI:10.1016/j.jhydrol.2020.125664.
16. Kumar, N.; Kumar, M.; Sherring, A.; Suryavanshi, S.; Ahmad, A.; Lal, D.
Applicability of HEC-RAS 2D and GFMS for flood extent mapping: A case study ofSangam area, Prayagraj, India. Model Earth Syst. Environ. 2020, 6, 397–405.
DOI:10.1007/s40808-019-00687-8.
17. Yamada, T.J. Adaptation Measures for Extreme Floods Using Huge Ensemble ofHigh-Resolution Climate Model Simulation in Japan, Summary Report on the
Eleventh Meeting of the Research Dialogue. United Nations Framework Convention
on Climate Change ( U N F C C C ) , B o n n , G e r m a n y 2019, 28–29. Available
online:https://unfccc.int/sites/default/files/resource/RD11SummaryReport.pdf(accessed on 8 April 2020).
18. Japan Meteorological Agency. Climate Change Monitoring Report 2017. Available
online: https://www.jma.go.jp/jma/en/NMHS/ccmr/ccmr2017_high.pdf (accessedon 20 December 2020).
19. Shakti, P.; Nakatani, T.; Misumi, R. Hydrological simulation of small river basins inNorthern Kyushu, Japan, during the extreme rainfall event of July 5–6, 2017. J.
Disaster Res. 2018, 13, 396–409. DOI:10.20965/jdr.2018.p0396.
20. River Council for Social Infrastructure Development. Flood Risk Management for
Wide-Area and Long-Lasting Rainfall–Multi-Layered Countermeasures forComplex
Disasters
2018.
Available
online:https://www.mlit.go.jp/river/mizubousaivision/toushin_e/1812_flood_risk_manage
ment_e.pdf (accessed on 20 December 2020).
21. Japan Society of Civil Engineers (JSCE). Onsite Field Investigation Team, Site–
Investigation Report for the Hokkaido Heavy Rainfall Disasters in August 2016; Title
was translated by N. Kimura; JSCE Report; JSCE: Tokyo, Japan, 2017. (In Japanese).
22. Shakti, P.; Hirano, K.; Iizuka, S. Flood inundation mapping of the Hitachi region in
the Kuji River basin, Japan, during the October 11–13, 2019 extreme rainfal event.
J. Disaster Res. 2020, 15, 712–725. DOI:10.20965/jdr.2020.p0712.
103Chapter 4
23. Nguyen, T.T.; Nakatsugawa, M.; Yamada, T.J.; Hoshino, T. Assessing climatechange impacts on extreme rainfall and severe flooding during the summer monsoon
season in the Ishikari River basin, Japan. Hydrol. Res. Lett. 2020, 14, 155–161.
DOI:10.3178/hrl.14.155.
24. Tachikawa, Y.; Miyawaki, K.; Tanaka, T.; Yorozu, K.; Kato, M.; Ichikawa, Y.; Kim,
S. Future change analysis of extreme floods using large ensemble climate simulation
data. J. Jpn. Soc. Civ. Eng. Ser. B1 (Hydraul. Eng.) 2017, 73, 77–90.
DOI:10.2208/jscejhe.73.77 (In Japanese).
25. Tanaka, T.; Tachikawa, Y.; Ichikawa, Y.; Yorozu, K. Impact assessment of upstream
flooding on extreme flood frequency analysis by incorporating a flood-inundation
model for flood risk assessment. J. Hydrol. 2017, 554, 370–382.
DOI:10.1016/j.jhydrol.2017.09.012.
26. Sakaguchi, S.; Nakayama, K.; Kobayashi, K.; Komai, K. Inundation analysis using
coupling storage function model with a distributed hydrological model in Kushiro
Marsh, Japan. Hydrol. Res. Lett. 2020, 14, 75–80. DOI:10.3178/hrl.14.75.
27. Bates, B.C.; Kundzewicz, Z.W.; Wu, S.; Palutikof, J.P. Climate Change and Water;
IPCC Technical Paper IV; Intergovernmental Panel on Climate Change: Geneva,
Switzerland, 2008.
28. Li, G.; Zhang, X.; Zwiers, F.; Wen, Q.H. Quantification of uncertainty in highresolution temperature scenarios for North America. J. Clim. 2012, 25, 3373–3389.
DOI:10.1175/JCLI-D-11-00217.1.
29. Sato, Y.; Kojiri, T.; Michihiro, Y.; Suzuki, Y.; Nakakita, E. Estimates of climatechange impact on river discharge in Japan based on a super-high-resolution climate
model.
Terr.
Atmos.
Ocean.
Sci.
2012,
23,
527–540.
DOI:10.3319/TAO.2012.05.03.02(WMH).
30. Yamada, T.J.; Hoshino, T.; Masuya, S.; Uemura, F.; Yoshida, T.; Omura, N.;
Yamamoto, T.; Chiba, M.; Tomura, S.; Tokioka, S.; et al. The influence of climatechange on flood risk in Hokkaido. Adv. River Eng. 2018, 24, 391–396. (In Japanese).
31. Sasaki, H.; Murada, A.; Hanafusa, M.; Oh’izumi, M.; Kurihara, K. Reproducibilityof present climate in a non-hydrostatic regional climate model nested within an
atmosphere general circulation model. SOLA. 2011, 7, 173–176.
DOI:10.2151/sola.2011-044.
32. Scharffenberg, W. Hydrological Modeling System HEC-HMS; User’s Manual;Publication of US Army Corps of Engineers: Davis, CA, USA, 2016.
33. International Centre for Water Hazard and Risk Management (ICHARM). IFAS ver
2.0
Technical
Manual2014.
Available
online:http://www.icharm.pwri.go.jp/research/ifas/ifas_2.0_top.html (accessed on 20
October 2018).
34. Beven, K.J.; Lamb, R.; Quinn, P.F.; Romanowics, R.; Freer, J. Top model. In
Computer Models of Watershed Hydrology; Singh, V.P., Ed.; Water Resources
Publications: Littleton, CO, USA, 1995; pp. 627–668.
104

Chapter 4
35. Arnold, J.G.; Srinivasan, R.; Muttiah, R.S.; Williams, J.R. Large area hydrologic
modeling and assessment part I: Model development. JAWRA J. Am. Water Resour.
Assoc. 1998, 34, 73–89. DOI:10.1111/j 1752-1688.1998.tb05961.x.
36. Brunner, G. HEC-RAS River Analysis System: Hydraulic Reference Manual, Version
5.0.; US Army Corps of Engineers Hydrologic Engineering Center (HEC): Davis,
CA, USA, 2016; pp. 1–538.
37. FLO-2D Software Incorporated. FLO-2D Reference Manual; FLO-2D Software
Incorporated: Nutrioso, AZ, USA, 2009.
38. DHI. User Manual. MIKE 21 Flow Model; DHI: Hørsholm, Denmark, 2016; pp. 12–
14.
39. Deltares. User Manual. SOBEK. Hydrodynamics, Rainfall-Runoff and Real-Time
Control, 1D/2D Modelling Suite for Integrated Water Solutions; Deltares: Delft, The
Netherlands, 2019; pp 1–3.
40. Sayama, T.; Fukami, K.; Tanaka, S.; Takeuchi, K. Rainfall-runoff-inundation
analysis for flood risk assessment at the regional scale. In Proceedings of the 5th
Conference of Asia Pacific Association of Hydrology and Water Resources (AHPW),
Hanoi, Vietnam, 8–10 November 2010; pp. 568–576.
41. Sayama, T.; Ozama, G.; Kawakami, T.; Nabesaka, S.; Fukami, K. Rainfall-runoffinundation analysis of the 2010 Pakistan flood in the Kabul River basin. Hydro. Sci.
J. 2012, 57, 298–312. DOI:10.1080/02626667.2011.644245.
42. Sayama, T.; Tatebe, Y.; Iwami, Y.; Tanaka, S. Hydrologic sensitivity of flood runoff
and inundation: 2011 Thailand floods in the Chao Phraya River basin. Nat. Hazards
Earth Syst. Sci. 2015, 15, 1617–1630. DOI:10.5194/nhess-15-1617-2015.
43. San, Z.; Zin, W.; Kawasaki, A.; Acierto, R.; Oo, T. Developing flood inundation map
using RRI and SOBEK models: A case study of the Bago River basin, Myanmar. J.
Disaster Res. 2020, 15, 277–287. DOI:10.20965/jdr.2020.p0277.
44. Try, S.; Lee, G.; Yu, W.; Oeurng, C.; Jang, C. Large-scale flood-inundation modelingin the Mekong River basin. J. Hydrol. Eng. 2018, 23, 05018011.
DOI:10.1061/(ASCE) HE.1943-5584.0001664.
45. Try, S.; Tanaka, S.; Tanaka, K.; Sayama, T.; Oeurng, C.; Uk, S.; Tanaka, K.; Hu, M.;
Han, D. Comparison of various gridded precipitation datasets for rainfall-runoff andinundation modeling in the Mekong River basin. PLoS ONE 2020, 15, e0226814.
DOI:10.1371/journal.pone.0226814.
46. Try, S.; Tanaka, S.; Tanaka, K.; Sayama, T.; Hu, M.; Sok, T.; Oeurng, C. Projectionof extreme flood inundation in the Mekong River basin under 4 K increasing scenario
using large ensemble climate data. Hydrol. Process. 2020, 34, 4350–4364.
DOI:10.1002/hyp.13859.
47. Shrestha, B.; Kawasaki, A. Quantitative assessment of flood risk with evaluation of
the effectiveness of dam operation for flood control: A case of the Bago River basinof Myanmar. Int. J. Disaster Risk Reduct. 2020, 50, 101707.
DOI:10.1016/j.ijdrr.2020.101707.
105

Chapter 4
48. Shakti, P.C.; Kamimera, H.; Misumi, R. Inundation analysis of the Oda River basin
in Japan during the flood event of 6-7 July 2018 utilizing local and globalhydrographic data. Water 2020, 12, 1005. DOI:10.3390/w12041005.
49. Japan River Association. The Ishikari River 2003. Available online:
http://www.japanriver.or.jp/EnglishDocument/DB/file/002%20Hokkaido%2003.pd
f (accessed on 20 May 2019).
50. Oki, T.; Nakatsugawa, M. A flood analysis model for rivers in low-lying areas that
integrates inland water and backwater. In Proceedings of the 37th IAHR World
Congress, Kuala Lumpur, Malaysia, 13–18 August 2017.
51. Hunter, N.M.; Bates, P.D.; Horritt, M.S.;Wilson, M.D. Simple spatially-distributedmodels for predicting flood inundation: A review. Geomorphology 2007, 90, 208–
225.
52. Khaing, Z.M.; Zhang, K.; Sawano, H.; Shrestha, B.B.; Sayama, T.; Nakamura, K.
Flood hazard mapping and assessment in data-scarce Nyaungdon area, Myanmar.
PLoS ONE 2019, 14. DOI:10.1371/journal.pone.0224558.
53. Lehner, B.; Verdin, K.; Jarvis, A. New Global Hydrography Derived FromSpaceborne Elevation Data. Eos. Trans. Agu 2008, 89, 93–94.
DOI:10.1029/2008EO100001.
54. Ministry of Land, Infrastructure, Transport, and Tourism in Japan (MLIT Japan).
Water Information System. Available online: http://www1.river.go.jp/ (accessed on
21 October 2020).
55. Mizuta, R.; Murata, A.; Ishii, M.; Shiogama, H.; Hibino, K.; Mori, N.; Arakawa, O.;
Imada, Y.; Yoshida, K.; Aoyagi, T.; et al. Over 5000 years of ensemble future climate
simulations by 60-km global and 20-km regional atmospheric models. Bull. Am.
Meteorol. Soc. 2016, 98, 1383–1398. DOI:10.1175/BAMS-D-16-0099.1.
56. Taylor, K.E.; Stouffer, R.J.; Meehl, G.A. Overview of CMIP5 and the Experimental
Design. Bull. Am. Meteorol. Soc. 2012, 93, 485–498.
57. Duan, W.; He, B.; Takara, K.; Luo, P.; Nover, D.; Hu, M. Impacts of climate changeon the hydro-climatology of the upper Ishikari river basin, Japan. Environ. Earth Sci.
2017, 76, 490. DOI:10.1007/s12665-017-6805-4.

106Chapter 5
Conclusions and Suggestions

Chapter 5
5.1. Introduction
This chapter presents the findings obtained from this study and some suggestions forfurther study.
5.2. Summarize of the study findings
Recently, river flooding due to short-term extreme rainfall has occurred frequently inJapan, and Hokkaido island located in the Northern part of Japan is not an exception. In
recent years, Hokkaido has been continuously exposed to many typhoons and extreme
rainfall caused severe floods. For example, the severe flood disaster in August 2016.
Serious damage was recorded for this severe flood event in Hokkaido, Japan. These
disasters are considered to be related to the impacts of climate change. Therefore,
adaptation to climate change impacts, as well as minimize severe flood damage should
be considered in this region. This study aims to investigate the impact of climate change
in extreme rainfall, as well as severe river floodings for the Ishikari River basin, the mostsocioeconomically important basin in Hokkaido, Japan. In addition, the climate change
impacts on severe flood inundation were assessed in the Chitose River basin, thedownstream lowland area of the Ishikari River basin. This basin is considered to be themost flood-prone in the Ishikari River basin. Owing to the topographical characteristics
of its low-lying area, the Chitose River basin is frequently affected by the backwater fromthe Ishikari River and experiences severe flood inundation. This study is conducted using
the IFAS model, the RRI model, and the rainfall data with a super high resolution of 5 km
as input data to these models. The remarkable point of this study is to use the largeensemble and super high-resolution climate simulations for extreme rainfall and severe
floods assessment. It could predict extreme rainfall as well as severe flood events withreturn periods equal to or larger than 100 years or more. In addition, simulation with large
ensemble members could reduce the uncertainty in the estimation of the probability of
extreme events. The following results were found:

Following validation, the IFAS model can provide reasonable simulations of riverdischarges in the Ishikari River basin.

Severe rainfall and river flooding events are expected to increase significantly in the
Ishikari River basin and in its main sub-basins in the future. Additionally, the river107

Chapter 5
flooding resulting from extreme rainfall would increase to a greater extent than theincrease in extreme rainfall in the Ishikari River basin and in its main sub-basins.
The effect of climate change is significant in the sub-basins located in the southernpart of the Ishikari River basin, where extreme rainfall is expected to increase by
29%-35%, whereas the river discharge is likely to increase drastically by 37%-56%.
The difference between the time of peak discharge at the reference station and thetime of peak water level at the confluence point in the main river is expected todecrease by 1.02 h to 2.14 h in these regions.
Special attention should be paid to the Chitose River basin, as it is located in alowland area of the Ishikari River basin that is prone to flood damage.



Extreme river flooding in the IRB is significantly affected by climate changes. River
discharge is expected to increase by 21–24% in the Ishikari River basin. The greatest
increase was observed in the middle of the Ishikari River.



The flood inundation is expected to be severe and higher in the Chitose River basin,with increments of 24.5, 46.5, and 13.8% for the inundation area, inundation volume,and peak inundation depth, respectively. The downstream area of the Chitose Riverbasin with low-lying characteristics is the most flood-prone area and should bespecially considered for minimizing flood damages in the future. This area is also
likely to experience flood inundation with a frequency of 90–100% in the future.
The inundation duration is expected to increase by approximately 5–10 h. In
addition, the results indicate that the area with high inundation frequency
corresponds to long inundation durations and vice versa in the Chitose River basin.

The short time difference from 0 to 10 h is predicted to increase significantly in the
Chitose River basin, which indicates that the flood inundation risk due to thebackwater phenomenon is likely to be more severe in the future in this basin.

Using large-ensemble and high-resolution climate simulations could reduce theuncertainty in the estimation of extreme weather events caused by climate change
impacts. Therefore, this method could be applied for assessing extreme rainfall andflood inundation for other river basins, in particularly in floodplain areas in theworld to mitigate flood damage.
Overall, these results are expected to provide useful information for river basin
108Chapter 5
management, particularly for climate change adaptation and flood damage mitigation infloodplain areas.
However, there still remains considerable uncertainty in this study. We focused on
assessing the changes in natural flood inundation risk in the target basin considering
extreme rainfall impacts and topographic vulnerability. Therefore, existing flood control
facilities such as dam operations, pump stations, and retarding basins were not
implemented. Furthermore, land-use activities as well as the socio-economic rapid
development were not considered. The impacts of these factors should be consideredalong the target basin in future studies as the rapid development would cause uncertainty
in the flood inundation prediction. In addition, the effectiveness of flood control facilities
should also be considered for flood inundation hazards in the Ishikari River basin as wellas in the Chitose River basin to obtain more reliable projections. This study was
conducted to assess changes in the future flood inundation hazards using the top 20 pastand top 36 future rainfall events (corresponding to return periods equal to or larger than
150 years) as input rainfall data for the RRI model. Therefore, assessing the future floodinundation risk based on yearly rainfall data for 3000 historical and 5400 future climatechange scenarios extracted from the d4PDF dataset would provide more reliable future
projections in the target basin.
5.3. Proposing flood mitigation measures
The flood disaster control measures include the “Structural measures” and “Nonstructural measures”. The structural measures such as dam, diversion channel, drainage
pump. And the non-structural measures such as human activities and awareness, earlyflood warning system, legal regulations. This study evaluated change in extreme rainfall
and severe floods considering climate change impacts for the return period equal to or
higher than 150 years, high-risk disasters. The construction of structural measures likedams, diversion channels will take a long time to complete. Therefore, to minimize flood
damage effectively and comprehensively in the future in the target basin, this study
proposes non-structural measures combined with suitable structural measures. These
measures is also suitable for other floodplain areas, especially for agricutural areas. Someof our proposals were presented as below:
109Chapter 5
Structural measures:

Maintaining and improving the function of retarding basin, levee, and other flood
control facilities to ensure its best efficiency to mitigate flood risk in the future.

Land treatment
Hokkaido island is the largest agricultual field in Japan. Therefore, this method canbe used to mitigate flood risk in the future. The agricultural areas can keep morewater during the flood season. Moreover, this method could help purify water, protect
the natural environment.
Besides the agricutural field, the other public works like parks, building, square, carpark and road, we could develop the plant system to help more water infiltrated tosoil when the flood season happens. This method is combined to urban landscape.
We believe that this method will be useful and suitable not only in the target basin
but also entire Hokkaido island.

Figure 5.1. Land treatment for agricultural areas

110Chapter 5

Figure 5.2. Structural measures for mitigation flood damage in public works
combined to urban landscapeIn addition, there are many available dams not only in the target basin but also entire
Hokkaido. Figure 5.3 shows the available dams in Hokkaido, and in the Ishikari Riverbasin. We believe that improving the function of dams, especially the function offlood control for all available dams is the most effective method to redure flood
damage in the target basin in the future.

111

Chapter 5

Figure 5.3. Dams in Hokkaido island
Non-structural measures:

Raising community awareness in flood prevention.



Based on the future prediction results, developing the early flood warning system.

The residents located in vulnerable areas with high-risk floods need to be relocatedto more safe areas.



Developing national regulations prohibiting living in severe flood-prone areas.



Developing national flood insurance programs.



Promote researches on rare extreme weather events for future climate change
adaptation.



Developing flood hazard mapping.
These measures could be considered for implementation to mitigate flood damage invulnerable areas considering climate change impacts in the future.

112

Chapter 5
5.4. Suggestions for future researches
For future studies, we will evaluate the impacts of climate change on severe floods
considering some aspects below to obtain reliable prediction results in the target basin.


Change in land-use activities should be considered along the target basin in futurestudies as the rapid development of land-use change would cause uncertainty in theflood inundation prediction.

The effectiveness of existing flood control facilities such as dam operations, pumpstations, and retarding basins should also be considered for flood inundation hazards
in the target basin to obtain more reliable projections.



In addition, the important task is to assess the future flood inundation risk based on
yearly rainfall data for 3,000 historical and 5,400 future climate change scenarios
extracted from the d4PDF dataset would provide more reliable future projections in
the Ishikari River basin as well as in the Chitose River basin.

113

Appendix

Appendix
Journals[1] Nguyen, T.T.; Nakatsugawa, M.; Yamada, T.J.; Hoshino, T. Assessing climatechange impacts on extreme rainfall and severe flooding during the summer
monsoon season in the Ishikari River basin. Japan. Hydrol. Res. Lett. 2020, 14,
155-161, DOI: 10.3178/hrl.14.155.
[2] Nguyen, T.T.; Nakatsugawa, M.; Yamada, T.J.; Hoshino, T. Flood inundationassessment in the low-lying river basin considering extreme rainfall impacts andtopographic vulnerability. Water. 2021, 13, 896-908. DOI: 10.3390/w13070896

Proceedings and Conferences[1] Nguyen, T.T.; Nakatsugawa, M.; Yamada, T.J.; Hoshino, T. Flood risk analysis for
the Ishikari River considering rainfall patterns using downscaled d4PDF data.
Proceedings of the 22nd IAHR-APD Congress 2020, Sapporo, Japan.

114