International Hydrological Programme

Risk Management of Water-related Disasters
under Changing Climate
The Twenty-fifth IHP Training Course
30 November - 11 December, 2015
Kyoto, Japan

Water Resources Research Center, Disaster Prevention Research Institute,
Kyoto University
Hydrospheric Atmospheric Research Center, Nagoya University
Supported by
Disaster Prevention Research Institute, Kyoto University

Outline
A two-week training course on risk management of water-related disasters under changing
climate is programmed for participants from Asian-Pacific regions as a part of Japanesecontribution to the International Hydrological Program (IHP). The course composed of a seriesof lectures, practice sessions, and technical visits to the Yodo River Basin will be held mainly
at the Disaster Prevention Research Institute (DPRI), Kyoto University during the two weeks
from 30 November to 11 December 2015.
Objectives
The number of human losses and economic damages linked to human practices has been
exacerbated by water-related extreme events. Water-related risk might further increase for anumber of reasons. The probability of extreme events which cause high impacts to society is
expected to increase because of human activities and/or as a result of climate variability andchange. On the other hand, increasing population and economic growth lead to intensive
urbanization, often in flood prone areas. Frequent disaster will prevent from developing or
exhaust society. Poor water governance coupled with lack of adequate emergency
management institutions and infrastructures reduces society’s capacity to cope with extreme
events and therefore increases the risk to life and property. In order to realize sustainable
development, appropriate risk management of water-related disasters is indispensable.
In light of the Focal Area 1.1 “Risk management as adaptation to global change” under
the Theme 1 “Water related disasters under hydrological change” of the IHP-VIII, the 25th
IHP training course is focused on three major objectives: (1) to acquire the latest knowledge
on risk management on water-related disasters under changing climate at river basin scale,(2) to make practice on methodologies for risk assessment, and (3) to discuss alternatives ofrisk management at river basin scale.
Dates
30 November to 11 December, 2015
Venue
Disaster Prevention Research Institute, Kyoto University, Uji, Japan
Conveners
Convener: TANAKA, Shigenobu (Disaster Prevention Research Institute, Kyoto University)
Chief assistant: NOHARA, Daisuke (Disaster Prevention Research Institute, Kyoto University)iLectures
Keynote 1

Resilience and urban floods management strategies

P. Gourbesville

Keynote 2

Water-related disaster risk, resilience and building back better

Keynote 3

Role of data in flood modeling, flood management and Pakistan projectLecture 1

Projected future meteorological environment - Heading to adaptation strategy E. Nakakita

Lecture 2

Fundamentals of basin-scale hydrological analysis

Y. Tachikawa

Lecture 3

Fundamentals in rainfall-runoff-inundation modelling

T. Sayama

Lecture 4

Data Integration and Analysis System (DIAS) for water-related disasters

Lecture 5

Fundamentals in flood frequency analysis

Lecture 6

Efforts to develop disaster statistics in the world

Y. OnoLecture 7

Fundamentals in river basin modelling

Y. SatoLecture 8

Fundamentals in optimum operation of reservoir systems

T. Hori

Lecture 9

Wadi flash floods risk management under changing climate in the arid regions
S.A. Kantoush

Lecture 10

Flood risk assessment toward flood risk management

H. TatanoLecture 11

Integrated sediment and floating debris management

T. SumiK. Takeuchi
S. KhanA. Kawasaki
S. Tanaka

Practices
Exercise 1

Self-introduction and country report on risk management of water-related disasters(All participants)

Exercise 2

Fundamentals of data processing

Exercise 3

Data analysis of GCM and historical data

K. Tanaka

Exercise 4

Flood frequency analysis

S. Tanaka

Exercise 5

Rainfall-runoff-inundation modelling

T. Sayama

Exercise 6

Optimization of reservoir operation

D. Nohara

T. Hamaguchi

Technical visit and field workshop
Lake Biwa, Katsura River, Yodo River and Hiyoshi Dam

ii

Schedule (30 November to 11 December)
Date

Time

Lectures/exercises

9:30 – 11:00

Opening, Registration & Guidance

30 November

11:15 – 12:30

Exercise 1(by each participant)(Monday)

14:00 – 16:00

Keynote 1

by P. Gourbesville

16:30 – 18:00

Welcome party (@ Room S-217D)

9:00 – 10:30

Keynote 2

by K. Takeuchi

10:45 – 12:15

Keynote 3

by S. Khan

14:00 – 16:30

Exercise 2

by T. Hamaguchi

9:30 – 12:00

Lecture 1

by E. Nakakita

13:00 – 13:30

Short Lecture

14:00 – 16:30

Lecture 2

by Y. Tachikawa

9:00 – 12:00

Lecture 3

by T. Sayama

13:30 – 15:15

Lecture 4

by A. Kawasaki

15:30 – 17:15

Lecture 5

by S. Tanaka

9:30 – 12:00

Exercise 3

by K. Tanaka

13:30 – 15:15

Lecture 6

by Y. Ono

15:30 – 17:15

Exercise 4

by S. Tanaka

1 December(Tuesday)2 December(Wednesday)
3 December(Thursday)
4 December(Friday)
5 December(Saturday)
6 December(Sunday)

9:30 – 17:15
10:00 – 17:00

by M. Suzuki & T. Goto (JICA)(Technical visits to the Lake Biwa and the Uji Riverby Y. Takemon & D. Nohara)(Technical visits and cultural exchange at the Kamo Riverand the Biwako Canal)

7 December

9:30 – 12:00

Exercise 5

by T. Sayama(Monday)

14:00 – 16:30

Lecture 7

by Y. Sato

8 December

9:30 – 12:00

Lecture 8

by T. Hori

(Tuesday)

14:00 – 16:30

Exercise 6

by D. Nohara

9 December(Wednesday)
10 December(Thursday)
11 December(Friday)

8:45 – 17:00

Field workshop at the Hiyoshi Dam and the Katsura River
by S. Tanaka & D. Nohara

9:00 – 11:30

Lecture 9

by S.A. Kantoush

13:00 – 15:00

Lecture 10

by H. Tatano

15:15 – 17:15

Lecture 11

by T. Sumi

9:30 – 11:00

Report presentation

11:00 – 11:30

Completion ceremony

11:30 – 13:00

Farewell partyiii

by each participant(@ Room S-217D)

Index of Lecture Materials
Keynote 1

Resilience and urban floods management strategies
Philippe GOURBESVILLE

1

Keynote 2

Water-related disaster risk, resilience and building back better
Kuniyoshi TAKEUCHI

11

Keynote 3

Role of data in flood modeling, flood management and Pakistan project
Shahbaz KHAN

19

Lecture 1

Projected future meteorological environment– Heading to adaptation strategy –Eiichi NAKAKITA

29

Lecture 2

Fundamentals of basin-scale hydrological analysis
Yasuto TACHIKAWA

47

Lecture 3& Exercise 5

Fundamentals in rainfall-runoff-inundation modeling
Rainfall-runoff-inundation modelling
Takahiro SAYAMA

83

Lecture 4

Data Integration and Analysis System (DIAS) for water-related disasters
Akiyuki KAWASAKI

93

Lecture 5& Exercise 4

Fundamentals in flood frequency analysis
Flood frequency analysis
Shigenobu TANAKA

105

Lecture 6

Efforts to develop disaster statistics in the world
Yuichi ONO

121

Lecture 7

Fundamentals in river basin modeling
Yoshinobu SATO

129

Lecture 8

Fundamentals in optimum operation of reservoir systems
Tomoharu HORI

137

Lecture 9

Wadi flash floods risk management under changing climate in the arid
regions
Sameh Ahmed KANTOUSH

147

Lecture 10

Flood risk assessment toward flood risk management
Hirokazu TATANO

161

Lecture 11

Integrated sediment and floating debris management
Tetsuya SUMI

163

iv

Exercise 2

Fundamentals of data processing
Toshio HAMAGUCHI181

Exercise 3

Data analysis of GCM and historical data
Kenji TANAKA

191

Exercise 6

Optimization of reservoir operation
Daisuke NOHARA

203

Technical Visits Brief guide of the Lake Biwa and the Uji River for Technical Visits on 5
December (Sat.)
Yasuhiro TAKEMON

211

Field Workshop

217

Overview of the Field Workshop at the Hiyoshi Dam and the Katsura(Hozu) River (on 9 December)
Daisuke NOHARAv

LECTURE MATERIALSKeynote Lecture 1:"Resilience and urban floods management strategies"
Philippe GOURBESVILLE (Professor, Polytech Nice Sophia & Innovatice CiTy lab, Nice Sophia
Antipolis University, France)
Abstract: Floods that happen in urban areas are governed by increased frequency. Existing flood defence
structures demonstrate its downsides. That signifies that primary protection regarding flood is notsustainable anymore. The solution is moving to risk culture and finding the balance between the shape ofland use and urbanization through adaptation, mitigation, prevention, response and recovery strategies.
Sensitivity analysis of urban systems implies a deeper investigation of urban flooding, condition of thebuilt environment, way of mapping built environment, its interaction with nature and vulnerability
evaluation. Further to that, a resilience approach to solve and assess the sensitivity of urban systems in
respect to flooding becomes crucial. The traditional approaches promote the struggle against the water
through implementation of structural measures while resilience approach underline flood friendly
approach through implementation of non-structural measures that are more adaptable to increased flood
frequency. Resilience approach divides responsibility to governmental and community level regarding
urban flood risk management. It also considers the resilience of a population to floods and it’s measured
with time. The structure of urban system interpreted through functional analysis offer the possibility toevaluate resilience of each element of urban system as well as overall resilience. Assessing resilience ofurban functions and services provide improved identification of „hot spots‟ and efficient recommendationof possible flood management strategies. An urban system is defined within city boundaries. The shape ofurban systems is changing over time since urbanization is also a dynamic process. Change of urban
systems over time does not imply just physical change of landscape. The change of system in social
aspect is significant as well e.g. population density. Scaling of urban system allows being able torecognize main urban patterns. Common for each urban system is to have the necessary elements in order
to be able to function. In order to break down the structure of urban pattern it is necessary to map systemelements to physical components, map the elements to systems requirements. This allows listing allnecessary tasks that urban system is performing. Physical components of the systems are urban functions
and services. Physical components of urban system are buildings, streets, parks, water distribution
network, shops, industrial buildings, electricity network, religion areas, etc. Some of them represent assetsthat the city needs to have in order to perform while others provide connections between different systemcomponents. Urban functions of a city are defined as physical components that urban system need toprovide as fundamental needs to residents.
Urban resilience can be characterized through a Flood Resilience Index (FRI) that integrates fivedimensions: natural, physical, economical, social and institutional. Within each dimension the set of-1-

major indicators can be chosen. The set of indicators or variables is taken as a substitute because it is very
difficult to quantify resilience in relative terms. The indicators are chosen according to the following
criteria: Sensitivity, Availability, Affordability and Relevance. The methodology is set to take into accountdifferent spatial scales. The evaluation of the Flood Resilience Index (FRI) on parcel/building and theblock scale focuses on urban function. The evaluation of FRI for the city and district/block scale is done
through five dimensions: natural, physical, social, economic and institutional.
Analysis of urban resilience regarding flooding processes opens the door to a new approach regarding risk
management in urban environments. At the same time, the approach clearly underlines the need toredefine urban environments in a more integrated way able to conciliate various objectives at the properscale for the inhabitants.

-2-

Resilience and urban floods management
strategiest t iResilience and urban floods management
strategiest t iPhilippe GOURBESVILLE

Philippe GOURBESVILLE

Resilience and urban floods management
strategiest t iResilience and urban floods management
strategiest t iPhilippe GOURBESVILLE

Philippe GOURBESVILLE

What are we talking about?
Earthquake + Tsunami, Japan, 2011

What are we talking about?
Sandy storm, USA, 2012ooIncreased vulnerabilityy due to urbanization and land use changesg
Alger tragedy (Algeria ‐ November, 2001)
o 800 victims
o a single
i l extremetrainfalli f ll eventt (250 mm / 2 hhours))

Babel Oued

ALGER
Resilience & urban flood management strategies Ph.Gourbesville-3-

Resilience & urban flood management strategies Ph.GourbesvilleWhat are we talking about?

What are we talking about?
• Structural protection measures doesn’t avoid disasters …

Development
pof structural measures & awarenessJapan, 2011

Example: flood protection wall in Nashville ($ 100 M ‐ 2015)

Resilience & urban flood management strategies Ph.Gourbesville

What are we talking about?

Resilience & urban flood management strategies Ph.Gourbesville

What are we talking about?

• Structural protection measures doesn’t avoid disasters …
• Extreme events have underlined vulnerability of modern societies &unpreparedness
j impactp
on urban environments which have a highg level of• A Major
vulnerability due to sophisticate & complex infrastructures…

Bangladesh,l d h 2015

Japan, 2011

Resilience & urban flood management strategies Ph.Gourbesville

What are we talking about?

Thailand, 2011
Resilience & urban flood management strategies Ph.Gourbesville

Reviewing concepts: the Water Cycle• A need for reviewing protection approach (and engineering design)• Is protection the only way? Clearly no because 100% safety will be never achieved• Reviewing engineering concepts… what if?

Japan, 2011

Thailand, 2011
Resilience & urban flood management strategies Ph.Gourbesville-4-

Resilience & urban flood management strategies Ph.Gourbesville

Reviewing concepts: the Real Water Cycle

All domains are supported by invariant activities:

Protectionof naturalenvironmentInvestigating /Surveying

Water uses
& services[Source
e: @qua p
project]

Naturalhazards
mitigation[Source
e: @qua p
project]

Observing /Monitoring

Operating

Water
cycle

Added value of
new technologies
Building &Decommissioning

Resilience & urban flood management strategies Ph.Gourbesville

Designing

Resilience & urban flood management strategies Ph.Gourbesville

HHow
ICT can help
h l improvingii risksi k management?
t?

H
How
can ICT help
h l improvingii water management??

The ICT implementationThi lt ti hash tot beb organizedi d aroundd a new approachh basedb d onbusiness processes analysis:“For each business process, some ICT solution can be implemented in order toimprovement its efficiency. The R&D actions must target the businessprocesses where the added value is maximized.”• Th
The topict i hash beenbaddressed
dd
d for
f more than
th 30 years andd a lotl t off ICT progresses
have already been done in the water domain since that time. SCADA, hydraulicmodels, GIS are some of the well‐known components.
• Hydroinformatics tools and methods have supported this evolution and havebeen integrated gradually in industrial activities (integration within BPs).
• Manyusing
iand tools

Resilience & urban flood management strategies Ph.GourbesvilleHigh resolution urban flooding simulation in Mexico citywater

utilitieshydroinformatics
h
d i f
ti

are

now
methods
th d[Sourcee: Ondeo Systems]• 3 main domains

Water domains & activities

Resilience & urban flood management strategies Ph.Gourbesville

Climate change and Flood simulations in Vietnam – Da Nang area

Flood management
Resiliencestrategies:& urban
needs
flood
and
management
operationalstrategies
concepts Ph.Gourbesville-5-

Flood management
Resiliencestrategies:& urban
needs
flood
and
management
operationalstrategies
concepts Ph.GourbesvilleWhat are we talking about?

Introducing resilience

The risk management cycleo Responseo Recoveryyo Preparedness

Resilience ‐ The capacityp y of a system,y, communityy or societyy p
potentiallyy
exposed to hazards to adapt, by resisting or changing in order to reach andmaintain an acceptable level of functioning and structure. This is determinedby the degree to which the social system is capable of organizing itself toincrease its capacity for learning from past disasters for better future
protection and to improve risk reduction measures.
Five dimensions for estimation:
Sourcce: Swiss civvil
proteection

–
–
–
–
–

natural,physical,h i lsocial,economic,
institutional

Resilience & urban flood management strategies Ph.Gourbesville

Resilience & urban flood management strategies Ph.Gourbesville

The resilience concept

The resilience concept
Damages & Impact

Protection measures
Sample for presentation of
FRI in radar chart form
(Rajib Shaw and IEDM Team(2009))

Magnitudeof
Event
Resilience & urban flood management strategies Ph.Gourbesville

Resilience & urban flood management strategies Ph.Gourbesville

The resilience concept

The resilience concept

Damages & Impact

Damages & Impact

T1
Protection measures

Protection level / Return period

T2
Protection measures
Magnitudeof
Event
Resilience & urban flood management strategies Ph.Gourbesville-6-

Investment
level/ Full Cost

Magnitudeof
Event

Resilience & urban flood management strategies Ph.GourbesvilleWhat means resilience?

The resilience concept
Damages & Impact

T1: high return period (100y) & limited damages – Choice of developed/oldcountries• Nice but doesn’t work really!
y Whyy it doesn’t work?
Several reasons:• difficult to asses extreme events (obs. & statistics)• allll protections
t ti
will
ill failed
f il d (b(butt no iincluded
l d d iin ddesigni hypothesis)h th i )• human environments are dynamic and change in time and space(design hypothesis are not relevant anymore) and vulnerability as well.
• non stationarity of natural processes (incl. climate evolution)

Protection level / Return period

T1
T2

Protection measures

Flood management strategies: needs and operational concepts Ph.Gourbesville

Investment
level/ Full Cost

Magnitudeof
Event

Resilience & urban flood management strategies Ph.Gourbesville

Resilience as a new strategy?

Flood Resilience Index (FRI)

T2: lower return period with affordable financial & technical capacity – Situation ofdeveloping and emerging countries
Damages?... In some places, less than for developed countries (implicit resilience)
Severall reasons:• better understanding and awareness of processes due to higher frequency• limited vulnerabilityy ((individual & collective))
T2 could be a starting point for developing more resilient environments (avoiding
mistakes from others)
T2 could be an option for rethinking risk management strategies in developed
countries (financial & technical constraints)• Development of urban flood resilience assessment tools with indicators
enables a comprehensive overview of resilience of a city and community
p between the nature of interaction and the structure of an• The relationshipurban system is fundamental• Developing a simple approach that could be operational in all situations• Was
W developedd ld withinithi CORFU FP7 projectj t andd appliedli d ini EuropeanEanddAsian major cities (exposed to flooding events)

Resilience & urban flood management strategies Ph.Gourbesville

Flood Resilience Index (FRI)

Resilience & urban flood management strategies Ph.Gourbesville

Flood Resilience Index (FRI)
Availability level
0
1
2
3
4
5

Understanding urban environment

Description
Not available
Poor availability – major interruptions
Low availability – interruptions provide minimum availability
Medium – small interruptions that are tolerable for small flood durations
Medium‐highMediumhigh – interruptions that are tolerable for long flood durations
Requirement fully provided

Requirements for urban function
EXTERNAL SERVICES
EnergyWater
Waste
Communication
Transport
INTERNAL SERVICES
Food availability
Occupation of urban function
Access to the urban function
FRI (parcel/building scale)

Resilience & urban flood management strategies Ph.Gourbesville-7-

Availability level (0 – 5)

Weights (1‐5)

0,1,2,3,4,5
012345
0,1,2,3,4,5
0,1,2,3,4,5
0,1,2,3,4,5
0,1,2,3,4,5

1,2,3,4,5
1
2345
1,2,3,4,5
1,2,3,4,5
1,2,3,4,5
1,2,3,4,5

0,1,2,3,4,5
0,1,2,3,4,5
0,1,2,3,4,5

1,2,3,4,5
1,2,3,4,5
1,2,3,4,5

Resilience & urban flood management strategies Ph.GourbesvilleFlood Resilience Index (FRI)

Flood Resilience Index (FRI)
3/10/2015 Nice
Ni
– France

Applicationli i to Nicei – France

Resilience & urban flood management strategies Ph.Gourbesville

Flood Resilience Index (FRI)

Resilience & urban flood management strategies Ph.Gourbesville

Flood Resilience Index (FRI)

Flood map – Nice, FRANCE 50 years return period

Resilience & urban flood management strategies Ph.Gourbesville

Resilience & urban flood management strategies Ph.Gourbesville

Flood Resilience Index (FRI)

Resilience as a new strategy?

FRI for different water depths

Implementing resilience in urban design & planning

0.2m

0.5m

1m

>1m

5
4
3
2
1
0

Resilience & urban flood management strategies Ph.Gourbesville-8-

Resilience & urban flood management strategies Ph.GourbesvilleConclusions & perspectives
A tremendous task to achieve!!
Improve understanding of processes
Rethink cities and be resilient!
Develop a “risk culture” & improve governance with relevant ICT
solutions• Rethink all concepts (incl.
(incl engineering) for a new holistic approach••••

Thanks for yyou attention!
Contact: Prof. Philippe Gourbesville
Nice Sophia Antipolis University
Polytech Nice Sophia@: gourbesv@unice.fr
T: +33 6 50 03 16 51

Resilience & urban flood management strategies Ph.Gourbesville-9-

Resilience & urban flood management strategies Ph.GourbesvilleKeynote Lecture 2:
Water-related disaster risk, resilience and building back better
Kuniyoshi TAKEUCHI (Advisor, International Centre for Water Hazard and Risk Management,
Public Works Research Institute; Professor-Emeritus, Yamanashi University)
Abstract:
In the Sendai Framework for Disaster Risk Reduction, “resilience” has been markedly emphasized
as compared with the Hyogo Framework for Action. Resilience is defined by UNISDR (2009) as“the ability of a system, community or society exposed to hazards to resist, absorb, accommodate toand recover from the effects of a hazard in a timely and efficient manner, including through thepreservation and restoration of its essential basic structures and functions.” Thus it is a combinationof both resistive capacity and recovery capacity and its difference from risk reduction is theemphasis of recovery capacity.
The four priorities of the Sendai Framework for Action are: understanding risk, risk governance,
investing for resilience, and “Enhancing preparedness for effective response, and building backbetter in recovery and reconstruction.” The emphasis of building back better reflects the emphasis ofresilience in the Sendai Framework. While such an emphasis is not necessarily popular among those
who emphasize the importance of developmental stage before disaster occurs as compared with thehumanitarian recovery stage after disaster happens, it is deadly important for resilience building. It isbecause regardless of the enormous efforts of disaster risk reduction, disaster does occur anywhereanytime and so does disaster recovery. Disaster recovery is a precious occasion to reduce risk andtransform society by building back better.
In Banda Aceh, Sumatra, Indonesia, its recovery from the Indian Ocean Tsunami in 2004 was rather
quick responding to its Master Plan aiming at “Reconstruction of disaster affected cities by restoringthem into their initial state of order”. On the other hand, the recovery of Tohoku cities from thedevastating Great East Japan Earthquake and Tsunami in 2011 has been rather slow reflecting the
Basic Act on Reconstruction that says “promoting dramatic measures … which does not limit itself
to recovery from disaster which simply restores affected facilities to its original state”.
In the light of resilience building, the benefit of quick recovery is obvious as the cost of supporting
evacuees in tolerable condition is tremendous. If recovery time is short, such investment and efforts
can be directed for building back better. Prolonged stay in temporal location is hard for people andmakes them difficult to move back to the original location as life is already adjusted in the temporal
location. On the other hand, if disaster affected people are allowed to move back to the original place
before enough risk reduction infrastructure is developed, it is extremely difficult to reintroduce
low-risk land-use design after recovery is made. In such cases, different resilience building strategy
should be taken. Building back better needs a pre-disaster recovery plan.

- 11 -

The Twenty-fifth IHP Training Course
Kyoto Japan
Kyoto,
30 November - 11 December, 2015

Contents
 From Hyogo to Sendai

Water-related
Water
related disaster risk
risk,
g back better
resilience and building Risk and Resilience Building Back Better• Banda Aceh
• Tohoku
ac oba• Tacloban

KuniyoshiyTakeuchiInternational Centre for Water Hazard and Risk
Management under the auspices of UNESCO (ICHARM)
Public Works Research Institute (PWRI)
Tsukuba, Japan

Hyogo Framework for Action 2005-2015

Sendai Framework for Disaster Risk Reduction
2015-2030 3 World Conference on DRR, 18 Mar 2015 (A/CONF.224/CRP.1)
Priorities for Action

World Conference on Disaster Reduction (A/CONF.206/6)
Kobe, Hyogo 2005 adopted by 168 nationsrd

Priorities for Action

 Understanding disaster risk.

1. Ensure that disaster risk reduction is a national and alocal priority with a strong institutional basis for
implementation.
2 Identify,
2.
Identify assess and monitor disaster risks andenhance early warning.
3. Use knowledge, innovation and education to build
a culture of safety and resilience at all levels.
4. Reduce the underlying risk factors.
5. Strengthen disaster preparedness for effective
response at all levels.

 Strengthening governance andinstitutions to manage disaster riskrisk.
 Investing in economic, social, cultural,andd environmentalit l resilience.
ili Enhancing preparedness for effective
response, and building back better inrecovery and reconstruction
reconstruction.

Global Sustainability Human well
well-being
being

Framework
Seven Global Targets Sendai
for DRR 2015-30(a) Substantially reduce global disaster mortality by 2030, aiming tolower average per 100,000 global mortality between 2020-2030
compared to 2005-2015.
(b) SR the number of affected people globally by …
(c) R direct disaster economic loss in relation to global GDP by 2030.
((d)) SSR disaster damage
g to critical infrastructure and disruption
pofbasic services … through developing resilience by 2030.
(e) Substantially increase the number of countries with national and
l
local
l drr
d strategiest t i byb 2020.
2020(f) Substantially enhance international cooperation to developing
countries throughg adequateqand sustainable support
pp to complementp
theirnational actions for implementation of the present Framework by 2030.
(g) Substantially increase the availability of and access to multih
hazardd early
l warningi
systemst
andd disasterdit riski k information
i f
ti anddassessments to the people by 2030.

Attributes

Freedom of choice
HealthSafetyS i lSocialMaterial needs
Health Culture Human Social relation
Peace
Air
Security Almerty Sen & Ogata

Water
W t

Bio-Diversity Land Energy
Eco-System Food Business

Environmental

Economic

Finance

Controllers

Risk Reduction& Resilience building

Science & Technology

Governance

Human Empowerment- 13 -

Security is a
product ofrisk reduction& vice versa

Disaster Risk

Vulnerability
 The characteristics and circumstances of acommunity system or asset that make it
community,
susceptible to the damaging effects of ahazard.
hazardResilience The ability of a system, community or society
exposed to hazards to resist, absorb,
accommodate to and recover from theeffects of a hazard in a timely and efficientmanner, including through the preservation
and restoration of its essential basic
structures and functions.

There is no such thing
as a natural disasterdisaster.

R=HxV
R: disasterdit riski k
H: hazard
V: vulnerability

The potential disaster losses, in lives, health status,
livelihoods, assets and services, which could occur to aparticular community or a society over some specified
future time period.
A potentially damaging physical event, phenomenon or
human activity that may cause the loss of life or injury
injury,property damage, social and economic disruption orenvironmental degradation..
.The characteristics and circumstances of acommunity, system or asset that make it susceptible
to the damaging effects of a hazard.

UNISDR (2009) Terminology on Disaster Risk Reduction

Building Back Better Disaster occurs anywhere anytime andrecovery takes
t k place.
lSome examples of recovery

 For recovery large humanitarian budget isinvested. Use it for development.
 It is an indispensablepoccasion to build backMechanism of building back better

better for higher resilience.
• Turn disasters to opportunities.
pp

Banda Aceh, Nov 11, 2009

Banda Aceh, Nov 11, 2009

Kamaishi, Jan 13, 2013

Yuriage, May 13, 2014

http://yamada-cmjv jp/
http://yamada-cmjv.jp/- 14 -

Masteraste Plana foro tthee Rehabilitatione ab tat o aanddReconstructionof the Regions and Communities of the Province of Nanggroe Aceh Darussalam andthe Islands of Nias, Province of North Sumatera (Republic of Indonesia, April 2005)

 Section 5.2 (16) “Reconstruction of disaster

affected cities by restoring them into theirinitial state of order” and “The cities strickenby earthquake and tsunami are to bereconstructed by immediateempowerment of the affected people,
restoring the initial physical order, social
order and economic system, …, selfrestructuring of settlements by thecommunities concerned, …”

Tacloban, Leyte Oct 20, 2015

Reconstruction
principles

Basic Act on Reconstruction
in response to the Great East Japan Earthquake(28 Sept 2011)(Heisei 23 Law 76, 2011) 24 June 2011

Two levels Approach
Level 1 Tsunami (Frequent scale: 50-150
50 150 years)
Life, prop & livelihood
Sea walls, highways:

Residence/disaster prevention bases Article 2“2) … communities will be restored withthe vision of Japan appropriate for mid-twentyfirst century. Such will be accomplished bypromoting dramatic measures with the perspectiveof revitalizing vibrant Japan which does not limit
y from disaster which simply
pyitself to recovery
restores affected facilities to its original state,
as well are construction measures which aim tofacilitate each individual to overcome the disasterand lead prosperous lives.”

Minamisanriku-cho

Business & industry builgs
Cut mountainhills & develop

HWL L2

L1

land elevation 2nd line seawall
E
Evacuation
ti shelterh lt
Seawall

Level 2 Tsunami (Maximum scale: 1000 yr) Life
Move to higher lands
Tall buildings to evacuateLanduse (park,(park factories
factories, farmland; commercial/business,
commercial/business residential areas)

Selection of recovery trajectory 1
 Itt iss verye y costly
cost y to maintaina ta tetemporary
po a y houses
ouses intolerable living conditions and public services.
 Prolonged
g stayy in temporalplocation make ppeoplepdifficult to move back to the newly built original location.
 Consensus building on where to move or elevate, realestate procedures, budgets allocation, moving earth etc.
need much time.
 LackL k off engineers,imachines,hicompanies,i
ttoo.
 The proper speed and trajectory depend on society:economic conditions,conditions people’s perception on a seldom
but high impact event, disaster literacy and culture etc.
 Only history would tell the right decision in the future
future.

- 15 -

Selection of recovery trajectory 2

居安思危 Be aware of risk while we are safe
思則有備 Awareness leads us preparedness
p p
有備無患 Preparedness leaves us no regret

 Can
C we standt d severall years off ttemporary residence?
id?

• School, hospital, livelihood, neighbors, Do we give up the benefit of everyday convenience for「春秋」左氏伝
Source： Zuo Qiuming “Zuoshi Commentary”
in Confucius ed. ”Spring and Autumn”, 480BC

once every 1000-year event? (Ex. in Tohni, back tolowland) Low protection with evacuation preparedness would be
ap
practical solution. Next tsunami is not necessarily
y thelargest one. Preparedness works for any.
 How to cope with the fading memory? By memorials,
festivals etc. Education leads to safety culture.
 Predesign (pre-disaster recovery plan) reduces waitingtime.
i

ICHARM cares people’s
empowerment andd well-being!
ll b i !
www icharm pwri go jp
www.icharm.pwri.go.jp
ICHARM preparedness for floods- 16 -

Keynote Lecture 3:
Water Security: Responses to Local, Regional, and Global Challenges with
Special Reference to Asia-Pacific Region
Shahbaz Khan
UNESCO Regional Science Bureau for Asia and the Pacific, Jakarta, Indonesia
Email: s.khan@unesco.org
The overall theme of International Hydrological Program (IHP) Phase VIII (2014-2021) has
been decided by the Member States of UNESCO as Water Security. This is based on thechallenges of water availability, both in quality and quantity, for supplying increasing
population and demand, and also responding to the needs of ecosystems. It addresses
water security challenges at local and global levels. Six themes have been scoped to deliver
IHP-VIII: (1) water-related disasters and hydrological change, (2) groundwater in a changing
environment, (3) addressing water scarcity and quality, (4) water and human settlements ofthe future, (5) eco-hydrology, engineering harmony for a sustainable world and (6) water
education. The IHP VIII strategy also includes cross-cutting issues such as IWRM, transboundary waters and the human dimension. IHP VIII is very important to the Asia-Pacificregion which supports about 60 per cent of the world’s population with 38 per cent of theworld’s water resources. The ASPAC region has twenty seven IHP National Committees,
seven of which are in LDCs. IHP has been very active in international research throughpublications for the FRIEND network, including a Catalogue of Rivers for the region.
UNESCO is promoting the use of remote-sensing-based climate and flood warningtechnology to upgrade flood management of Pakistan. The project will help build thecapacity of Pakistan Meteorological Department and other agencies responsible for flood
forecasting, early warning and management at the national, provincial and district levels.
This project will enable the institutional capacity of Pakistan, to predict floods as prior as 1
to 14 days by tracking weather and flood waves. This would be major milestone in achieving
the better capacity to mitigate extreme floods like 2010 floods in Pakistan. The project will
not only bring state of the art technology but will also harness linkages between Pakistani
institutions such as Pakistan Meteorological Department (PMD), SUPARCO, Federal Flood
Commission (FFC), NDMA and Japanese institutions such as International Centre for Water
Hazards and Risk Management (ICHARM), a UNESCO Category II Centre, and Japan
Aerospace Exploration Agency (JAXA).

- 19 -General Description of IHP(International Hydrological
Programme)

Water Security: Responses toLocal, Regional, and Global
Challenges with Special
Reference to Asia-Pacific Region
Education Sector

25th IHP Nagoya-Kyoto Training Course on“Risk
Risk Management of Water-related
Water related Disasters under
Changing Climate”

Sciences Sectors

Culture Sector

International Hydrological Programme-UNESCO’s Intergovernmental Scientific
Cooperative Programme in Hydrology andWater Resources
Natural Sciences-The only intergovernmental programme of• International Hydrological Programme (IHP);
the UN system devoted to water research,• Intergovernmental Oceanographic Commission (IOC); watert resources management,t andd educationd ti• Man and the Biosphere Programme (MAB);
and capacity building• International Geosciences Programme (IGCP);-Established in 1970’s, now that prepared the• International Basic Sciences Programme (IBSP).
(IBSP)
eighthi hth phaseh
off IHP (IHP
(IHP-VIII)
VIII) forf 2014 2021
Social and Human Sciences

P f
D
hb KhanKh
Professor
Dr Sh
Shahbaz
UNESCO Regional Science Bureau for Asia and the Pacific, Jakarta,
Indonesia

2

IHP‐‐ VIII 2014‐
IHP
2014‐2021

UNESCO IHP Network

Water Security:yResponse to Global, Regional and Local Challenges

Water related Institutes and Centres(18)Water-related

Water-related Chairs(29)

33

4

4

UNESCO’s Actions for Disaster
Reduction
Long‐‐term Goals
Long

UNESCO-IHP
UNESCOIHP ini theth Asia
A ig
Pacific Region
•
•
•
•
•
•
•

• 17 IHP National Committees• 6 UNESCO Water Centres• 6 UNESCO Water Chairs in
the Asia Pacific Region.
• Regional Steering
Committee for Southeast
Asia and the Pacific

Observation and early warning networks of natural hazards
Hazard risk mapping
Disaster‐resistant building codes
Education for disaster reduction
Help make schools safer
Promotion of public awareness through communication
Protection of cultural monuments and sites

65- 21 -

Extreme Weather Events in
P ki tPakistan

FloodsTropical Cyclones

Heat Waves

Droughts

Strategicg Strengtheninggg of Flood Warninggand Management Capacity of Pakistan
1- Phase 1Funded by the Government of Japan

7

8

Executive summary of the 2010 floods

Extreme Weather Anomalies
During 2010

Recorded the historical Maximum
Recorded above the Designed Capacity• Jan & February, 2010:
Drought condition in the country• June 2010:
Extreme Heat wave condition. At many places
temperatures exceeded 50
50°C
C• July & August 2010:
Worst Floods in about 100 years

Tarbela
Kabul
River

ChashmaTaunsa

Kotri

9

10

Executive summary of the 2010 floods

Problem revealed by the flood
2010 and counter measures takenin this project

Upper Indus
There was limited or no
flood forecasting ability
for the areas severely
damaged by the floods

Lower Indus
The flood devastated theareas where had noinundation experience inthe past
11

Flood forecasting
including upper-Indus will
be introduced by a new
system utilizing satellitedata（A1)
Updating flood hazard
maps in lower Indus tocover the new inundated
areas（A2))
12- 22 -

UNESCO Post 2010 Floods
Actions in Pakistan

Executive summary of the2010 flfloodsd

Flood affected area : 78/121 districts(64%)
Population affected : 20 million (1/9 of population)
Deaths: 2000 approx.
Injured: 3000 approx.
approxHouses damaged : 1.6 million
A affected
ff t d : 100000 kkm22
Area
100,000
Economic losses : US$10.0 billion

1) UNESCO DG sent a team of flood
management experts to Pakistang2010.
on 22nd August2)Based on the mission to Pakistan, UNESCO
preparedppresponseppprojectjwith the Pakistaniauthorities to reinforce the country’s capacity in:• integrated flood and watershed management• groundwaterd t resources forf emergency situations
it ti• landslides and ground instability especially for relocation ofaffected population.

(Direct 6.5billion, Indirect3.6billion)

Reconstruction cost

: US$8.74
US$8.74--10.85billion* Source: Preliminary Damage and Needs Assessment by ADB&WDB

14
14

13
13

Component of the Pakistan Flood
ProjectjJapanese ODA and
UNESCO projectj t

A. Strategic Augmenting of Flood Forecasting and Hazard
Mapping Capacity
A-1 Development of Indus IFAS
A-2 Floodplain and Hazard Mapping of Lower Indus

B. Knowledge Platforms for Sharing Transboundary
and Community Data
B-1 International Networking for Sharing of Transboundary Data
B-2 Knowledge platform for timely national, provincial and districtlevel data sharing

C. Capacity Development for Flood Forecasting
andd H
Hazardd M
Mappingi• Master degree training course for the Pakistan government staff• Short training courses for the senior water managers• Training workshops on use of flood forecasting models and flood
hazard maps
16

< 3million USD

15

Project Component

Implementation Framework
UNESCO

Pakistan Authorities
PMD
Development of
Flood Forecasting System Component: A1• Flood
Fl d fforecastingti andd earlylwarning system• Hazard mapping

A1

Project Implementation

IFAS Introduction

International Partners
UNESCO International NetworkIndus IFAS has been developedIndus-IFAS
in collaboration with UNESCO &ICHARM

Data support
Experts
UNESCO Network

PCRWR
Soils and Hydrological data

Component: A1, A2

Component: A1

UNESCO Water Center

SUPARCO
Flood Risk Hazard Mapping

NDMA (Including NIDM)
National Policy andFlood Management at National,
Province, District level• GSMAP Local Calibration*Satellite based rainfall

International Centre for
Water Hazard and Risk
Management under the auspices of UNESCO

V lid ti andd update
Validationd t iin 2013*

Component: A1, C

Coordination for flood management
at provincial level Component: B2

Indus River Commission
Transboundary Data sharing
Component:B1

Upper Indus

Right sideof Indus
tributaries
hill areas

Real time flow hydrograph from
Existing FEWS*The result was published through FFD/PMD website

ModelsM
d l hhave bbeen madedoperational toward flood season
in 2014

Component: B2,
B2 C

FFC

Kabul River

Test operation in 2012

ICHARM

Component: A2

Integrated
t
t d
Flood
Analysis
System

JAXA
Japan Aerospace Exploration Agency
Component: A1, A2

17

Geographic area to be covered
by Indus-IFAS
Indus IFAS
(enclosed by dotted lines)

18- 23 -

Integrated Flood Analysis System (IFAS)

Rainfall‐Runoff‐Inundation(RRI) Model

Flood forecasting system using satellite data

inp
put

Global Geological data formodeling Elevation data, Landuse data,, etc.

Ground rainfall and SatelliteSatellitebased rainfall
GSMaP_NRTGSMaP_MVK+
3B42RT(V6)
3B42RT(V5)QMORPHCMORPH

Subsurface + Surface
1D Diffusion
in RiverCourtesy of JAXARiver diRi
discharge,
h
W
Water
t llevel,l
Rainfall distribution

Run-off analysis
Surface model

2D Diffusion
in Catchment

Integrated Flood Analysis System

Model creation
Aquifer modelRiver course
model• Diffusion Wave Approximations• 1D in River• 2D in Catchment• Subsurface flow
• Vertical Infiltration with Green‐Ampt• Saturated Subsurface + Surface Flow

Calculation

Flow/water level

Floodforecasting/warning

Promoting safe
evacuation

Vertical Infiltration

Reduce/Prevent
flood damage

Sayama, T. et al.: Rainfall‐Runoff‐Inundation Analysis of Pakistan
Flood 2010 at the Kabul River Basin,
Hydrological Sciences Journal, 57(2), pp. 298‐312, 2012.

19• Rectangular river cross sections• Width, Depths, Levee heights can be
assignedgfor each river ggrid‐cells• Over‐topping and step‐down formulae areused to compute the interactions between
water in river and on slope• Water depth and discharge boundary
conditions can be wet at any grid‐cell21

22

Project Component

On FFD WEBSITE:
http://www.pmd.gov.pk/FFD/index_files/ifashyd.htm

23- 24 -

On FFD WEBSITE:
http://www.pmd.gov.pk/FFD/index_files/ifashyd.htm

On FFD WEBSITE:
http://www.pmd.gov.pk/FFD/index_files/ifashyd.htm

IFAS RESULTS2014 at P.Bridgeg

Project Component

On FFD WEBSITE:
http://www.pmd.gov.pk/FFD/index_files/ifashyd.htm

A2

Flood Hazard MappingUpdated and simulated Hazard Maps
are available through the web site.

RRI RESULTS2014 at Guddu

Cover lower Indus (32 districts)
including newly affected areas by theflood 2010

Punjnad

Real time inundation simulation
model(RRI model) has been provided
and its accuracy will be checked incomingi flfloodd seasons.
Flood Hazard Mapping Area
(enclosed by dotted lines)

28

Project Component

Project Component
B

Software Platform for Transboundaryand domestic data sharing The
Hydro-meteorologicaldata
sharing information system, called
PIFMIS,, has been pprovided to
FFD/PMD.

Enable show
BHUs, Settlements, Roads,
Schools, Agriculture andBridges with rayers

 This system is expected to provide acommon platforml tf
f allll flood-relatedfor
fl d l t d
stakeholders in flood management
 International dialogue fortransboundary data
sharing/flood management has
been started with neighboring
countries through the theinternational forum/conferencewith using UNESCO water
network.

29- 25 -(PMD, SUAPRCO, IRSA, FFC, WAPDA, IWC, provincial
irrigation departments, etc.)

 PFIMIS enables hydro-meteorological
data entry, analysis, and display of
fl d relatedflood
l t d information
i f
ti in
i a userfriendly way and enhances interorganizational coordination, while
sharing
h i
precipitation,i i istage, andd
discharge data in near real-time.
30

Project Component
B

Project Component（3/3）

Software Platform for Transboundaryand domestic data sharing

C

Human
Capacity
Development
H
C
it D
l
t

Master Degree Course training

Flood fighting drill & Flood forecasting/operationi room observationbi (short( h term training)i i ) Several human capacity building activities were conducted for Pakistan
agencies.
 6 Pakistan professionals(PMD, SUPARCO, Irrigation department) have graduated andobtained Master’s Degrees through ICHARM training course in Japan.
 Intensive short term trainings were conducted for senior managers inJapan and 11 experts have received trainings on flood forecasting andmanagement in Japan.

 A media centre equipped with advanced devices was newly establishedin PMD
PMD-FFDFFD in Lahore within the framework of the project and it enables
to directly provide the real time flood forecasting and warning to the
public.
31

32

Operationalization of Indus-IFAS
Some examples of links and products distributed during
2015 floods:

Strategicg Strengtheninggg of Flood Warninggand Management Capacity of Pakistan
2- Phase 2• http://reliefweb.int/map/pakistan/pakistan-floods-2015ppp
p
simulated-inundations-rajanpur-muzaffargarh-rahim-yarkhan-28-july• http://reliefweb.int/sites/reliefweb.int/files/resources/Map_
RRI%20Indus_Rajanpur%20RYK_MGarh_270715.pdf
http://disasterwatch sgs suparco gov pk/maps/disasters• http://disasterwatch.sgs-suparco.gov.pk/maps/disasters

Funded by the Government of Japan

33

34

Component of the Pakistan Flood Project -Phase 2-

A. Establishment of the technical foundation for sustainable capacity development on
A
the flood management, forecasting, early warning and flood hazard analysis in
Pakistan agencies

A-1 Technical studies on the improvement of the accuracy of flood
forecasting and early warning system in Pakistan
A-2 Strengthening the flood forecasting and warning capacity in Eastern
RiRivers(Jhelum,(Jh lCh
Chenab,b R
Ravii andd S
Sutlejtl j rivers)i)
A-3 Strategic and continuous enhancement of the flood management
capacity in Pakistan

B. Technical studies to promote strengthening of cooperation with Indus river
basin countries for transboundary flood management and transboundary data
sharing

B-1 Technical studies on strengthening of the transboundary floodgcapacityp y of the Indus river basin countries
management
B-2 Reinforcement of the relationship within the Indus river basin countries
for transboundary flood management and data sharing

Th rivers
Theicovereddb
by th
the previousiproject
j t (Phase‐1)(Ph1)
and by the proposed project (Phase‐2)

C Capacity
C.
C
it building
b ildi andd educationd ti tto community
it on flflood
d managementt ffor proper
utilization of flood hazard information and tools

36

35- 26 -

Component of the Pakistan Flood Project -Phase 2-

Current Status

A. Establishment of the technical foundation for sustainable capacity development on
the flood management, forecasting, early warning and flood hazard analysis in
Pakistan agencies

1) Government of Japan and
UNESCO signed an agreement of $ 4.05 million forstrengthening flood warning system in Pakistan (Islamabad,
10 March 2015)

B. Technical studies to promote strengthening of cooperation with Indus riverbasinbi countries
t i forf transboundarytbd
fl d managementt andflood
d transboundary
t
b
d
d t
data
sharing

C. Capacity building and education to community on flood management for proper
utilization of flood hazard information and tools(i) Technical studies on utilization of the hazard information for better
understanding of the local people;(ii) Education on flood management, response and evacuation for schoolteachersand children;(iii) Creation of the modular education and training program for school teachers
tailored for flood risk reduction with NIDM, NDMA and universities in Pakistan;and(iv) Training workshops on flood management for local government officers andlocal leaders (2 times per year ‐ 4 in total);2) Partners contracting.
3) International Partners Technical and Capacity Building
Meeting (5-8 Aug 2015), Avari Hotel, Lahore with ALL
gathered for the first time.
PARTNERS g
37

38

Strategic Strengthening of Flood Warning and Management Capacity of Pakistan(phase2)
International Partners Technical and Capacity Building Meeting (5(5‐88 Aug 2015)

Link with Japanese expertise andother JAK activities through JFIT
JFIT.

Participants: Pakistan Government (FFD/PMD, PCRWR, SUPARCO), Pakistani Universities and Institutes(NUST, LET Lahore, Peshawar Univ. Beaconhouse National Univ. COMSATS, Soil and Water
Conservation Research Institute), Japanese Partners (JAXA, ICHARM), UNESCO, JICA

25th IHP Nagoya Training course:“Ri k Management“Risk
M
t off Water‐related
W t
l t d Disasters
Di t
under Changing Climate”
Partners Technical Meetings （7 Aug）

Meeting withPakistani and Japanese Partners(8 Aug)Dates : 30 November to 11 December,
December 2015
Venue : Disaster Prevention Research Institute, Kyoto University, Uji, Japan
Conveners : Prof TANAKA, Shigenobu (DPRI)
Chi f assistant:Chief
i t t NOHARA,
NOHARA Daisuke
D i k (DPRI)

Meeting with
Afghan Partners(8 Aug)g

This training is extremely relevant for our project as it will include:‐ a comprehensive overview of Water‐related disaster risk managementwith leaders in the region on the topic (ICHARM, KU,TU, UNESCO).
‐ hands on trainings on RRI with Prof Sayama and frequency analysis with
Prof Tanaka.

Field Trip for Marala Barrage(6 Aug)40

ThanksContact for further information:s khan@unesco org
s.khan@unesco.org- 27 -

Lecture 1: Projected future meteorological environment- Heading to adaptation strategy Eiichi NAKAKITA (Professor, Disaster Prevention Research Institute, Kyoto University, University)
Abstract:
First, the physical science basis on the climate change reported by the IPCC WG1 in the fifth
assessment report of IPCC will be outlined in the lecture. In the report, very important results suchasWarming of the climate system is unequivocal.



Human influence on the climate system is clear.



Limiting climate change will require substantial and sustained reductions of greenhousegas emissions.

are reported. Regarding the water cycle, it is predicted that changes in the global water cycle inresponse to the warming over the 21st century will not be uniform. The contrast in precipitation
between wet and dry regions and between wet and dry seasons will increase, although there may beregional exceptions.
Second, it will be shown what Japanese program on risk information on climate change is. The
program is called “SOUSEI program”. Using the world-class supercomputers, such as Earth
Simulator, we are pursuing research and development in which all themes are organically linked.
Our research and development include prediction and diagnosis of imminent global climate change
expected to occur within a few years or decades, research on greenhouse gas emission scenarios andassociated long-term climate change projections, development of probabilistic climate change
projection techniques, and development of technology for precise impact assessment, etc. Under the
program, the theme for precise impact assessments on climate change aims to scientificallydemonstrate the connection between the aforementioned increase in natural disasters and global
warming and to look 100 years into the future to see how serious it may become. The research
results are to be presented as “actual figures” and are expected to be used as data for the government
and municipalities to consider how to protect the lives of people in urban and rural areas, coastal
areas, and river areas. A “100 year impact assessment” was proposed by this program’s antecedent,
KAKUSHIN, but this is the first attempt to produce an actual figure for “the maximum predictedamount of future rainfall.” To generate this kind of specific figure, detailed data with a high degreeof precision is required. Even with all the data that we can collect, the sample size and precision are
still inadequate. So, it is very important to take on the challenge of developing an assessment modelthat can produce predictions even given the data limitations, and we endeavor to assess extreme
phenomena.

- 29 -

The Twenty-fifth IHP Training Course(30 N
Novemberb - 11 D
December,b 20152015,
Kyoto University, Uji, Kyoto, Japan)

Projected future meteorologicalenvironment- heading to adaptation strategy Eiichi Nakakita

Research Division of Atmospheric and Hydrospheric Disaster
Disaster Prevention Research Institute (DPRI)
Kyoto University
nakakita@hmd.dpri.kuoto-u.ac.jp
Leader of Group D
2

1

Warming of the climate system isunequivocal.
unequivocal

Observations

Human influence on the climate
system is clear.

Understandings

FutureLimitingLi
iti climate
li t changehwill
ill requireisubstantial and sustained reductionsof greenhouse gas emissions.

Observed globally averaged combined land andocean surface temperature anomaly 1850–2012
1850 2012

Warmingg of the climate systemyisunequivocal.
Warmingg of the climate systemyisunequivocal, and since the 1950s, many of
the observed changes are unprecedented
over decades to millennia. The atmosphereandd ocean havehwarmed,d theth amounts
t offsnow and ice have diminished, sea level has
risen, and the concentrations of greenhousegases have increased.
g

Each of the last
three decades has
been successively
warmer at the
Earth’s surface
th any precedingthan
didecade since 1850

(IPCC/AR5)- 31 -

Precipitation

Observed change in surface temperature 1901–2012(IPCC/AR5)

Confidence in precipitation change averaged over global land areas since
1901 is low prior to 1951 and medium afterwards.
afterwards Averaged over the mid
midlatitude land areas of the Northern Hemisphere, precipitation has increased
since 1901 (medium confidence before and high confidence after 1951).

Snow and sea ice

Extreme events

Over the last twoddecades,dtheth GreenlandGl dand Antarctic ice
sheets have been
losing mass, glaciers
have continued toshrink almost
worldwide, and Arctic
sea icei andd Northern
N thHemisphere springsnow cover havecontinued to decreasein extent (highconfidence)• Changes
g in manyy extreme weather and climate events
have been observed since about 1950.
• It is very likely that the number of cold days and nights
has decreased and the number of warm days and nights
has increased on the global scale.
• It is likely that the frequency of heat waves has increasedin large parts of Europe, Asia and Australia.
• ThereTh
are likely
lik l more landl d regions
i
where
h
the
th numberb offheavy precipitation events has increased than where it
has decreased.
decreased The frequency or intensity of heavy
precipitation events has likely increased in North
America and Europe. In other continents, confidence inchanges in heavy precipitation events is at most
medium.

© IPCC 2013

Glaciers
Ice loss ： Green Land
Antarctica

En
nergy (1021 Jouless)Fig. TS.3© IPCC 2013

226 (±60%) Gt yr-1 (1993-2003)()
215 (±25%) Gt yr-1 (2002-2011)
147 (±50%) Gt yr-1 (2002-2011)

Box 3.1, Fig. 1(IPCC/AR5)

Ocean warming dominates
the increase in energy
stored in the climate
system accounting forsystem,more than 90% of theenergy accumulated
between 1971 and 2010(high confidence). It isvirtually certain that theupper ocean (0−700(0 700 m))
warmed from 1971 to 2010(see Figure SPM.3), and it
likely warmed between the1870s and 1971.
Upper ocean: above 700m
Deep ocean: below 700 m; including
below2000 m estimates startingfrom 1992

100 Gt yr-1 of ice loss corresponds to 0.28 mm yr-1 of global mean sea level rise- 32 -

Global average sea level(IPCC/AR5)

The rate of sea level rise since the mid
mid-19th
19th century has
been larger than the mean rate during the previous twomillennia (high confidence). Over the period 1901 to 2010,global mean sea level rose by 0.19 [0.17 to 0.21] m.

(IPCC/AR5)

Observations

Understandings
Human influence has been detected in warming of the atmosphere andthe ocean, in changes in the global water cycle, in reductions in snow
and ice,
ice in global mean sea level rise,
rise and in changes in some climate
extremes. This evidence for human influence has grown since AR4. It isextremely likely that human influence has been the dominant cause of
the observed warming since the mid-20th
mid 20th century.
century

Future(IPCC/AR5)(IPCC/AR5)

Detection and Attribution of Climate Change
 It is extremely likely that more than half of the observed increase in global averagesurface temperature from 1951 to 2010 was caused by the anthropogenic increasein ggreenhouse ggas concentrations and other anthropogenic
p g
forcings
g together.
g It is very likely that anthropogenic forcings have made a substantial contribution toincreases in global upper ocean heat content (0–700 m) observed since the 1970s.
 It is likely that anthropogenic influences have affected the global water cycle since1960. Anthropogenic influences have contributed to observed increases inatmospheric moisture content in the atmosphere (medium confidence), to globalscale changes in precipitation patterns over land (medium confidence), tointensificationf
off heavy precipitation over land regions where data are sufficientff(medium confidence), and to changes in surface and sub-surface ocean salinity(very likely). 。

(IPCC/AR5)- 33 -

CO2 scenarios for AR5（Representative Concentration Pathways：RCP）

Observations

CO2
concent.
concent
936ppmTemp
inreacing
4.3℃

Understandings

670ppm

2.8℃

538ppm2.4℃

412
412ppm

1 6℃
1.6℃

Future※気温上昇量は1850‐1900年基準

20

Surface temperature

RCP Scenarios （~２３００）
2300年までのRCPシナリオ

Global surface temperature change for the endof the 21st century is likely to exceed 1.5
1 5°C
Crelative to 1850 to 1900 for all RCP scenarios
except RCP2.6.

(IPCC/AR5)

Colored bar：likely range （= 66% confidential range ）Sea Ice
Sea level Rise

Based on an assessment of the subset of models that mostcloselyll reproducedtheh climatologicalli
l i l mean state andd 1979 to2012 trend of the Arctic sea ice extent, a nearly ice-free Arctic
Ocean19 in September before mid-century is likely for RCP8.5(medium confidence)(IPCC/AR5)- 34 -

Ocean Acidification

Atmosphere：Temperature(IPCC/AR5)

 It isi virtuallyi t ll certaint i ththatt there
th
will
ill b
be more ffrequentt hhott anddfewer cold temperature extremes over most land areas on
daily and seasonal timescales as global mean temperatures
increase.
 It is veryy likelyy that heat waves will occur with a highergfrequency and duration.
 Occasional cold winter extremes will continue to occur.

RCP2.6 (2081〜2100) ： -0.06 〜 -0.07
RCP8.5 (2081〜2100) ： -0.30 〜 -0.32

Atmosphere：Water Cycle

Changesin Extremes
Extremechange in precipitation

 Changes in the global water cycle in response to the warming overthe 21st century will not be uniform. The contrast in precipitation
between wet and dry regions and between wet and dry seasons will
increase, although there may be regional exceptions.

Annual Precipitation

 Extreme
E tprecipitation
i it ti eventst over mostt off theth mid-latitude
id l tit d lland
d masses andd overwet tropical regions will very likely become more intense and more frequent by theend of this century, as global mean surface temperature increases
 Globally,y, it is likelyy that the area encompassedp
byy monsoon systems
ywill increaseover the 21st century. While monsoon winds are likely to weaken, monsoon
precipitation is likely to intensify due to the increase in atmospheric moisture.
Monsoon onset dates are likely to become earlier or not to change much. Monsoon
retreat dates will likelyy be delayed,yresultingg in lengtheninggg of the monsoon season in
many regions.
 ・・・・, ENSO-related precipitation variability on regional scales will likely intensify.
(IPCC/AR5)

Extreme precipitation events overmost of the mid-latitude land masses
and over wet tropical regions willvery likely become more intense andmore frequent by the end of thiscentury, as global mean surface
temperature increases.
increases Intensity
duration of drought will likely toincrease fro global to regional scales.

- 35 -

RCP2.6
CO2eq = 475 ppm

RCP8.5CO2eq = 1313 ppm

Limiting climatechange will require
substantial andsustained reductionsof greenhouse gas
emissions.

Climate Stabilization, Climate Change Commitment
and IrreversibilityyFactors related to risk induced by climate change

Cumulative emissions of CO2 largely determine global
mean surface warming by the late 21st century and beyond(see Figure SPM.10). Most aspects of climate change will
persist for many centuries even if emissions of CO2 are
stopped. This represents a substantial multi-centuryyclimate change commitment created by past, present andfuture emissions of CO2.

34

（IPCC WGII SPM，2014）

Launching of Sousei Program Kyousei(共生)Program:2002-2007
 20kmRCM (daily rainfall) Kakushin(革新)Program:2007-2012
 20kmGCM, 5,2,1kmRCM (hourly rainfall) Natural Disaster (Inc. water resources)
 Sousei(創生)Program:2012-2017
 Impactp
assessment and producingpg adaptationpmethodologies (First priority)
 for Natural Disaster, Water resources, Ecosystem
and Eco service (Kyoto University will lead thenation wide assessment team)- 36 -

Outlook SOUSEI Progam
for 2017

GCM results

2017

Impactassessment,
mitigation
andadaptation

2016
GCM results

2014

Makingmodel

S
SouseiiP
Program (2012-2017)(2012 2017)

Projection
considering
uncertainty,
uncertainty
variability
and worst
class

Imminent global
climate change(AORI,UT)

Stabilization
target setting(JAMSTEC)

Risk Information
(
(MRI)
)

Impactassessments(DPRI,KU)

Climatevariability andchangeLong‐term
projection

Probabilistic
climateprojection

NaturalDisasterIntegrated
prediction
systemLarge‐scalevariations

Producing astandard
climatescenario

Water
Resources

E
Ecosystemt
PI: Eiichi Nakakita (DPRI, Kyoto U)

37

2012

Sousei Program (2012-2017)

Theme (Group) D
I
ImpacttA
Assessmentt
PI: Prof. E. Nakakita (Kyoto U)
Theme A

Theme B

Near future
projection
G
Group
A
A:
CC Pred. and
Anal.

G
Group
B
B:
CC Pred. and
Anal.

Group D:
CC Impact
AnalysisSl
Slope

Outputfrom GCM
andd RCM
Interpreta‐tion ofoutputM
Mountains
t i

#D i
#D‐i

#D ii
#D‐ii

#D iii#D‐iiiNaturalHazardWater
Resources

Ecosystem

C t lA
CoastalArea

Hourly precipitation, temperature, water vapor, wind velocity, radiation and air pressure

SOUSEI
Theme A and B

Regional climate model （RCM_5km, RCM_2km, RCM_1km)
Stochastic
typhoon model

Surface hydrological model

Probability density function of extreme value（depending on spacio‐temporal scales）
Stochastic precipitation model (time series depending on spacio‐temporal scales)

Sedimentation andpof soiltransportation

Rainfall runoff

River channel flow

Climate modeling studies

SOUSEI
Theme CCMIP / IPCC
CORDEX etc.
Scientific journals

Storm suurge

Soil runoffBuuilding damagge by strong wind

Reservoir operation

Inundationn including
undergrounnd shoppingmooleEvaluation

H bit bl A
Habitable
Area

Probabilistic
i f
informationti（25‐years time series (20km) and ensemble predictions (60km) for current, near future and century end）

Soil production

Various
Models（with long‐（gterm run）

Ri
River

Long‐term projection

Typical two paths how knowledge from climate modelingstudies are utilized in impact/adaptationpp
analysesyPrediction and evaluation of disaster environment in JapanDPRI / Kyoto
Kyoto--Univ.

Theme C

Integrated Earth
System

100 yyears reanalysis
y

G
Group
C
C:
CC Pred. and
Anal.

38

SOUSEI
Theme
D
Impact and adaptation
analyses
Mitigation policy

Adaptation policy

Global / Terrestrial
Long-term
Longterm stabilization target

National / Local

UNFCCC etc.

Decreasing of safety against landslide, debris flow, flood, draught, storm surge and strong wind .
Assessment of current protection system and proposal of alternatives- 37 -

Local authorities
Businesses
Citizens

Steps towards National Adaptation Plan

Disasters and Infrastructure Design

Establish “Expert Committee on Climate Change Impact Assessment” at
114th Global Environmental Subcommittee, Central Environmental Council(2 July, 2013)

Range for disaster Mitigation

Designvalue((Return
period)Summarize climate change impacts
impacts, risk assessment and future issues(March Feb, 2015)• Extraction of priority areas/issues in short‐term (‐ 10 years),
middle‐term (10‐30 years) and long‐term (30‐100 years)

River dischadarge
S m surge
Storm• Further detailed projection of climate change in Japan tomonitor extreme events• Assessment on climate change impacts in Japan• Analysis on risk information

Range for disaster Prevention

Develop National adaptation plan as a government‐wide integrated
effort (Decmber, 2015)[Slide drawn by MOE modified by Nakakia]

Disasters and Infrastructure Design

Disasters and Infrastructure Design
Survivability Critical, Edge of Survivability (The worst case)

Range for disaster Mitigation

River dischadarge
S m surge
StormRiver dischadarge
S m surge
Storm

Designvalue((Return
period)Range for disaster Mitigation

Uncertainty of projection((Probability)y)Designvalue((Return
period)Range for disaster Prevention
Projection of design value• How much change?
• Where is severe location?
• When does it become severe?

Range for disaster Prevention
Projection of design value• How much change?
• Where is severe location?
• When does it become severe?

Nakakita (2010,2011)

Sousei (創⽣) Program (20122017)
Ultimate Goal
Proposing adaptation philosophy
consistent with mitigation
Post Sousei
philosophy

Sl
Slope

Generating PDF of extreme
values with higher accuracy

Outputfrom GCM
andd RCM

Generating of PDF using a lotof 60km ensemble
Converting extreme values in
60km-scale into values inregional-scale using RCM5
and RCM2 dynamicallydownscaled from GSM20.

M
Mountains
t i

Ri
River

H bit bl A
Habitable
Area

C t lA
CoastalArea

Hourly precipitation, temperature, water vapor, wind velocity, radiation and air pressure（25‐years time series (20km) and ensemble predictions (60km) for current, near future and century end）

Regional climate model （RCM_5km, RCM_2km, RCM_1km)
Stochastic

Surface hydrological modelInterpreta‐
typhoon modeltion of
Generating PDF of extreme
values
Probability density
function of extreme value（depending on spacio‐temporal scales）
outputStochastic
precipitation
model
(time
seriesdependingonspacio‐temporalscales)with higher accuracy
Reservoir operation

Kakushin(2007-2011)

Rainfall runoff

Worst case senarios
Worst case typhoon
Multi-hazard

For adaptation decision making
Social Senario
Deterministic,
Deterministic Probabilistic and Beyond

River channel flow

Storm suurge

Various
Models
Sedimentation andSoil runoffpof soiltransportation（with long‐（g
Takingworst case senarios into consuderations
term run）

Bu
uilding damagge by strong w
wind

Soil production

Sousei (2012-2016)

Inundationn including
undergrounnd shoppingmoole

Developing decision making
methodology under highuncertainty of risk
Developing decision making
methodology under noinformation on probabilitypy ofa worst case

Prediction and evaluation of disaster environment in JapanDPRI / Kyoto
Kyoto--Univ.

Proposing adaptation philosophy consistentwith
ith mitigation
iti ti philosophyhilhEvaluation

Nakakita (2012, 2015)- 38 -

Decreasing of safety against landslide, debris flow, flood, draught, storm surge and strong wind .
Assessment of current protection system and proposal of alternatives

New Time-slice Experiments of AGCM
by MRI have finished
δSST

YS

Cluster 3
Cluster 2

AS

Cluster 1
Ensemble mean

RCP

60kmAGCM

Present
climate

2.6

4.5

6.0

8.5

KF

20kmAGCM
5kmNHRCM

60kmAGCM
60kmAGCM
20kmNHRCM

Cloud Physics

ES 100x100 ensemble experiments

Sub-groups in Group D

Collaboration among Sousei A, C and D initiated in 2014

updated 2013/8/1

20 years×1 member i Climate change impacts on natural hazards (Eiichi Nakakita. Kyoto U) i-a Metrologicalgrisk (Takemi,
(
Kyoto
y
U))
12
 i-b River risk (Tachikawa, Kyoto U)25
 i-c Coastal risk (Mori, Kyoto U)18
 i-d
i d Risk management (Tatano,(Tatano Kyoto U)6
 i-e River risk in global scale (Suzuki, PWRI)
15

Increasing of
ensemblemember ii Climate change impacts on water resources (Tanaka, Kyoto U) ii-a Social-economic risk (Tanaka, Kyoto U)18
 ii-b Anthropogenic effects (Oki, U Tokyo)
8

20 years×100 members

1951~1970
1951
1970

＋4℃

 iii Change impacts on ecosystem and biodiversity (Nakashizuka, Tohoku U)
 iii-a Forest and lakes (Nakashizuka, Tohoku U)
4
 iii-b Social-economic impactp((Managi,
g , Tohoku U))4
 iii-c Impact in East and East-South Asia (Kumagai, Nagoya U) 10
 iii-d Coastal ecosystem (Yamanaka, Hokkaido)
10

Shiogama＠
NIES

i-a: Metrological Disasters (Takemi, KU)

Sub-groups in Group D

Downscaling of MRI
MRI-GCM/CMIP5
GCM/CMIP5 for impact
assessment of natural disaster

updated 2013/8/1

Dynamic downscaling andperturbation of tracks ofhistorical typhoon to estimate
worst class typhoon for natural
disasters

 i Climate change impacts on natural hazards (Eiichi Nakakita. Kyoto U) i-a Metrologicalgrisk (Takemi,
(
Kyoto
y
U))
12
 i-b River risk (Tachikawa, Kyoto U)25
 i-c Coastal risk (Mori, Kyoto U)18
 i-d
i d Risk management (Tatano,(Tatano Kyoto U)6
 i-e River risk in global scale (Suzuki, PWRI)
15

Participation by
RCM

PseudoPseudo
experiments for
Ise Bay
Typhoon ii Climate change impacts on water resources (Tanaka, Kyoto U) ii-a Social-economic risk (Tanaka, Kyoto U)18
 ii-b Anthropogenic effects (Oki, U Tokyo)
8

Statistical downscaling of
typhoon iii Change impacts on ecosystem and biodiversity (Nakashizuka, Tohoku U)
 iii-a Forest and lakes (Nakashizuka, Tohoku U)
4
 iii-b Social-economic impactp((Managi,
g , Tohoku U))4
 iii-c Impact in East and East-South Asia (Kumagai, Nagoya U) 10
 iii-d Coastal ecosystem (Yamanaka, Hokkaido)
10- 39 -

Projection ofheavy rainfalls in
Baiu rainy season+ 95％ cofidence+ 90％ conf
No change

Downscaling of GCM
for river discharge,
storm surge, waves
land side to evaluate
regional scale impact

i-b: River Disasters (Tachikawa, KU)

i-c: Coastal Disasters (Mori, KU)

DDS by team i-a / Bias correction, Statistical downscaling

Developing river discharge model
including natural processes and dam
operation.
Considering uncertainty of projection

External Forces
Storm
Surge

Sediment transport/Land slide
developing model to project
sediment behavior over river basin
scale. Making precipitation map
with hourly time scale

Wind
Waves

Impact Assessment
Beach/Tidal
Flat

Floodingl diDevelopinglmodel
d l off river flfloodingdffor major plainsl(Kanto, Osaka, Nagoya)

Coastal
Structures

i-d

i-d Economic imapct• We may be almost sure that average of extreme
design value would increase.
increase• However, projected increase in the design value ismerely rough estimation
estimation,• because, for example, the worst case typhoon for apriver basin may
y not be realized (computed)(p)
specific
in a single projected time series.
y important to estimate river• Therefore, it is very
discharge when a worst case typhoon would passthrough, even though we cannot estimate return
period.
i dPProductionn Capacity

Facility Damage+Recovery
Facility Damage + Recovery+Lifeline Impacts

Facility Damage
Facility Damage+Lifeline Impacts

Lifeline
Resilien
nce
Factor1

0

Time

Disaster

Economic Loss

There is high uncertainty
in projected design value

1. Developing integrated water hazard risk assessment
2. Socio-economic impact of inundation
3. Cost-benefit of adaptation

Recovery of Gas

Recovery of Water
Recovery of Electricity

Virtual Shifting of typhoon’s initial position- for makingg a worst scenario -Schematic ofreturntvalue’sl ’ uncertaintyt i tHigh uncertainty:
extremetevents,t
flood, land slide

GEV
Obs.
Model A
Model B

NHM-5km

Return value can
NOT be used as
Design level

Model A

Return value canbe used as design
level

i-b

LCC

ii-d:
d: Measuring socio
socio-economic
economic impacts of climatechange and effectiveness of adaptation strategies(Tatano KyotoU)(Tatano,Low uncertainty:
agriculture,
water resources

Flooding

Socio-Economic Impact
i-c Storm surge

AMS

Sea Level
Rise

Virtual Shifting of typhoons
initial position by keeping
potential
t ti l vorticity
ti it same(a vorgas method)

AGCM20

Dynamic downscaleby RCM

Obs. Can RCM reduce

the uncertainty
and bias?
Model B

Worst case impact assessmenton• Land：L d extremetwind
i d andd rainfall
i f ll• Ocean： storm surge and wave heightWith 25-years single time series
Konoshima and Nakakita (2010)

Ishikawa et al (2009)- 40 -

AGCM20mm/hhourly precipitation“worst track”
Central Tokyo

daily precipitation
Ｉｓｈｉｋａｗａ ｅｔ ａｌ．2009

Ｉｓｈｉｋａｗａ ｅｔ ａｌ．2009

Simulation of River Discharge using
Precipitation Output （Tone River Basin）

River discharge by the virtual shifting oft htyphoonwhich
hi h was projected
j t d byb GCM
River ddischargee（tonn/sec)

Main Points‐ Yakatahara (1677.5 km2)
‐ Yattajima (5133.6 km2)
‐ Tone‐Ozeki (6058.8 km2)
‐ Kurihashi (8772.2 km2)
Dam Points‐ Yagisawa Dam‐ Naramata Dam‐ Fujiwara Dam‐ Aimata Dam‐ Sonohara Dam‐ Kusaki Dam‐ Shimokubo Dam

Tone River
Basin

Tokyo

Current design
value(crresponding to
200 return
period)Track of a typhoon
projected by GCM20(listed from the top)

Oku et al (2009)

Possibility of peak discharge,
almost double of current design
discha ge
discharge

Tone-Ozeki
KurihashiTime

Yattajima (八斗島)Design Flow Rate：22,000 m3/s （200years）

Track of virtuallyshifted typhoon

Oku et al (2009)

PGW of Ise-bay Typhoon (1959)

Virtual Shifting of typhoon’s initial position
for Ise-bay typhoon (a Worst Case Scenario)
A t l condition
Actualditi- 41 -

PGW condition
ditiMax. wind

Reproduced

PWG

Ise Bay35.7(m/s)

41.1(m/s)

diffrence+5.4(m/s)

Osaka Bay

32.3

36.3

+4.0

(DPRI：Oku, Talemi, Ishikawa)

Simulated Inundation
PWG with sewage system in 1959

Multi-flooding disaster( i(riverandd stormtsurge flflooding)
di )PWG with sewage system in 2009

Developing river discharge model
including natural processes and dam
operation.
Considering uncertainty of projection

Floodingl diDevelopinglmodel
d l off river flfloodingdffor major plainsl(Kanto, Osaka, Nagoya)

Economic imapct
Storm surge

Kobayashi (2014)

Projected maximum storm surge heightwith inundation–typhoon Vera at Ise Bay-

Extreme typhoon Vera Extreme and shifted typhoon Vera(future climate)( future climate+ worst course)•••

Isewan Typoon Class senarios
Inundation depth caused by the storm surge
Damage ratio corresponds to inundation
depth

Property Losses

Multiple flooding disaster (river and storm surge flooding)• Worst scenario is different between storm surge and river floodingg• Storm surge• Key factors: central pressure and track of the typhoon,
astronomical tide
• River flooding• Key factors: intensity and duration of precipitation

Exceeedance Probabilityy

Typhoon Vera(historical run)

Toward Economic Impact Assessment（Estimating Property losses）

EP Curve

Worst senario

Economic Loss（DPRI KU：Tatano, Hatayama）

Shibutani et al, 2014

Sub-groups in Group D

Property Loss Estimation in
Ise Bay area

updated 2013/8/1

Focused area

B ildi
Buildings

Households

 i Climate change impacts on natural hazards (Eiichi Nakakita. Kyoto U) i-a Metrologicalgrisk (Takemi,
(
Kyoto
y
U))
12
 i-b River risk (Tachikawa, Kyoto U)25
 i-c Coastal risk (Mori, Kyoto U)18
 i-d
i d Risk management (Tatano,(Tatano Kyoto U)6
 i-e River risk in global scale (Suzuki, PWRI)
15

C it l gougedd in
i enterprisestiIt i ini enterprisestiCapitalInventories

Farmer’s capital goods

 ii Climate change impacts on water resources (Tanaka, Kyoto U) ii-a Social-economic risk (Tanaka, Kyoto U)18
 ii-b Anthropogenic effects (Oki, U Tokyo)
8

Farmer’s inventories

 iii Change impacts on ecosystem and biodiversity (Nakashizuka, Tohoku U)
 iii-a Forest and lakes (Nakashizuka, Tohoku U)
4
 iii-b Social-economic impactp((Managi,
g , Tohoku U))4
 iii-c Impact in East and East-South Asia (Kumagai, Nagoya U) 10
 iii-d Coastal ecosystem (Yamanaka, Hokkaido)
10（DPRI, Kyoto Univ.：Tatano, Hatayama, 2014）- 42 -

ii. Water resources and demandii-a Tanaka, KU; ii-b Oki, UTokyo
Near future

HI simulation
Nat simulation

Water Stress time series

Line ： GCM Ensemble mean
Shade ： uncertainty range（（Max/minin ５GCM））

variation of regional median
1980‐2099 （129yr）

2100

Nash coefficient based on monthly water flowing apresent climatep
Near futureRiver discharge

2100

D
DemanddSatoh, Yoshimura
and Oki (2014)

Stress• Almost linear increase in many region. （Except region 14,16,18,23,24,26.
14 16 18 23 24 26 Decrease in region 2）• Water resource management alleviate short‐term variation and long‐term change .
24
Change rate, besides absolute change, is very important in terms of adaptation.

withdrawn
time

Water stress depend on season

Change of water stressiii-a Vulnerability offorest/lakes andimpact assessment(Nakashizuka Tohoku U)

Sub-groups in Group D
updated 2013/8/1

 i Climate change impacts on natural hazards (Eiichi Nakakita. Kyoto U) i-a Metrologicalgrisk (Takemi,
(
Kyoto
y
U))
12
 i-b River risk (Tachikawa, Kyoto U)25
 i-c Coastal risk (Mori, Kyoto U)18
 i-d
i d Risk management (Tatano,(Tatano Kyoto U)6
 i-e River risk in global scale (Suzuki, PWRI)
15

Temperature +1℃

Risk
k for chaange

 iii Change impacts on ecosystem and biodiversity (Nakashizuka, Tohoku U)
 iii-a Forest and lakes (Nakashizuka, Tohoku U)
4
 iii-b Social-economic impactp((Managi,
g , Tohoku U))4
 iii-c Impact in East and East-South Asia (Kumagai, Nagoya U) 10
 iii-d Coastal ecosystem (Yamanaka, Hokkaido)
10

Methods

Ecosystem services Risks

Distribution model(－) Invasion into
croplandExpansion tonorthward

2℃

2.5℃

3℃
潜在非生育域
辺縁域

適域

適域→適域
適域→辺縁域
適域→潜在非生育域
辺縁域→辺縁域
辺縁域→潜在非生育域

Shimazaki et al. (2012)76

Planned activities of SOUSEI for TE
Ecosystems
Bamboo f.

Groowing areea

Subbalpine foorest ii Climate change impacts on water resources (Tanaka, Kyoto U) ii-a Social-economic risk (Tanaka, Kyoto U)18
 ii-b Anthropogenic effects (Oki, U Tokyo)
8

1.5℃

CC impacts on terrestrial ecosystems

Adaptation measure
Not to plant
Intensify management

Alpine plants
Distribution model

Tourism

Tree line shifts

Extinction risk

Secure refugia

Value decrease Protection area

Genetic model
Forests
Distribution shift
Typhoon damage
Disease expansion

Matter cycling
Water quality
Disaster RR
Disease RR

Acceleration
Pollution incl..
Disaster risk
Disease risk

Networking
Restoration
Structural improve

Trophic structure

Water quality

Pollution risk

Intensify management

Watershed model

Water quality

Pollution risk

Land use management

Yield model

Pollination

Yield decrease

Land use management• CC have already made impacts on ecosystems innorthern and higher elevation• The speed of the distribution shift have been measured( d smallerll thanh thath expected
db
i )
(and
by temperature rise)• The change is being modeled according to CC scenarios• Some adaptation measures could be suggested

Lakes

Watershed
Agricultural

Red: ongoing

Orange: started- 43 -

Sea surface temperature (SST) as a primary factor
d tdetermining
i i theth distribution
di t ib ti off marinei organismsiiii-d Assessment of multiple effects ofclimate change on coastal marine
ecosystem (Yamanaka, HokkaidoU)
Temperate ocean•Ocean acidification
prevent to growth

10℃

10℃

18℃
18℃

30℃

Sub tropical ocean

Bleaching (30deg)•Warm temperatureenhance Bleaching（Ωアラゴナイト）

Orange： coldest season for coral (10℃)
黒線：最暖月30℃（白化）Green：coldest season for tropical coral (18℃)

Shrink of coral
reef

Tittensor et al. (2010) Nature

Yara et al. (2012)

Effects of global warming and ocean acidification
on marine ecosystem considered in this study
Global warming

Who will suffer from ocean acidification?

Ocean acidification
L
Lower
C CO3
CaCO
saturation state (Ω)

Rising water
p
(T)
( )
temperature

Migration, bleaching
Migration,Mitienhanced metabolism

Migration,enhancedhd metabolism
b lDissolution

CoralsFish
Higher grazing pressure
on seaweeds by herbivores

Seaweeds

For sustainable use of marine resources
Natural factors

Human impacts• Inevitable

Global warming andocean acidification• Evitable• Evitable• Medium-term• Long-term• Short-termgMitigation(& Adaptation)g Mitigation• Predictablep Adaptation

Outlook SOUSEI Progam
for 2017+ • Predictable + • Predictable

GCM results

2017
2016

We need:• Further scientific knowledge for more accurate
d projection
j ti
assessmentt and• Adaptive responses based on precautionary principle• Paradigm shift from mass production/consumption
to sustainable society

GCM results

2014

Makingmodel
2012- 44 -

Projection
considering
uncertainty,
uncertainty
variability
and worst
class

Impactassessment,
mitigation
andadaptation

84

Joint Symposium on Adaptation with Japanese
Ministry of Land,
Land Infrastructure andTransportation (MLIT)

Steps towards National Adaptation Plan
Establish “Expert Committee on Climate Change Impact Assessment” at
114th Global Environmental Subcommittee, Central Environmental Council(2 July, 2013)

MLIT issued a guideline on strategy on adaptation for water related disastersuch as flood, storm surge, land slide and so on under the climate change in July. in 2

• Further detailed projection of climate change in Japan tomonitor extreme events• Assessment on climate change impacts in Japan• Analysis on risk information

Summarize climate change impacts
impacts, risk assessment and future issues(March Feb, 2015)• Extraction of priority areas/issues in short‐term (‐ 10 years),
middle‐term (10‐30 years) and long‐term (30‐100 years)

Develop National adaptation plan as a government‐wide integrated
effort (Decmber, 2015)

2015/5/29 国立オリンピック青少年金総合センター
総主催 文部科学省 気候変動リスク情報創生プログラム/
気候変動リスク情報創生プログラム/国土交通省 水管理国土保全局
後援 土木学会 水工学委員会/
水工学委員会/地球環境委員会[Slide drawn by MOE modified by Nakakia]

Thank you for your kindattention
tt ti
Joint Symposium
y p
between Sousei C and D

Photo: Uji, Kyoto- 45 -

Lecture 2: Fundamentals of Basin-scale Hydrological Analysis
Yasuto TACHIKAWA
Department of Civil and Earth Resources Engineering, Graduate School of Engineering,
Kyoto University
1.1 The Science of Hydrology
Hydrology is the science which deals with the waters of the Earth, their occurrence,
circulation and distribution on the planet, their physical and chemical properties and theirinteractions with the physical and biological environment, including their responses to humanactivity. Hydrology is a field, which covers the entire history of the cycle of water on the
Earth (UNESCO International Hydrological Decade, 1964). Water is the source of all lives on
the Earth and is a resource that is indispensable for our social and economic activities. The
water cycle and its time and space distribution depend on the solar radiation, topography andvarious conditions of the Earth surface. Hydrology is a discipline that provides theunderstanding of the physical processes of the water movement and the foundations forproper use and protection of water resources.
Key word: hydrology, hydrologic cycle, waters budget, water resources
1.2 Modeling of Rainfall-Runoff System
One of the main tasks of the hydrologists is to predict the hydrograph from knowledge of
rainfall, snowmelt and evapotranspiration information under an initial condition (initial soil
water) and catchment physical characteristics (topography, soil, vegetation) of the study basin.
The hydrologic cycle of a river basin can be regarded as a runoff system in which hydrologic
processes such as evaporation, transpiration, infiltration, subsurface runoff, and surface runoff
interact with each other. A straightforward way to predict the hydrograph is to represent therunoff system by combining mathematical descriptions of dominant hydrologic processes.
This mathematical representation is called a rainfall-runoff model. A rainfall-runoff model
plays a major role for river planning and river basin management. Simple conceptual runoff
models have been used for a long time. Detailed spatially distributed models and land surfacehydrologic models have been developed based on the advancement of observation technology
and hydrologic information such as radar rainfall observation, remote sensing of land surface
information, and geographical information of river basins.
Key words: runoff system, rainfall-runoff model, distributed rainfall-runoff model, land
surface hydrologic model- 47 -

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- 48 -

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- 50 -

 =#   %

"# #"     

Sun
Cloud

Latent heat release
Rainfall and snowfall

Solar radiation
Transpiration

Snowpack
Snowmelt

Earth radiation

Evaporation

Latent heat
Sensible heat

Infiltration

Evaporation

Slope runoff
Groundwater
Sea
River flow 



   

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    

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;  

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- 53 -  

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    1 " C%   DE     

Water flowing into
the control volume
per unit time
(Min, mass; I, volume㻕

!

Water flowing out of
the control volume
per unit time
(Mout, mass; O, volume㻕

Water stored in thecontrol volume(M, mass; S, volume)       #  " ## #           1 " C%D   

&

 # 1 
F B 6   :F F #    


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  1 " C. DE  #  1 "  ; #      C.   D  1 " ;

6  :  #  1 "
  C.   DE     #  "   ##  #

6  :F B 6   :F 1    ##  F
1 

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  0   ": # 1

6 :
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B 

6 :

     # #   # #

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


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1 )

  

 # #) 

)  -        

C.   D   
B

#  6 : 5   
C.   D-

-  # )) 

 

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-  # 


###

C.   D 8    6 : 1

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

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Atmosphericwater on the ocean
11

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0  -

Lecture 3: Fundamentals in rainfall-runoff-inundation modelling
Takahiro SAYAMA (Associate Professor, Disaster Prevention Research Institute, Kyoto University)
Abstract:
Flooding is a global phenomenon which causes widespread devastation, economic damage and lossof human lives. In steep mountainous landscapes, severe storms rapidly raise river water levels andoccasionally cause abrupt levee breaches, which often result in devastating flood disasters. On theother hand, in flat landscapes such as large river deltas, river floods and additional precipitation on
floodplains cause long-term inundations. Although seasonal flooding benefits agriculture andfisheries, as symbolized by "living with floods", changes in flooding patterns and scales due torecent climate changes may cause significant instability in maintaining sustainable societies.
Hydrologic modeling can predict such floods and contribute to mitigate the disasters in various
ways. It helps issue flood warnings based on real-time flood predictions and analyze flood risks forbetter river basin management. In fact, many rainfall-runoff models have been in use for theseobjectives, but their primary goal has been the prediction of river discharges, not the prediction offlood inundations. Many flood inundation models have also been developed, but they often require
discharge or water level boundary conditions at levee breach or overtopping points; therefore, thesemodel structures are not always ideal for real-time flood runoff and inundation predictions or floodrisk assessment at the large river basin scale.
The lecture introduces the idea behind the development of a Rainfall-Runoff-Inundation (RRI)
model, which is a two dimensional hydrologic model simulating for both rainfall-runoff processes
and flood inundation processes simultaneously at the river basin scale. The lecture covers also somebasics of runoff generation mechanisms and their model representations. Furthermore, some
application studies in various large scale flooding such as 2010 Pakistan Floods and 2011 Thailand
Floods are presented to discuss the new approach for flood predictions and risk assessment.

- 83 -

Exercise 5: Fundamentals in rainfall-runoff-inundation modelling
Takahiro SAYAMA (Associate Professor, Disaster Prevention Research Institute, Kyoto University)
Abstract:
Rainfall-Runoff-Inundation (RRI) model is a two-dimensional model capable of simulating
rainfall-runoff and flood inundation simultaneously (Sayama et al., 2012). The model deals withslopes and river channels separately. At a grid cell in which a river channel is located, the model
assumes that both slope and river are positioned within the same grid cell. The channel is discretized
as a single line along its centerline of the overlying slope grid cell. The flow on the slope grid cells is
calculated with the 2D diffusive wave model, while the channel flow is calculated with the 1D
diffusive wave model. For better representations of rainfall-runoff-inundation processes, the RRI
model simulates also lateral subsurface flow, vertical infiltration flow and surface flow. The lateralsubsurface flow, which is typically more important in mountainous regions, is treated in terms of thedischarge-hydraulic gradient relationship, which takes into account both saturated subsurface andsurface flows. On the other hand, the vertical infiltration flow is estimated by using the Green-Amptmodel. The flow interaction between the river channel and slope is estimated based on different
overflowing formulae, depending on water-level and levee-height conditions.
RRI model and its related various tools are originally developed with Fortran 90 language.
Although the model is fairly simple with limited numbers of parameters, to learn the model
application requires some basic understanding of the command prompt and GIS, which required
before some time investment to learn at the initial stage. To decrease the technical barrier, especiallyfor practitioners without much experience in hydrologic modeling, ICHARM, Public Works
Research Institute (PWRI) has been developing a Graphical User Interface (GUI) for smoothapplications and visualizing modeling procedures and results. RRI-GUI uses basically HydroSHEDS
dataset, global scale topography and flow direction dataset, so that any of a river basin (or multiple
river basins) in the world can be fairly easily modeled at least to start with. This lecture provides
RRI model with the GUI interface and introduces how to apply the GUI at river basins. It also covers
some basic idea about how to properly set model parameters based on observed records. Since themodel is freely available, we hope the model can be used in practice after participants being morefamiliar with the model.
Takahiro Sayama, Go Ozawa, Takahiro Kawakami, Seishi Nabesaka, Kazuhiko Fukami,
Rainfall-Runoff-Inundation Analysis of Pakistan Flood 2010 at the Kabul River Basin, Hydrological
Sciences Journal, 57(2), DOI: 10.1111/jfr3.12147, pp. 298-312, 2012.

- 84 -

Outline

Fundamentals in rainfall‐runoff‐inundation modelling

1. Basics of flood forecasting and warnings: Japanese case
2. Three types of “Flood Hazard Models” and objectives
3. Basics in Rainfall‐Runoff Modeling
4 Introduction
4.
I t d ti to
t R
Rainfall‐Runoff‐Inundationi f ll R
ff I
d ti (RRI) Model
M d lDisaster Prevention Research Institute, Kyoto University

Takahiro Sayama
12

Water Level Criteria for Flood Warning (Japan)

Flood Forecasting
Numerical Weather Prediction Model•

Numerical Weather Prediction
Model: Quantitative Precipitation
Forecasting (QPF).
()•

Rainfall‐Runoff Model: simulating
streamflow discharge withstreamflo
ith rainfall
input.

Precipitation
Rainfall-Runoff Model

Flood Danger Water Level
BankE
Evacuationti Alert
Al t Water
W t Level
L l
Flood Watch Water Level

Discharge•
Flood Inundation Model

River Routing Model•

Water Level

Inundation

River Routing Model: tracking
flood wave movement along a
open channel with upstreamhydrograph.

Riverbed

Flood Inundation Model:
simulating flooded water
spreading on floodplains withinflow discharge.

For residents…
When Flood advisory information is issued,
For basin residents, make yourself preparedand protection items available for sudden
evacuation. Also, don’t forget to check riveri f
information
ti att any given
i
ti
time.

Usual water level

For residents …
When Flood warning information is issued
issued,Pay attention to evacuation information
issued by your top local officer.
When an evacuation order is issued, quickly
evacuate under instructions given by floodfighting corps.

Water Level Criteria for Flood Warning (Japan)
Description of the drawing
Observed water level

Flood Danger
W t Level
Water
L l

８

１０
１１

３
１

４

５

６

２

１２

Fllood advisory reemovedFFloodwarning

Fllood advisory

Fllood notification

Fllood danger inform
mationF
FloodadvisoryF
FloodwarningFFloodwarningFFloodwarningFFloodwarning(N
No announcement))F
Floodadvisory

Title of the forecast

９７

Evacuation Alert
Water Level
Flood Watch
Water Level

Di t ib t d Fl
Distributed
FlooddF
Forecastingti S
Systemt

Forecasted water level

Flood Forecasting with
ith a Distributed
Distrib ted
Model using Quantitative Precipitation
Forecasting: 3 hours read-time, every 10
minutesminutes,
at every a few kilometer

Flood advisory : when water level reaches Flood Watch Water Level and is expected to increase
Flood warning : when water level reaches to Evacuation Alert Water Level and is expected toreach Flood Danger Water Level(MLIT)- 85 -(Hyogo Prefecture)

HEC

MIKE
Danish Hydraulic Institute (DHI)
http://mikebydhi com/
http://mikebydhi.com/•

US Army Corps of Engineers
Hydrologic Engineering Centers (HEC)
http://www.hec.usace.army.mil/

MIKE11– One‐dimensionalO diil riveriroutingti modeld l– NAM: A lumped, conceptual rainfall‐runoff model
simulating overland flow, interflow and baseflow as afunction of the water storage in each of four mutuallyinterrelated storages representing the storage capacity of
the catchment.
– SHE: Complex fully distributed process‐based oriented
modeling•

MIKE21– Flows, waves, sediments and ecology in rivers, lakes,
estuaries, bays, coastal areas and seas in two dimensions•

HEC‐RASHEC
RAS (River(Ri
AnalysisA l i System)S t )(1) steady flow water surface profile computations(2) unsteady flow simulation(3) movable boundary sediment transport(4) water quality analysis•

HEC‐HMS ((Hydrologicyg Modelingg System)y)– Elements: subbasin, reach, junction, reservoir, diversion,
source, and sink
– SCS curve number, Green Ampt
– linear quasi‐distributed unit hydrograph method– kinematic wave or Muskingum‐Cunge– Priestley‐Taylor method (PET)– Snow
S
melt
lt

7

8

Steps in the Modeling Process“Rainfall-Runoff Modelling, The Primer”, by Keith Beven

Importance of Perceptual Modeling

910

Surface Runoff Generation

林地の最終浸透能 ： 約200 mm/h

11

- 86 -

Runoff Generation Mechanisms

Peter Kienzler, ETH Zurich

14
Bloschl et al., Runoff Prediction in Ungauged Basins, Cambridge Press

Variable Source Area (VSA) Concept

Petra Fackle, ETH Zurich

Courtesy J.J. McDonnell

Hewlett and Hibbert (1967)
Hewlett and Troendle (1975)

Chris Graham PhD thesis

Storage Function Model

Tank Modelre(t)SurfaceCatchment is represented as a single storage tankds
 re (t  TL )  qddts kq p ,re  fr  r (t )  R 
f

Subsurface

s(t)

1

q(t)


f 
1 .0



sa

 t



  r (t )  R sa  t
Deep G
G.W.
W
re: effective rainfall (mm/h)
q: specific discharge (mm/h)
Q : q x A / 3.6: discharge (m3/s)
A: catchment area (km)17

- 87 -

Example of finite difference schemes (t   t )  s (t ) re (t  TL )  q t t18

Kinematic Wave Rainfall‐Runoff Model

Grid‐cell based Kinematic Wave
Rainfall‐Runoff ModelContinuous Equation

h q

r
t x
Kinematic wave model

q

i 5/3
h
n

q : discharge
h : water depthr : rainfall
i : slopen : Manning’sM
i ’ roughnessh
KINEROS

14

20

Kinematic Wave Rainfall‐Runoff Modelfor subsurface and surface flow

Lumpedp Model vs. Distributed ModelContinuous Equation

h q
r

t x
Kinematic wave model
for saturated subsurface and surface flowh  d 
kih

q i
5/3
 h  d   kih
nWhat are the limitation of lumped models?
- cannot reflect the spatial distributions of- rainfall, topography,p g p y state variables- cannot obtain stream flow at various points in a basinh  d 
21

22

Rainfall‐Runoff‐Inundation

Introduction to Rainfall‐Runoff‐
I
Inundationd ti (RRI) M
Model
d l
Motivations of using Rainfall-Runoff-Inundation Model
1. Rainfall-runoff and inundation cannot be separated with large inundation
2. Kinematic wave is not suitable for flat topography
3. Important for representing inundation process for better river predictions
4. Inundation itself may be of interest in flood forecasting or risk assessment
23

24- 88 -

Rainfall‐Runoff‐Inundation
Predictionof Thailand Flood 2011
(conducted on 2011/10/14)

Rainfall‐Runoff‐Inundation Model
Input

1D Diffusion
in RiverSubsurface + Surface
Vertical Infiltration

Rainfall

OutputW. Depth

Discharge
DEM
W. LevelLand CoverInundation
Nakhon Sawan

Cross Sec.

2D Diffusion
on Land
Simulation Domain : 163,293 km2
Grid Size : 60sec (1776 x 1884 m)
Simulation Period：
2011/07/01 – 2011/11/30
Input Rainfall:2011/07/01 – 2011/10/14
3B42RT (Satellite Based Rainfall)(Every 3hours, Spatial Resolution: 0.25 deg)2011/10/14 – 2011/10/21
JMA‐ GSM Weekly Weather Forecasting
JMA（Forecasting Lead Time: 8 days, Update every 12 hours）
2011/11/15 – 2011/11/30(Previous year’s 3B42RT rainfall in the same period)• Two‐dimensional model capablepof simulatingg rainfall‐runoff and flood inundation simultaneouslyy• The model deals with slopes and river channels separately• At a grid cell in which a river channel is located, the model assumes that both slope and river are
positioned within the same grid cell
Sayama, T. et al.: Rainfall-Runoff-Inundation Analysis of Pakistan Flood 2010 at the Kabul River Basin,
Hydrological Sciences Journal, 57(2), pp. 298-312, 2012.

20

1 : July 1
31 : Aug 1
62 : Sep 1
92 : Oct 1
123 : Nov 1
152 : Nov 30

Ayutthaya
Bangkok21
http://www.icharm.pwri.go.jp/news/news_j/111024_thai_flood_j.html

Example of RRI Model Application (1)Example of RRI Model Application (2)Real-time flood runoff and inundation forecasting system
for the Chao Phraya River Basin based on RRI Model(developed by JICA/FRICS and operated under RID, Thailand)

1. Flood Risk Assessment with RRI

http://floodinfo.rid.go.th/index_en.html

R i f ll
RainfallT
TopographyhFrequency
A l iAnalysis
Design Rainfall(1/50 1/100 …))(1/50,

L d
LanduseAgriculture
g

Urban

RRI Model

DamageAssessment

Inundation

Damageg Curves

Counter measures
JICA Project onComprehensive FloodManagement Plan for
the Chao Phraya River
Basin in Thailand 27

Flood Risk
32

Main difference between typical
distributed R‐R models and RRI?
Distributed R‐R Model

RRI Model

Flow directions are fixedb
based
d on ttopographyhFlow directions changeb
based
d on watert levelsllOne-dimensional Saint-Venant Equationqq uqgn 2 q 2h gh  ghi 0  7 / 3  0
t x
x
h
Kinematic Wave

Diffusive WaveLand

1D Kinematic (or similar)

Dynamic WaveRiver

2D Diffusive

29- 89 -

Basic Equations
Three Conditions of Surface / Subsurface Flow

Momentum equations

Shallow water equations
for typical 2D inundation

q x uq x vq x
H  x  ghtxyx  w
q y uq y vq y
H  y  ghyy  wt
x

Mass balance equationh q x q y


r
y
t
x

Momentum equations
Cell storage based modelwithith diff
diffusioni wave approx.
1
H

qx   h5 / 3
n(e.g. Hunter et al. 2007,)

Mass balance equation

x

Surface / subsurface flow conditions(A) Only overland flow
(no infiltration loss, no
subsurface flow)(C) Saturated subsurface+ Saturation excess
overland flow(B) Vertical infiltration+ Infiltration excess
overland flow H sgn  x 

i , j 1i, j
dh i , j qxi 1, j  qxi , j q y  q y H 
1
H
 r i , j q y   h5 / 3


sgn 

y
n
dt
y
x
 y 

i, j

r i , j : Rainfall,hi , j : Water depth, qx , y : Discharge between grid-cells

23

32

Key Steps in RRI Modeling
Topography

1 Input Topography
1.

RainfallDigitalgElevation Modelrain

dem dir,dem,
dir accacc

dem

Flow Direction

Flow Accumulation
dir

RRI Control File
RRI Input txt
RRI_Input.txt
RRI Execute(m)hs, hr, qr

4500
0HydrographhydroInundation

500
0These three datasets can be obtained from HydroSHEDS(SRTM based topography and flow direction dataset)

hpeak
25

34

2 Input Rainfall
2.
Rainfall Data
(input)

3 RRI Control File (RRI3.
(RRI_Input.txt)
Input txt)

Thiessen polygon(output)

RRI_Input_Format_Ver1_4_1
RRI
I
t F
t V 1 4 1./rain.txt./adem.txt./acc.txt./adir.txt/ di t t
0# utm(1) or latlon(0)1# 4-direction (0), 8-direction(1)2568# lasth600# dt
60# dt_riv
104# outnum
66.0d0# xllcorner_rain
23 0d023.0d0# yllcorner_rainlli0.1 0.1# cellsize_rain…
…
(continue)

Prepare rainfall data with time and location information (e.g. above excel file)and execute rainThiessen.exe program to create rainfall input file

Prepare RRI control file (RRI_Input.txt), which includes parameter settings,
and execute 0_rri_1_4_1.exe to run RRI model

35

36- 90 -

4 Visualizing Results
4.
hs

hr

qr
q

water depth on ground [m]river depth [m]river discharge [m3/s]

t=3
t=2
t=1

Inundation

Hydrograph

37

38

Key Terms•

Types of Rainfall
Rainfall‐Runoff
Runoff Models

State Variables– A variable in the model that is part of the solution of the model equations and which
varies with time during a simulation but which is not a flux or exchange of mass.

••

Empirical R-R model (black box model)--- Empirical functions are derived from rainfall-runoff relationship--- Rational method, Unit hydrograph•

Conceptual R-R model--- Constructed with conceptual functions of rainfall-runoff processes--- Storage function model,
model Tank model•

Physically-based R-R model (process description model)--- Constructed with fundamental equations of water flow dynamics--- Kinematic wave model

Boundary conditions– Constraints and values of variables required to run a model for a particular flow domain
and time period. May include input variables.

•

Initial conditions– Values of storage or pressure variables to initialize a model at the start of a simulation
period.

•Calibration– The process of adjusting parameter values of a model to obtain a better fit between
observed and predicted variables.
variables•

Validation– A process of evaluation of models to confirm that they are acceptable representations ofa systemsystem.
From “Rainfall-Runoff-Modelling, The Primer” by Keith Beven

40

39- 91 -

Lecture 4: Data Integration and Analysis System (DIAS) forwater-related disasters
Akiyuki KAWASAKI (Project Associate Professor, Graduate School of Engineering, The Universityof Tokyo)
Abstract:
DIAS (Data Integration and Analysis System) was launched in 2006 as part of the Earth Observation
and Ocean Exploration System, which is one of five National Key Technologies defined by the 3rd
Basic Program for Science and Technology of Japan. The mission of DIAS is: 1) to coordinate thecutting-edge information science and technology and the various research fields including
water-related disaster management; 2) to construct data infrastructure that can integrate earth
observation data, numerical model outputs, and socio-economic data effectively; 3) to createknowledge enabling us to solve the global environment problems; and 4) to generate socio-economic
benefits. DIAS is unique in the establishment of R&D community to support application and tool
development on data infrastructure, in addition to the building of data infrastructure itself
collaborating with ICT specialist. In the lecture the concept of DIAS from the viewpoint of threesystems, namely infrastructure system, application development, and R&D community is firstly
introduced. Then DIAS’s principle on data sharing in terms of data access, metadata, and data policy
is summarized. As a case study on DIAS’s contribution to solving water-related disaster risk, we
introduce flood management in Cambodia, Pakistan, and Philippines based on data analysis andintegration on climate change projection data and local in-situ dataset. The lecture concludes with
our thought on some of important directions in DIAS research and development toward theimplementation of the Sendai Framework for Disaster Risk Reduction 2015-2030.

- 93 -

Agenda

Data Integration andAnalysis System (DIAS)
for water-related disasters

1. Introduction
2. Data system: DIAS
3. System for inter-linkage: Water Cycle
Integrator
4. Opportunity
5. Summary

Akiyuki KAWASAKI
Department of Civil Engineering,
The University of Tokyoe: kawasaki@hydra.t.u-tokyo.ac.jp
w: http://wci.t.u-tokyo.ac.jp

1

2

Source: http://www.singularity.
Com/charts/page50.html

Agenda1. Introduction
2. Data system: Data Integration andAnalysis System (DIAS)

7 years

3. System for inter-linkage: Water Cycle
Integrator
4. Opportunity
5. Summary

35yers34

Source:http://www.singularity.Com/charts/page67.html

Source: http://www.singularity.Com/charts/page63.html 5- 95 -

6

Agenda1. Introduction
2. Data system: Data Integration andAnalysis System (DIAS)
3. System for inter-linkage: Water Cycle
Integrator
4. Opportunity
5. Summary
Information explosion=excessiveinformation?
78

Data Integration and Analysis System

Data Integration and Analysis Systema legacy for Japan's contributions to GEOSS

a legacy for Japan's contributions to GEOSS

To create knowledge enabling us to solve
the Earth environment problems and to
generate socio-economic benefits,

enabling us to do integrated research andto realize inter-disciplinarity

Water

ClimateFood

Fishery

Biodiversity

10

9eͲinfrastructure mapping in the world
Academic〜Society

EU 2007 ※2
Japan (2006)¾ Basicplatformdevelopmentoftheintegrationandfusioncoresystem¾ InstituteofIndustrialScience¾ Basicplatformdevelopmentoftheontologyregistrysystem¾ CenterforSpatialInformationScience¾ Basicplatformdevelopmentoftoolsfortheadvancedapplicationof
technologytothetaskofstatisticallyͲdriveninformationfusion¾ SchoolofEngineering¾ GraduateSchoolofAgriculturalandLifeSciences¾ CenterforClimateSystemResearch¾ AtmosphereandOceanResearchInstitute

Global (2013) ※1
Euro (2012)

EU 2011

Global(2008)

Euro&US (2014)
US (1998) ※1
US (2011)

KyotoUniversity,NagoyaUniversity,NationalInstituteofInformatics
JAXA,JAMSTEC,NationalInstituteforEnvironmentalStudies
HokkaidoUniversity,KanazawaUniversity,andMeteorologicalInstituteAcademic
Global (2007) ※2

11- 96 -

Data sharing

US (2009)※1：
No e-Infrastructure※2：
Focus on geospatial dataset

Data sharing〜Solution 12

Computational Modeling in Two Stages;
Driving Evolution & Enabling Revolution

DIAS:Structure
GRENEͲei

C lim a te
H y d ro e le ctric
C h an ge
p ow erA d a p ta tio n

R ECCAG E O S S /A W C I

G E O S S /A fW C C I

DIASͲPA S IA N M o n so o n Y e a r

SͲ8

10

S ocial
Im p lem en tation

Join t R esearch

H ealthIn tern ation al C on trib u tion

10

U rb an
B iod iversity

A g ricu ltu re

IC T E xp erts

D ata A rch ive

A p p lica tio n D e v e lo p m e n t

Field
S p ecialists

W ater10

D isaster
R isk
R ed u ction

Data Size, Bytes

R & D C o m m u n ity

Field S p ecialists

16

C lim ate

IC T E xp erts

E con om y

D ata Processin g

S earch / D ow n load

10

10

10

10

S to ra g e

2005

14

2qx 2q resolution;
synoptic scales

13

H ig h S p e e d

S e rv e r

10RevolutionaryThegrowthofdatavolumeis4timesand
computationalpoweris9
timesfasterthantheone
ofgrowthratioofglobal
economy(Economist)

2001

Homogeneous
Cluster

11

Information
Power Grid

Heterogeneous
Cluster

Evolutionary

10

TeraFLOPS

ExaFLOPS

PetaFLOPS

9
9
10

10
10

11
10

12

10

10

13

10

14

15
10

Compute Speed, FLOPS

16
10

17
10

18
10

14

Real Demonstrated Performance doing useful Science

Data Integration and Analysis System

Data Integration and Analysis Systema legacy for Japan's contributions to GEOSS

a legacy for Japan's contributions to GEOSS

accelerating data archiving, including dataloading, QC and metadata registration

tackling a large increase in volume of
the Earth observation data.
ÆNanotechnology

12

N e tw o rk

13

IPCCAR4(2007):40TB

Interactive
physics, biology,
chemistry;
assimilation ofsatellite data

GigaFLOPS

B a se S y ste m
A n a ly sis

2020

2015

2010

.25qx .25q resolution;
hurricanes, storm fronts

15

IC T E xp erts

H ig h C a p a city

.25qx .25q resolution;
add cloud, chemistry,& radiation effects

D IA S /C E O S
W a te r P o rta l

CMIP5

Fully interactive (biology,
chemistry, physics)
ensemble simulations inan operational mode

IPCCAR5(2012):2.6PB

2.7PB(2012)
600TB(2007)15

Data Integration and Analysis Systema legacy for Japan's contributions to GEOSS

accelerating data archiving, including dataloading, QC and metadata registration16Á¬¦¦¦¤Whereisdata?
Howtoaccess?

Ecosystem
Meteorology

D
B

农业

DB

DB

350
300
A 1:高度成長社会
A 2：多元化型
B 1:持続発展型
B 2：地域共存型

250
200
150
100
50

1992

1994

1996

1998

2000

2004

2002

2006

2008

2012

2010

2014

2016

2018

2020

0

DB

DB

농업

Quality?
Reliability?

DB
350
300
A 1:高度成長社会
A 2：多元化型
B 1:持続発展型
B 2：地域共存型

250
200
150
100
50

2018

2020

2016

2014

2012

2010

2008

2004

2006

2002

1998

2000

1996

0
1994

DB

1992

Agriculture

Hydrology

DB
350
300
A 1:高度成長社会
A 2：多元化型
B 1:持続発展型
B 2：地域共存型

250
200
150

Climatology

TechnicalTerms
among
Different
Disciplines

100
50

1994

2010

2008

2004

2006

2002

2000

1992

1998

1996

2012

2018

2016

2014

2020

0

DB

LandUseDB
AsiaMonsoonYear
24project,277stations

ClimateData
16RiverBasin,202stations

HealthDB

18RiverBasin,280stations
completed17- 97 -

DB

18

18

Data Integration and Analysis System

Data Integration and Analysis Systema legacy for Japan's contributions to GEOSS

a legacy for Japan's contributions to GEOSS

enriching data searching capability

tackling a large increase in diversity of
the Earth observation data.
Ontology

MetadataDesign

19

Data Integration and Analysis System

20

DataIntegrationandAnalysisSystem(DIAS):Overview

a legacy for Japan's contributions to GEOSS

ClimateChangeModeling

enriching data searching capability

ClimateEconomicImpact
AssessmentCreateScientific
Knowledge

Econo
my

Scientific
ObservationData
Research
Community

Input

Research
Institutions
SupportSpace andSatellite Agencies

CreatePublic
Value

Water

HealthRisk
Management

Disaster
Risk
Reduction◀

International
Research
GroupsDemographicandSocioͲ
EconomicData

Preparedness

HealthDataAnalysis◀ RemoteCollaboration◀ CrossͲsectorand
InternationalCollaboration◀ CapacityDevelopment

Resilience
Assessment

Private Sector

Input

International
Development
Agencies

SupportPublic Sector¶野県

PolicyMaking
Ź SocioͲEconomic
AnalysisŹ DisasterRisk
Management
Ź UrbanPlanning
Ź

Urban

Agriculture
Biodiversity

UrbanResilience
Assessment

Policymakers

NGO
Civil Society

Adaptationand
Mitigation

Biodiversity
Assessment

22

21

DIAS:CaseStudies

O n‑ going projects and practices

B iod iversity・W ater
cycle・C lim ate

A g ricu ltu re

D isa ste r R isk
M an ag em en t

C lim ate・W ater
cycle ・E con om y・A g ricu ltu re• C lim ate Pred iction / E arly W arn in g
‑ H im aw ari8 W eath er S atellite d ata‑ R ain fall O b servin g d ata

Pakistan ：In d u s R iver
C lim ate ch an g e p red iction ,flood an d p overty m easu res• In frastru ctu re R isk M an ag em en t

• IPC C 5 In tern ation al A g reem en t
• C ollab oratin g w ith G lob al E arthO b servation S ystem of S ystem s(G E O S S )C lim ate・W ater cycle

D ata C ollection
D ata A rch ive• E n viron m en tal R isk M an ag em en t
• A g ricu ltu ral Projection

B u rkin a Faso: V olta R iver
C lim ate ch an g e m easu res・
Flood p red iction

D ata A n alysis

C lim ate
S ou rces an alysis of C O 2
M eth an e in E ast A sia

In d u stria l

C lim ate・W ater cycle
Y osh in o R iver
C lim ate ch an g e m easu res,flood p red iction

H an oi: Flood con trol,
h yd roelectric p ow er su p p ly

A p p lica tio

C o n trib u t
io nn• R ed ‑tid e p red iction in aq u acu ltu re

AFRICA• E n h an ce S afety in A viation In d u stry

C ross‑S ector
C ollab oration

D ow n scalin g
S im u lation• In teg rated W ater R esou rces
M an ag em en t for h yd ro p ow er

GEOSS/AfWCCI• C lim ate C h an g e M o n itorin g in N ag an o p ref.
• C on sen su s‑b u ild in g in U rb an Plan n in g
• D isaster R isk Pred iction an d Prep ared n ess
for T orren tial R ain s

ASIA

H okkaid o: S eiyou d istrib u tion
S h ib u to R iv.: R iv. reg en eration

W ater cycle

In te rn a ti
on al

C lim ate・W ater cycle
T u n isia: M ed jerd a R iver
C lim ate ch an g e
p red iction ・ Floodm easu res

S u sta in a b le
S o cie ty

C lim ate

R esearch C om m u n ity

C lim ate ch an g e in A sianm on soon reg ion an d
its im p act on ag ricu ltu re

Pu b lic S ector

A frica:C O 2 em ission s
estim ation

Private S ector

23- 98 -

C lim ate・W ater cycle・U rb an ・H ealthD h aka: A tm osp h ere, flood ,u rb an h ealth risk
Laos: In fection d istrib u tionan d rain fall p red iction

G EO SS/AW CI

C lim ate・W ater cycle・U rb anC lim ate・A g ricu ltu re

Ph ilip p in es：M an ila
C lim ate ch an g e
p red iction ・Floodm easu res

T h ailan d : C lim ate ch an g e
im p act on ag ricu ltu re
C lim ate・W ater cycle・A g ricu ltu re
C am b od ia：S an g ker R iv.
clim ate ch an g e p red iction ,ag ricu ltu ral su p p ort
C lim ate・W ater cycle
K en ya: N yan d o river
C lim ate ch an g e m easu res・
Flood p red iction ・A g ricu ltu ral su p p ort

C lim ate・w ater cycle・U rb an ・B iod iversity
H ach ioji: C lim ate ch an g e
p red iction ・Flood p red iction
B u tterflies m on itorin g

C lim ate・W ater cycle・U rb an ・H ealth

C lim ate・W ater cycle・U rb an ・ A g ricu ltu re

V ietn am : Perfu m e R iverU rb an flood p red iction an d
H ealth at th e tim e of flood

In d on esia：C itaru m R iv. Flood p red iction
M u siR iv.： C lim ate ch an g e p red iction24

D IAS O fficial w ebsite < http:/ / w w w .diasjp.net>

3 5 tools and applications are now available!

FullEnglishwebsiteisnowunderconstruction!

< http:/ / w w w .diasjp.net> 26

25

Applications and tools

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(MLIT),Japan

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Infrastructure,TransportandTourism
(MLIT),Japan< http:/ / w w w .diasjp.net>

31< http:/ / w w w .diasjp.net>

Agenda32

3. System for inter-linkage:
Water Cycle Integrator

1. Introduction

3-1. Coupled A-L Das (CALDAS)

2. Data system: Data Integration and

3-2. Real-time flood forecast system:

Analysis System (DIAS)

Upper Tone Basin and Red River, Vietnam

3. System for inter-linkage: Water Cycle

3-3. Seamless integrated river-sewerage-coastal

Integrator

hydraulic model: Tsurumi River

4. Opportunity

3-4. River-sewage-public health: Hue, Vietnam

5. Summary

3-5. Water-climate-agriculture workbench:
Cambodia
33

34

Water Cycle Integrator

Coupled A-L DAS (CALDAS)-COUPLER

ModelsWeatherPrediction

SeasonalPrediction

ClimatePrediction

Vis.
IR

DataAssimilation
River Flow
Water
Cycle
Model

Snow

Soil
Moisture
GravityRain
fall

RainfallAgriculture
SupportRiver
Flow

Observation
Atmospheric
Model

SewageLoadingUnit

Crop
Model

ClimateChange
Adaptation

RiverBasinManagement
GroundWater
Val.

GroundElevation
Urban
WaterModel

IWRM

Evapotranspiration

MW

Urban
Environment
ConservationInͲsituData

Assimilation
SchemeCOUPLER
Cloud Microphysics
Atmospheric State
Scheme(Moisture & Temp.)

AgriculturalInfo.

Index
Organism

Biodiversity
Data

Land Surface
SchemeInͲsituData
GroundWater

6.9&10.6GHz
23 & 89 GHz

Validation
Time Integration
Grid Integration

Reference Site
Data Products

Famer’sNeeds

socioͲeconomic&
recognitioninformation

Soil
Moisture

Aqua AMSRE

Nesting

Evaluation&BiasCorrection
Satellite

GCM

Radiative
Transfer
Model
Soil RTM
Atm. RTM
Surface emission at High Freq.

Water
Quality35- 100 -

36

InsituͲstation

CorrectedGCMavg

FutureCorr_GCMavg

REALͲTIMEDATAINTEGRATION
EnsembleFloodPrediction

FutureͲ Past

Data Integration and Analysis System (DIAS)
Meteorological Data

Probability:
10year

Temp.

wind

rain

sunshine

Pre-processing
Model input data

River, Dam

Radar, NWP
Radar(C-band)NWP Output(GPV)

Runoff Water Level
Dam: WL, inflow, release

Real Time DataManagement System
Prediction System
Pre-processing
Radar, GPV

WEB-DHM

Ensemble Rainfall Prediction
RainfallPattern 12
RainfallRainfallPattern
Pattern N
Ensemble Flood Prediction

Probability:
100yearRiver Runoff
Soil Moisture

Statistical Analysis
Error
Estimation

Flood
Pattern 12
Flood
FloodPattern
Pattern N

37

38

Integration of River-Sewage-Public Health in Hue, Viet Nam

WaterͲClimateͲAgricultureWorkbenchinCambodia

StakeholderMeetingforCoͲdesign

StakeholderMeetingRealͲtimeRainGaugeÆ SatelliteDataCorrection
Æ WideDataDissemination
Famers’Needs&Experiences

inundationaffected
byriverfloodand
seatide.

risksofinfectionfrom
exposuretopathogens

waterquality:
stagnantordilutedbyriverwater.
1

300

0.6

200

0.4

Waterdepth

0
9/17
0:00

HolisticViewofWaterͲClimateͲAgricultureProblems

WaterCycleͲRiceProduction
CoupledModel

400

0.8

0.2

NationͲwideDailySoil
MoisturefromSatellite

500

EC

9/17
12:00

100
0
9/18
0:00

EC(ʅS/cm)

Waterdepth* (m)

1.2

39

LocalInformation

ClimateChangeAnalysisTools

OJT forLocalPractitioners

RiceProductionMonitoring

40

Agenda

GEO and GEOSS
1. Introduction
2. Data system: Data Integration and• GEO (Group on Earth Observations) is an
Intergovernmental Initiative

Analysis System (DIAS)– 60 Nations– European Commission– 43 Participating Organizations

3. System for inter-linkage: Water Cycle
Integrator• With a Single Objective: GEOSS

4. Opportunity– To establish a global, coordinated, comprehensive
and sustained system of Earth observing systems

5. Summary

41- 101 -

42

Global Earth Observation System of Systems（（GEOSS）

44

43

GEOSSAsianWaterCycleInitiative(AWCI)
moviepresentation(4:27)

44

Agenda1. Introduction
2. Data system: Data Integration andAnalysis System (DIAS)
3. System for inter-linkage: Water Cycle
Integrator
4. Opportunity
5. Summary

45- 102 -

46

Lecture 5: Fundamentals in Flood Frequency Analysis
Exercise 4: Flood Frequency Analysis
Shigenobu TANAKA (WRRC, DPRI, KYoto University)

In the risk management or the risk assessment of water-related disasters, the relationshipbetween the frequency of hazard and its consequence is indispensable. The probability of rare
events is important. The aim of this lecture is to introduce fundamental knowledge of flood
frequency analysis. There are, in general, two ways of preparing sample for flood frequency
analysis based on Extreme Value Theory, that is, Annual Maximum Series(AMS) and Peaks
Over Threshold(POT). The method using AMS is popular for flood frequency analysis.
Generalized Extreme Value distribution(GEV) including Gumbel distribution as its special caseis used corresponding to the analysis of AMS. It is easy to understand that larger hazards cause
disasters. Since AMS consists of larger events in the regions where many independent flood
events occur in a year, AMS is useful. But there are different regions where very small floods orno flood occur in some years such as semi-arid area. In this case, AMS is no longer applicable
and POT is useful. Generalized Pareto distribution(GP or GPD) including Exponential
distribution as its special case is corresponding to POT. Further, when both AMS and POT
extracted from a time series are available, it is interesting whether the result of each analysis
almost coincides or not.
This lecture provides sampling method and corresponding analyses for both sampling method,
AMS and POT. When we use POT, we have to extract independent peaks and then there is an
issue of selecting threshold. A selection method of threshold based on Exponential distribution
is introduced.
Through this lecture, it is introduced that AMS, POT, probability density function(PDF),
cumulative distribution function(CDF), return period, return level, Gumbel distribution,
Generalized Extreme Value distribution(GEV), Exponential distribution, Generalized Pareto
distribution(GP), method of moment, L-moment, probability paper, plotting positions. After thislecture, you will exercise how to do in practice.

- 105 -

FLOOD FREQUENCYANALYSIS
Shigenobu Tanaka
Ch
hao
o Phr
hray
ayaa Ri
Riveverr FlFloo
ood,d, Tha
hail
ilan
and
d
Rangsit Canal on 12 Nov 2011

CHAPTER 1∏

FREQUENCY
ANALYSIS OF
EXTREMES
In order to plan or design the disastercountermeasures, future extreme isone of the important issues. When the
maximum record or smaller event isused for design, we don’t needfrequency analysis. However, if we
use extrapolated extreme for design ofinfrastructure, we need frequency
analysis of extremes. Generally, when
the asset in the flood prone area
become large, we need more insuranceor safety. Countermeasures against
such extreme event should be
implemented before the occurrence.
Shi
Sh
S
hig
igen
enob
enob
obu Ta
obu
Tan
naak
kaa
Watteer Re
Wa
Wate
Reso
Reso
sour
sour
urce
urce
ces RecesReses aarrchh Cen
ente
teerr,, DPR
PRI, KU- 107 -

Explanatory Hazard Variable for Flood Damages

SECTION 1

What is the key variable for representing flood magnitude?

REPRESENTATIVE HAZARD•

With which variable can we express flood
frequency or flood magnitude?

•

In Japan, since one of the most probable cause ofdike breech is overtopping of flood water, peakwater level is important but the level is easily
affected by river morphological change. Peakdischarge is an alternative but the observation
period is very short. Then, rainfall during design
rainfall duration is used as the variable for flood
frequency analysis.

•

In the earlier stage of modernization of Japan, thefloods once in several years or ten years were used
for design of flood control facilities such asembankments.

•

Later, the maximum flood record was used for thedesign.

•After the World War II, the way of using the
maximum flood has been switched to probabilistic
flood and further, not only flood control but also
water resources development by reservoir became
popular, which needs hydrograph of discharge.

•

Nowadays, assessment of flood risk throughplanning of flood control facilities have been
carried out with rainfall data.

•

Because rivers without sufficient embankment
such as the Chao Phraya will cause inundation
with moderate flood, inundation volume isappropriate to the representative hazard variable
for flood damages. It is found that the inundation
volume of the Chao Phraya can be estimated by 5month rainfall depth of the basin.

Flood frequency shows relationship between the magnitude of flood and how often theflood occurs. The magnitude of flood generally depends on rainfall characteristics. In a steep
river basin, peak discharge is the most important. On the contrary, in a very flat flood plain,
flood inundation water volume is the most important. The former depends on mostly rainfall
intensity and its pattern in time and space but the latter on total rainfall in some period. Further,like in Japan, most rivers are installed with continuous embankments on both sides. Onceembankment breach occurs, flood water spreads to the flood plain. Because soil embankment isvery vulnerable for over topping, whether overtopping occur or not is critical. Water level canbe determined by the peak discharge. Then, peak discharge can be one of the most importantfactors. So, the peak discharge is used for assessing flood magnitude in practice. However,discharge observation period is usually not so long and discharge and/or water level at a station
has been influenced by upstream flooding condition. On the other hand, rainfall observation has
been carried out for rather long period compared to discharge observation and not affected byalteration of upstream basin and river channel. Furthermore, the conventional hydrologicalfrequency analysis is based on stationary condition.
Concurrently with the application of probabilistic flood and quick development ofurbanization, water resources in many river basins had become short and flood control by dam
had become necessary to protect existing urbanized area in flood plain. In this context, the peakdischarge of hydrograph and catchment averaged rainfall in design rainfall duration have been
key variables for the flood risk assessment in Japan.
If there exists no sufficient embankment along the river channel such as Chao Phraya riverin Thailand, even moderate floods cause inundation and then flood damage. Generally, flood
damage depends on water depth and inundation extent. The water depth and inundation extent
can be explained by the inundation volume. In such case, the inundation volume should be used
for assessing flood frequency. It is investigated that the total amount of five month rainfall
2

explains the inundation volume in the Chao Phraya river basin well.
Explanatory hazard variable depends on dominant factors of flood damage in considering flood plain. Once we know the relationship between
frequency of explanatory hazard and flood damage in money, we can know expected annual flood damage.
Peak discharge

Flood frequency can be
expressed in terms of

Catchment average rainfall
during design rainfall duration

Good relationship

Flood Damage $Inundation volume

Expected Annual Flood Damage

Discharge

Water Level

Discharge and water level inCampong Cham, in Cambodia.

Inundation frequency in Chao Phraya basin
3- 108 -

Relationship between Annual Maximum
5 months rainfall and calculated
Inundation volume in Chao Phraya basin

Relationship between how muchrainfall and how often

SECTION 2∏

When you have a set of observations (X1, X2, .

TERMINOLOGY

width, for example, 20 mm in the following example and count the number of Xi which falls ineach bin such as 6 for from 40 to 60 mm and 30 for 60-80 mm. Histogram plots these numbers
against bin specifications. Histogram
30
shows relative frequencies of variables.
HistogramTotal of the number of each bin is equal25
to sample size n. It is seen that the20
frequency of big rainfall becomes
15
small, that is, extreme event occurs
seldom.
10• Histogram• Random variable : usually of events

expressed by X, lower case x a
possible value of X

• PDF : Probability Density

Function, usually expressedf(x)• CDF : Cumulative

Next, if we consider total area of thehistogram as 1, the height of each bin
will be relative frequency(height ofeach bin divided by sample size)
divided bin width. So, the dimension ofy axes will be 1/(dimension ofvariables). In this sample, there are nodata between 280-360 mm. It is
expected when the sample size become
very big and the width of bin become
very small, the envelope will be rathersmooth.

5Distribution Function, usually
expressed by F(x)

0

50

100

150

200

250

300

350

Annual maximum daily rainfall• Non-exceedance probability :usually expressed by F or p

• Quantile : xp is expressed

inverse form of F(xp)=p0.010
Probability density• Exceedance probability : 1-F. . , Xi, . . . , Xn), select arbitrary bin

Histogramwith PDF

0.008
0.006
0.004
0.002
0.000

0

50

100

150

200

250

300

350

Fig. 2.1 Histogram and PDF fitting

Annual maximum daily rainfall

4Here, we’d like to see several important definitions. PDF(probability density function) is
defined as derivative of CDF(cumulative distribution function).
When we take annual maximum series as variate, Return Period T is defined as 1/(1-p), thereciprocal of the exceedance probability. When p=0.95, 1-p=0.05, for example, then T=20.
Confirm the relationship between PDF and CDF in the pictures below.

• Return period :

T=

1
1−p

0.010

μx = E[X ]0.008• Variance : Var(X) or σ 2x2

σx2 = Var(X ) = E[(X − μx) ]• Sample : a set of observations

PDF : Probability
Density Function

0.006
0.004

Mean

0.000

0

100

. . , Xi, . . . , Xn)• Sample size : n• Sample estimators_ 1 nμ^x = X =
Xi
n∑
i=1

n
_ 2
1
X
−
X)
σ^2x = S 2 =
i
(
n−1∑
i=1

200

300

400

Annual maximum daily rainfall

pd 1.0
CDF :Cumulative
Distribution
Function

0.8

0.6

0.4

p0
0.2

0.0

(unbiased estimator)

exceedance
Probability :1-p

0.002

Cumulative Distribution Function(X1, X2, .

Probability density• Mean of a random variable X

0

100

200

300

400

Annual maximum daily rainfall

designlevel

no damagelevel
5- 109 -

Fig. 2.2 PDF and CDF

Extremes and Extreme Value
TheorySECTION 3∏• Block Maximum : Annual

We know from experience that the severer natural hazard is, the more infrequent. And also,
larger hazard causes extensive damage. Since usual hazard event does not cause significant
disaster, our concern is that how large (or small) event will occur how frequent, that is,probability of such unusual event. The extreme value theory deal with this problem.

Generalized Extreme
Value(GEV) distribution

AMS(Annual Maximum Series) and POT(Peaks Over
Threshold)

EXTREME VALUE
THEORY
Maximum Series(AMS)

F[x] = exp −x−ξ
1−κ( α )}{

1/κ

Gumbel distributionx−ξ
F[x] = exp −exp −
{
α }]
[

In this process, mean value such as the mean of the daily precipitations in a year is notuseful. Such rainfall does not cause disaster. Our concern is big events which cause disaster.
There are several definition of “big events”.The “block maximum” is usually used to investigate
the behavior of extreme value. Annual maximum daily precipitation, annual maximum
temperature, annual maximum peak discharge, annual maximum inundation area and so on are
the examples of block maxima. In this theory, the probability distribution of block maxima is
discussed not probability of whole data. This means you don’t need to collect whole data but justannual maximum series(AMS). The other popular definition of “big events” is peaks overthreshold(POT). In this course, distributions for analyzing AMS and POT will be explained,
respectively.

Generalized Extreme Value Distribution(GEV)
It has been proved that there are just three types of extreme value distribution, that is,Gumbel-type distribution, Fréchet-type distribution and Weibull-type distribution. These three
types of extreme value distribution can be combined into a single form

6

1/κ

x−ξ
F[x] = exp − 1 − κ( α )}
{

POT

(3-1)

• Peaks Over Threshold

Generalized Pareto (GP)distributionx−ξG(x) = 1 − 1 − k
(
α )

1/k

which is called Generalized Extreme Value(GEV) distribution, and where ξ:location parameter, α:scale parameter, κ : shape parameter. When κ < 0, GEV is Fréchet-type distribution and has afinite lower bound x > ξ + α /κ, on the other hand, when κ > 0, GEV is Weibull-type distribution
and has a finite upper bound x < ξ + α /κ.Please note there are two forms of definition of GEV
with different sign of shape parameter. Handbook of Hydrology uses the form of Eq.(3-1).
In the special case κ=0, this equation becomes Gumbel distribution.

Exponential distributionx−ξG(x) = 1 − exp −
{
α }

x−ξ
F[x] = exp −exp −
{
α }]
[

(3-2)

where −∞ < x < ∞.
In order to estimate the risk of large disaster, the probability F(x) is indispensable. The risk isgenerally defined as expected annual flood damage and in order to estimate it, we need theprobability distribution function F(x) of concerning hazard and damages as its consequence.

Generalized Pareto distribution(GP)In hydrological frequency analysis, AMS is widely used. However, there are cases that they
don’t record every year’s maximum event but all events that they operate their barrages for theflood mitigation and no record in dry years. Every flood with barrage operation is significantly
large and usually independent and we can regard them as POT. In this case, we cannot apply
GEV distribution including Gumbel distribution any more. Generalized Pareto distribution(GP or
GPD) including Exponential distribution is used for denoting probability of POT.
Eq.(3-3) shows the CDF of GP.

7- 110 -

1/kx−ξG(x) = 1 − 1 − k
(
α )

x−ξ
G(x) = 1 − exp −
{
α }

(3-3)

(3-4)

x−ξ

where ξ:location parameter, α: scale parameter, κ : shape parameter.
Please note there are two forms of definition of GP with different signof shape parameter. Handbook of Hydrology uses the form of Eq.(3-3).

exp{− α }g(x) =

α(3-5)

The mean of the PDF of Exp becomes α in case of ξ=0. Using thischaracteristics, we can select effective threshold(see sample mean
excess function(SMEF)).

In the special case κ=0, this equation becomes Exponential
distribution. The CDF and PDF of Exponential distribution(Exp) are
shown by Eq.(3-4) and (3-5).

350
300

POT Threshold : 85 mm, Sample Size : 63

250

AMS

200

POT

150
100
50
0

1960

1980

2000

350
300

POT Threshold : 105 mm, Sample Size : 36

250

AMS

200

POT

150
100
50
0

1960

1980

2000

Fig. 3.1 Example of AMS and POT
8curves. AMS is fitted with Gumbel distribution and POT with
Exponential distribution.

Figure 3.1 shows an example of time series of two day rainfall in
62 years and includes AMS and POT. Red points connected with redlines are AMS and blue points with vertical fine lines are POT. The
threshold of POT is 85mm in the top figure and 105mm in the botom
one. Histograms of AMS and POT are shown in Fig. 3.2 with fitting

There is an equation (3-6) which connects AMS and POT.
F(x) = exp{−λ(1 − G(x))}

where F(x):distribution function corresponding AMS which shows theprobability that the annual maximum in a year will not exceed x,
G(x):distribution function of POT which shows the probability of lessthan x, λ:arrival rate of POT. Eq.(3-6) can be also useful in probability
plot of AMS samples with POT’s.

0.012

2day rainfall (AMS)

Probability Density

0.010
0.008

Gumbel0.006

Selection of Threshold

0.004

As mentioned previously, an interesting characteristics of Exp isused in selecting threshold. Sample mean excess function(SMEF)
shown Eq.(3-7) calculate the mean of variable x more than u.

0.002
0.000

0

100

200

300

400

500

600

2日降水量mmn

en(u) =

0.030

2day rainfall (POT)

Probability Density

0.025

0.015

Exp

0.010
0.005

0

100

200

∑i=1 (xi − u)I(u < xi)n∑i=1 I(u < xi)(3-7)

where, I(u < x) : 1, if u < x and 0 otherwise. When x more than c aredata from Exponential distribution, then Eq.(3-7) is constant in therange u>c. An example of SMEF is shown in Fig. 3.3(bottom right).

0.020

0.000

(3-6)

300

400

500

600

2日降水量mm

Fig. 3.2 Histogram of AMS and POT
9- 111 -

Catchment
average 2 day annual maximum rainfall
日吉ダム上流域流域平均2日降水量

Gumbel Probability Paper
GumbelGP

Catchment average 2
日吉ダム上流域day
rainfall

0.99

350
300

GEV

降水量mmRainfall
(mm)

0.995

Exp

0.98

250
200
150
100
50

0.95

0
F

1960

1980

2000

0.9

Sample Mean Excess Function
2日降水量極大値の標本平均超過関数
60

0.8
0.7

閾値超過平均値
SMEF

55
0.5

POT

0.3
0.2
0.1

50

45

AMS
40
0

100

200

300

400

500

60

2 day rainfall
(mm)
2日降水量
（mm）

80

100

120

140

160

2 day2日降水量極大値閾値mm
rainfall threshold (mm)

180

200

Fig. 3.3 Example of AMS and POT comparison with time series and SMEF
10

SECTION 4∏

Product Moment and L-Moment
Product Moment

PRODUCT MOMENT

μ=• Mean μ

 μ =

∞

∫−∞
∞

2

x − μ) f(x) ⅆx∫−∞ (•Gumbel Distribution’s mean μ

and variance σ 2 μ = 0.5772 α + ξ
π 2α 2 σ =
6
2∞

xf x ⅆx∫−∞ ( )

σ2 =xf(x) ⅆx

•Variance σ 2 σ 2 =When PDF of variable x is denoted as f(x), Mean μ and Variance σ2 of x are expressed as,

∞

(4-1)
2

x − μ) f(x) ⅆx

∫−∞ (

(4-2)

These values are called Product Moments. f(x) of two parameter distribution includes parameterssuch as location and scale parameter. GEV is a three parameter distribution and has additionalparameter, shape parameter.
In practice, nobody knows the population characteristics such as the parameters of f(x). In
order to fit a distribution to given sample, theoretical moments should be same as unbiased
sample moments. From this relation, parameters are determined. For example, in Gumbel
distribution’s case, f(x) is expressed as follows.
x−ξf(x) =

exp{− α }αx−ξexp −exp −
{
α }]
[

(4-3)

Then, by substituting Eq.(4-3) into Eq.(4-1) and (4-2) and conducting integration, we can obtain
μ and σ2 for Gumbel distribution as follows.
μ = 0.5772 α + ξ(4-4)π 2α 2
6

(4-5)

σ2 =

11

- 112 -

When we replace product moments μ and σ2 with sample estimators μx and σx2, respectively, we
get the simultaneous equations for two parameters, α and ξ.

PWM & L-MOMENTn∑i=1 Xiprobability space.

 E(X ) =1∫0

= 0.5772 α + ξn• Basic idea is integration in_ 2n∑i=1 (Xi − X )x(u) ⅆu

n−1

=

(4-6)

π 2α 26(4-7)

For the other distributions, the solutions are shown in Table 18.2.1 of Handbook ofHydrology(David Maidment, 1993).

• Order Statistics

 X = {X(1) ≥ · · · ≥ X(n)}•Unbiased PWM Estimators(4-11)

There may be cases that a sample includes outliers. The product moment method is known tobe affected by the outliers excessively since r-th order product moment includes xr. In order toimprove this situation, PWM(Probability Weighted Moment) or L-moment method weredeveloped.

_ b0 = X

n−1 (n − j)X( j)∑ n(n − 1)
j=1

 b1 =The expectation of the random variable X is defined as follows.

n−2 (n − j)(n − j − 1)X( j) b2 =∑

n(n − 1)(n − 2)

j=1

n−3 (n − j)(n − j − 1)(n − j − 2)X( j) b3 =∑

n(n − 1)(n − 2)(n − 3)

j=1

PWM and L-Moment∞

E(X ) =∫−∞x ⅆF(x) =∞∫−∞xf(x) ⅆx(4-8)

With the transformation of u = F(x), then, du/dx = f(x), we can write as follows.

general formula (4-12)

1

E(X ) =n−j
)X( j)
1 n−r (
 br = ∑ r
n j=1
n−1
( r )

∫0

x(u) ⅆu(4-9)

For higher r-th order moment,
br =

1

∫0

x(u)u r ⅆu

(4-10)

12    

and it includes just first order x. This difference is the advantage of PWM or L-Moment against
Product Moment. The r-th order PWMs are shown in Eq.(4-11).
_
b0 = X

 







b1 =

 





b2 =



n−1 (n − j)X( j)∑ n(n − 1)
j=1

n−2 (n − j)(n − j − 1)X
( j)

∑
j=1





b3 =











 L-MOMENT• L-Moment (4-13)

λ1 = b0λ2 = 2b1 − b0∑n(n − 1)(n − 2)(n − 3)

And Eq.(4-12) is the general formula of Eq.(4-11).
br =

n−j
1 n−r ( r )X( j)
n∑
n−1
j=1 (
r )

λ1 = b0λ4 = 20b3 − 30b2 + 12b1 − b0λ2 = 2b1 − b0

L-kurtosis τ4 = λ4 /λ2(4-12)The r-th order L-moments are shown in Eq.(4-13) using PWM in Eq.(4-11). The solution of
L-moment is shown in Table 18.1.2 in Handbook of Hydrology. For more detailed information of
L-moments, see Regional Frequency Analysis(Hosking and Wallis, 1997)

λ3 = 6b2 − 6b1 + b0
L-skewness τ3 = λ3 /λ2(4-11)n−3 (n − j)(n − j − 1)(n − j − 2)X
( j)
j=1



Fig. Outlier and its effectn(n − 1)(n − 2)

λ3 = 6b2 − 6b1 + b0
λ4 = 20b3 − 30b2 + 12b1 − b0
τ3 = λ3 /λ2,

τ4 = λ4 /λ2

13- 113 -

(4-13)

PARAMETER ESTIMATION BY L-MOMENTFOR AMSParameter Estimation by L-Moment for AMS
For Gumbel distribution, the parameters can be given by the combination of L-moment asshown below.
λ1 = 0.5772 α + ξ
λ2 = α ln 2

• Gumbel Distribution

 λ1 = 0.5772 α + ξ

The quantile x of Gumble distribution is obtained by following equation,

 λ2 = α ln 2

x = ξ − α ln(−lnF)• GEV Distribution

For GEV distribution, the relationship between three parameters and L-moment estimators is
expressed as below.
1.0

k ≈ 7.859c + 2.9554c 2

λ1 = ξ + α{1 − Γ(1 + k)} /k(4-16)

λ2 = α(1 − 2−k)Γ(1 + k) /k

λ2k

τ3 = 2(1 − 3−k) / (1 − 2−k) − 3(1 − 2−k)Γ(1 + k)

0.0
0.5
1.0

The relationship between τ3 and κ is not easy to solve but is insimple decreasing function as Fig. 4.1. By defining c with τ3 as
Eq.(4-17), we can get the approximation of shape parameter κ.

•Quantile Estimator•Gumbel Distributionc=x = ξ − α ln(−lnF)

2λ2
ln2
ln2
2
=
−
−
3 + τ3 ln3
3λ2 + λ3 ln3k ≈ 7.859c + 2.9554c 2•GEV Distributionαk
1 − (−lnF) }
k{

0.5
Τ3

ξ = λ1 − α{1 − Γ(1 + k)} /k

x =ξ+(4-15)

where F is non-exceedance probability.

2λ2
2
ln2
ln2
=
 c=
−
−
3 + τ3 ln3
3λ2 + λ3 ln3α=(4-14)

{

0

2

4

6

8

10

k

Fig. 4.1 relationship between
κ and τ3,
(4-17)

After obtaining κ, we can easily get scale and location parameters α and ξ by Eq.(4-16).
(k ≠ 0)14

PARAMETER ESTIMATION BY L-MOMENTFOR POT(1)• Exponential Distributionα=λ2k(1 − 2−k)Γ(1 + k)

, ξ = λ1 − α{1 − Γ(1 + k)} /k

Note that Eq.18.2.22c in Handbook of Hydrology is not correct and above Eq.(4-18) is correct.
The quantile x of GEV distribution is obtained by following equation,
x =ξ+

α
k
1 − (−lnF) }k{1
λ1 = ξ + α, λ2 = α
2• GP Distribution

where F is non-exceedance probability.

when ξ is known[Note]:approximation of gamma function(λ1 − ξ)−2
λ2

Γ(w + 1) = wΓ(w)k=(4-18)(k ≠ 0)(4-19)

α = (1 + k)(λ1 − ξ)when ξ is unknown
1 − 3τ3k=
1 + τ3α = (1 + k)(2 + k)λ2
ξ = λ1 − (2 + k)λ2

Parameter Estimation by L-Moment for POT
In the process of POT, we must realize that G(x) is different from F(x). After parameter
estimation of G(x), in order to know annual exceedance probability, it is necessary to transform G(x)to F(x) by Eq.(3-6) with arrival rate λ which is equal to number of events per observation period.
Exponential distribution and Generalized Pareto(GP) distribution are generally used for G(x), andparameter estimation by L-Moment is as follows. For Exponential distribution,
λ1 = ξ + α
λ2 =(4-20)

1α2(4-21)

and for GP distribution, we need following three equations,15- 114 -

λ1 = ξ + α / (1 + k)

PARAMETER ESTIMATION BY L-MOMENTFOR POT(2)

λ2 =
τ3 =

• Quantile Estimator• GP Distribution

α(4-23)(1 + k)(2 + k)
1−k3+k(4-24)The solution of L-Moment for κ is, when ξ is known,x = ξ + α{1 − (1 − G)k} /k•Exponential Distribution(4-22)k=(λ1 − ξ)− 2, α = (1 + k)(λ1 − ξ)
λ2(4-25)

when ξ is unknown,k=x = ξ − αln(1 − G)1 − 3τ3, α = (1 + k)(2 + k)λ2, ξ = λ1 − (2 + k)λ2
1 + τ3(4-26)

Quantile estimator for GP and Exponential distribution are as Eq.(4-17) and (4-18), respectively;•POT to AMS transformation

GP

F(x) = exp{−λ(1 − G(x))}

x = ξ + α{1 − (1 − G)k} /k•AMS to POT transformationlnF(x)
G(x) = 1 +λ(4-27)

Exponential Distributionx = ξ − αln(1 − G)

(4-28)In order to obtain return level, it is necessary to get G corresponding F using inverse form of Eq.
(3-3).
G =1+

lnF
λ

(4-29)

16

SECTION 5∏
PROBABILITY PAPER& PLOTTING POSITION

How to use a probability paper
In practice, there used to be a probability paper. Normal Probability Paper and Log-Normal
Probability Paper are popular. Concerning to annual extreme value, i.e. AMS, Gumbel probability
paper is recommendable. On Gumbel probability paper, the data of exceedance probability qi isplotted at yi in probability axis. And also Gumbel distribution function F(x) is expressed in a straightline.

• AMS :Gumbel probability

yi = −ln[−ln(1 − qi)]paper

yi = −ln[−ln(1 − qi)]•

Plotting Position(exceedance probability)(5-1)

In Japan, generally, probability paper has variable on horizontal axis and cumulative probability

Table 5.1 Plotting Positions
Name

Formulaa

T1

Motivation

Weibulli/(n+1)0

n+1

Unbiased exceedance probabilites for all distributions

Median(i-0.3175)/(n+0.365)

0.3175

1.47n+0.5

Median exceedance probabilities for all distributions

APL(i-0.35)/n

~0.35

1.54n

Used with PWMs

Blom(i-3/8)/(n+1/4)

0.375

1.60n+0.4

Unbiased normal quantiles•Weibull Plot : α=0

Cunnane(i-0.40)/(n+0.2)

0.40

1.67n+0.3

Approximately quantile-unbiased•Gringorten Plot : α=0.44

Gringorten(i-0.44)/(n+0.12)

0.44

1.79n+0.2

Optimized for Gumbel Distribution

Hazen(i-0.5)/n

0.5

2n

A traditional choice

qi =

i−a
n + 1 − 2awhere n : sample size, i :order of the order statistics, α :plotting-position parameter•Cunnane Plot : α=0.40•Hazen Plot : α=0.50

T1 is the returrn period each plotting po
osition assings too the largest ob
bservation in a sample of size n.

17- 115 -

on vertical axis(Fig. 5.1). In U.S., probability lies on horizontal axis.

Gumbel Probability Paper

When you plot a sample on a probability paper, you have to know theexceedance or non-exceedance probability for each data of the sample.
Plotting position shows them. Several plotting position formulas(Table 5.1)
have been proposed to show exceedance probability qi and they are
expressed in an equation with different α.
qi =

i−a
n + 1 − 2a

Gumbel
99.5

200

GEV

p 99

100

98

50

95

20

90

10

80

5

(5-2)

You have to decide which plotting position you use. Cunnane plotting
position is recommended for wide range of distribution. After plotting yoursample data, you can overlay fitted probability distribution function on it. If
you do so, you can easily find any return level corresponding return period
and vice versa. And you can also check the goodness of fit and reason of lessperformance(Fig. 5.1).

F

70

Table 5.2 shows plotting positions for sample size 30 with Cunnane
plot(a:0.40). Figure 5.2 shows example plots of sample size 30 using
Cunnane plot. It is seen the maximum data is plotted about return period 50
years. Generally, a sample including outliers is fitted by GEV with negative
shape parameter κ in case of the definition as Eq.(3-1).

50
30
20
10
5
1

100

200

300

400

500

Rainfall mm

Fig. 5.1 Probability Paper in Japan

18

Table 5.2 Plotting Positions 
for sample size 30(Cunnane plot)































 

 


 

 
 

 


 




 


 
 

 
 
 




 
 
 

 
 


 




 


 

 
 

 


 

 



Gumbel Probability Paper

Gumbel Probability Paper

Gumbel

99.5

100

99

98

50

98

50

95

20

95

20

90

10

90

10

80

5

80

5

GEV

99

F

Gumbel

200

99.5

F

70

70

50

50

30
20
10
5
1

30
20
10
5
1

100

200

300

Rainfall mm

400

200

GEV

100

200

300

400

Rainfall mm

Fig. 5.2 Examples of Probability Plot and Gumbel/GEV fitting
on Gumbel Probability Paper
19- 116 -

500

100

SECTION 6∏
TIPS FOR FREQ. ANAL.
• Examine extremes in both

AMS and POT• While similar results are

expected, different results
come out in general.

• Let’s have an experience• Probability paper is useful forchecking the goodness of fit of
distributions• Do not directly compare AMS

and POT, use Eq.(3-3)• The maximum of AMS andthat of POT from a time series
should show almost identicalexceedance probabilities.

• GEV is apt to have long tailthan GP in hydrologicalfrequency analysis.

Frequency Analysis Procedureof a Time Series
The followings are general procedure of frequency analysis for a time series.
1) Checking trend
2) Pick up extremes AMS and POT3) assess with AMS
1) probability plot on a Gumbel probability paper using plotting position
2) estimate parameters of distributions
3) plot the distributions onto 1)4) estimate quantile4) assess with POT1) probability plot on a Gumbel probability paper2) plot sample mean excess function and determine the suitable threshold3) re-select POT more than the threshold4) estimate parameters of distributions, if necessary, probability plot
5) plot the distributions onto 4-1)
6) estimate quantile5) compare results of AMS with that of POT6) judge
20

Exercise

SECTION 6∏
EXERCISE (AMS)• Draw Histgram• Draw Probability Plot
• Obtain Plotting Position qi• Fit Gumbel and GEV to thesample• Obtain L-moments

estimator• Obtain Gumbel & GEV

distribution parameters• Draw both lines onto theProbability Plot

• Obtain 1/30, 1/50 and 1/100

Quantiles by Gumbel and
GEV, respectively• Obtain return periods for therecord maximum, 1.1 and 2.0
times of it.

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

Group1
56.3
56.9
64.5
66.3
72.2
73.2
74.
76.2
77.8
78.9
79.9
80.5
84.5
86.4
99.8
106.8
109.
109.7
110.1
116.7
123.8
125.6
126.1
132.8
133.
149.5
150.9
159.4
162.
164.8

Group2
56.9
62.3
66.3
69.9
73.2
74.
74.1
80.5
88.8
89.2
96.
97.9
106.8
107.6
110.1
111.7
113.8
115.7
126.1
129.2
133.5
138.2
141.5
146.6
158.7
159.4
164.8
165.9
171.5
193.7

21

- 117 -

Group3
62.3
67.
69.9
70.5
74.8
77.6
88.8
92.9
93.6
94.2
97.9
101.7
107.6
113.8
115.7
116.1
122.2
126.2
138.2
139.7
141.5
147.
150.4
152.8
163.6
164.7
168.5
175.9
193.7
278.3

Group4
55.3
60.
61.9
70.1
70.6
71.5
74.8
77.2
77.6
83.9
92.2
93.6
103.5
106.2
116.1
118.
118.5
119.
126.2
139.7
141.
147.
151.3
152.8
154.5
157.5
161.2
164.7
278.3
371.9

Group5
43.
54.
55.3
56.
61.9
65.5
70.1
73.
73.
74.5
75.5
77.2
83.9
87.5
93.
96.
96.5
97.5
103.
104.5
118.
119.
122.5
154.5
161.2
167.5
169.5
215.
225.5
371.9

Group6
54.
56.
65.5
66.
70.
73.
74.5
93.5
94.
96.
97.5
98.5
104.5
107.5
115.
119.
122.5
123.5
130.5
141.
151.
167.5
185.
186.
195.
215.
220.5
222.5
234.5
259.5

L-moments estimators
Λ1
Λ2
Λ3
Λ4
Λ2 Λ1Λ3 Λ2Λ4 Λ2

Group1103.587
19.1547
2.31475
0.153344
0.184915
0.120845
0.00800556

Group2114.1321.66171.66655
0.723629
0.189799
0.0769353
0.0334059

Group3123.5725.3174
4.39023.436
0.204883
0.173407
0.135717

Group4123.8732.04899.741237.599880.258730.303949
0.237134

Group5112.183
32.8746
12.9197
8.651030.293044
0.393001
0.263153

Group6131.283
33.573
6.142451.683830.255729
0.182958
0.0501542

Gumbel distribution

Group127.634487.6361Αparameters:
Ξ

130
150
quantiles:
1100Group231.251396.0918

Group1181
195
215

Group336.5252
102.488

Group2202
218
240Group446.2367
97.1822

Group3226
245
271

Group547.42884.8079

Group648.4356
103.326

Group4254
278
310

Group5245
270
303Group6267
292
326

Group437.0641
0.199204
93.4794

Group531.7826
0.320914
79.2591

GEV distribution
ΑΚparameters:ΞGroup129.5590.078271
88.6616130
150
quantiles:
1100Group235.28810.150927
98.4029

Group1
177
188
203

Group336.3413
0.00543864
102.397

Group2192
202
215

Group3227
246
272

Group4273
312
373

Group647.5198
0.0202833
102.883

Group5274
327
414

Group6
269
296
332

22

REFERENCES∏
References    

Hosking, J.R.M. and J.R. Wallis：Regional Frequency Analysis ‒An
Approach Based on L-Moments‒, Cambridge Univ. Press., p.207,
1997.
Stedinger, J.R., R.M. Vogel, and E. Foufoula-Georgiou: Frequency
Analysis of Extreme Events, Chap. 18, Handbook of Hydrology, (Ed.)
D. R. Maidment, McGraw-Hill, New York, pp.18.1-18.66, 1993．



 









































 

23- 118 -







Lecture 6: Efforts to develop disaster statistics in the world
Yuichi ONO, (Assistant Director and Professor, International Research Institute of Disaster Science(IRIDeS) at Tohoku University)
Abstract:
The lecture consists of three components. First, we will take an overview about the current
situation of disaster loss and damage database (global/national) in the world and consider what arelacking in the existing database.
Secondly, we will take a look in to the international process related to disaster risk reduction
focused on disaster statistics. Especially focusing on the targets established under two internationalframework which is an important driver to develop disaster statistics in the world, Sustainable
Development Goals (SDGs) and Sendai Framework on Disaster Risk Reduction (SFDRR). Targets
to reduce the number on death, people affected and direct economic loss caused by disaster was
included in these frameworks and development of disaster loss database have become a “Must thing”
for countries to monitor and report the situation. The lecture will also include on-going discussion of
how to monitor these targets such as indicators and terminologies.
Thirdly, as one of the specific activities to support development of disaster statistics in theworld “Global Centre for Disaster Statistics” will be introduced. The centre was established in April
2015 based on the cooperation between UNDP and International Research Institute of Disaster
Science (IRIDeS), Tohoku University. The centre plan to (1) develop a comprehensive global
database on disaster loss and damage; (2) analyze the disaster loss data; (3) make incites to theinternational process on disaster risk reduction and development agendas. In the lecture, distinctive
points of the Centre’s activities and its strategy will be introduced.
Overall, the lecture provide a good chance to (1) know the situation and future strategy todevelop disaster statistics in the world, in other words to support development of national database incountries and to gather those data into one global database; (2) know the international process of
disaster risk reduction including the contents of targets in SDGs and SFDRR and present discussion
on monitoring (indicator and terminology) method of the targets.

- 121 -

Lecture6：
Eff t toEfforts
t developdldi t statistics
disastert ti ti iin th
the world
ld

Data – Analysis ‐ Insight

Yuichi Ono, Ph.D.
Assistant Director and Professor
Professor, International Research Institute of Disaster
Science (IRIDeS) at Tohoku University
Visiting Professor, Disaster Prevention Research Institute (DPRI) at Kyoto University

Current Situations Overview

Contents of the lecture

Existingg international databases on disaster losses

1. Current situations overview• Existing international database on disaster loss data
• Situations
Sit ti
iin regionsi• What are the lacking points of the existing database?
• DesInventar platform

2. Drivers for enhancing database development• World Conference on Disaster Risk Reduction• Sendai Framework on Disaster Risk Reduction• Sustainable Development Goals• Monitoring Process of the Targets and Indicators

EM‐DAT database NatCatSERVICEDesInventar format

Ownership

CRED:

Munich Reinsurance

Mostly Governments

SScope off thethdatabase

126 countries

N t Identified
Not
Id tifi dOver 82 countries

21,468 events
1990‐Present

21,700 events
1980‐Present

Over 490,000 events

Disasters whichmeet a certain
criteria
Multiple Source

Disasters with Humanor Economic loss

All Disasters

Insurance Company
based (Munich Reinsurance

Authorized by theGovernment

Disaster
Collected

3 Global Centre for Disaster Statistics
3.
• Outlines• Activities• Five Distinctive Points

Data Source

4. Conclusions

Situations in Regions

Current Situations Overview

Centre for Research on the
Epidemiology if Disaster(UN agencies, Countries, Red
Cross, Red Crescent, World
Bank, Reinsurance, Media)※mainly developed countries

Depends on the country(Oldest record )offices and clients, International
Insurance association)

Situations in Regions

Some by NGO, ResearchInstitute etc.

Current Situations Overview• Asia and the Pacific• Latin America‐ ESCAP (UN Economic and Social Commission for Asia and the Pacific),
Committee on Disaster Risk Reduction playing an importantroll in promoting development of disaster loss database in the region‐ UNDP assisted countries and 18 databases based on the DesInventar
Figure: Current situations of the development
formatof national disaster loss databases in Asia

27 databases depending on the DesInventar Format.
D I
DesInventarb d on thebased
h similar
i il methodology
h d lddevelopedld bybgroups of researchers and institutional actors in Latin America
called LA RED.

• Africa• Europe and the European Commission

18 database based on the DesInventar
format

Joint Research Centre of the European Commission is preparing
guidelines for standardizing European Union Loss database(Groeve et alal. 2013)
2013).
4 databases based on the DesInventar Format.

Some other databases exist‐ Global Information and Early Warning System(not focused on disaster loss)‐ Natural Disaster Database for Central Africa(data gathered from existing databases)- 123 -Country a disaster statistics system construction at thenational level have been completed by UNDP support
Country a disaster statistics system construction at thenational level is on the way by UNDP support

Current Situation Overview

What is missingg in the existingg databases?

What is missingg in the existingg database?

• Large‐scale disaster are well reported while smaller scaleones are not
NatCatSERVICE

Current Situation Overview• Not many countries collect and own disaster loss data
authorized byy their governmentsg• Data collection methodology
installed in 82 countries

Large‐scalegdisaster

But the problem is⁃ Some database stopped updating⁃ Not collected from all local regionsof the country⁃ Methodology and Standard is somehow different between countries

Insurance: Developed
p countries
EM‐DatMiddle‐scale disaster
100 affected or 10 deaths

Missing Data!!

※Especially with countries not in the

S ll
Small‐scalel didisastert

DesInventar format
http://www.desinventar.net/index_www.html

Current Situation Overview

DesInventar p
platform

Drivers for enhancing database development

World Conference on Disaster Risk Reduction• Based on inputs from the local officials• Web
Web‐basedbased platform to provide outputs on simple visual images,
imagesincluding graphs and maps
• Some country has adapted the DesInventar and has its own one• Disaster risk is a potential risk
• WCDRR is a UN conference to develop an international strategy to
reduce disaster risks (c.f. disaster management)※Disaster: Disaster caused by natural hazards• Major update in the 3rd Conference was toset “7 Global Targets”(example: Indonesia)stC f
att YYokohama
k h1994 1 Conference
Yokohama Strategy“5 Plan for action”nd2005 2 Conference at Kobe
Hyogo Framework for Action“3 Strategic goals”
5 Priorities for Action”
Action“5

Sample of Database in Sri Lanka (DesInventar platform)rd2015 3 Conference Sendai
Sendai Framework for
Disaster Risk Reduction“7 Global Targets”“4 Priorities for Action”

Sample of Database in Indonesia (modified DesInventar platform)

Drivers for enhancing database development

Sendai Framework on Disaster Risk Reduction

Drivers for enhancing database development

Sendai Framework on Disaster Risk Reduction• Adopted 7 “Global targets”• 4 “Priorities for action” adopted in the conference
Priority 1
1: Understanding disaster risk
risk.
Priority 2: Strengthening disaster risk governance to manage disaster risk.
Priorityy 3: Investingg in disaster risk reduction for resilience.
Priority 4: Enhancing disaster preparedness for effective response and to“Build Back Better” in recovery, rehabilitation and reconstruction.

((a)) Substantiallyy reduce gglobal disaster mortalityy byy 2030,, aimingg to lower the averageg per
p 100,000,
global mortality rate in the decade 2020–2030 compared to the period 2005– 2015;(b) Substantially reduce the number of affected people globally by 2030, aiming to lower the average
global figure per 100,000 in the decade 2020–2030
2020 2030 compared to the period 2005
2005–2015
2015(c) Reduce direct disaster economic loss in relation to global gross domestic product (GDP) by 2030
(d) Substantially reduce disaster damage to critical infrastructure and disruption of basic services,among them health and educational facilities
facilities, including through developing their resilience by 2030(e) Substantially increase the number of countries with national and local disaster risk reduction
strategies by 2020;(f) Substantially enhance international cooperation to developing countries through adequate andsustainable support to complement their national actions for implementation of the present
Framework by 2030
(g) Substantially increase the availability of and access to multi‐hazard
multi hazard early warning systems anddisaster risk information and assessments to people by 2030

- 124 -

Drivers for enhancing database development

Sendai Framework on Disaster Risk Reduction

Drivers for enhancing database development

Sustainable Development
pGoals• 7 “Global targets” adopted in the conference• A new set of goals and targets developed after the Millennium
Development Goals adopted at the UN Sustainable Development
Summit in September 2015
• 17 Goals and 169 Targets were adopted• Disaster Risk issues was also included as one of the targets• Target 11.5 and 11.b is closely liked
2000 Millennium Summit
withi h SFDRR
S
Millennium Development Goals((a)) Substantiallyy reduce gglobal disaster mortalityy byy 2030,, aimingg to lower the averageg per
p 100,000,
global mortality rate in the decade 2020–2030 compared to the period 2005– 2015;(b) Substantially reduce the number of affected people globally by 2030, aiming to lower the average
global figure per 100,000 in the decade 2020–2030
2020 2030 compared to the period 2005
2005–2015
2015(c) Reduce direct disaster economic loss in relation to global gross domestic product (GDP) by 2030

• With these 3 outcome targets,targets development of disaster loss database
have become a “Must thing” for countries.

“8 Goals”“16 Targets”

2015 Sustainable Development Summit
Sustanable Developmetn Goals“17 Goals”““169
Targets””

Drivers for enhancing database development

Drivers for enhancing database development

Sustainable Developmentp
Goals

Monitoringg Process of the Targetsg• Goal 11. Make cities and human settlements inclusive, safe, resilientand sustainable• Indicators and Methodology⁃ In monitoring the targets
targets, indicators (Definition of words included in thetarget, Method of calculation, and Level of disaggregation etc.) need to be
developed⁃ Open‐ended
Open ended intergovernmental expert working group on indicator andterminology relating to disaster risk reduction had its first meeting in Geneva
in June 2015
⁃ FirstFi open‐ended
d d intergovernmentalil meetingi in
i Geneva,
G
SeptemberSb 2015⁃ Second one to be held in Geneva around February 2016p
to be completedp
byy the end of 2016 in the form of the UN⁃ Expected
Resolution⁃ 11.5 By 2030, significantly reduce the number of deaths and the number ofpeople affected and substantially decrease the direct economic losses relative
to global gross domestic product caused by disasters
disasters, including water
water‐related
relateddisasters, with a focus on protecting the poor and people in vulnerable
situations⁃ 11.b
11 b By
B 2020,2020 substantiallyb t ti ll iincrease th
the numberb off cities
iti anddh
humansettlements adopting and implementing integrated policies and plans towards
inclusion, resource efficiency, mitigation and adaptation to climate change,
resilience
ili
to disasters,diandd develop
d l andd iimplement,li liline withini h theh SendaiS d i
Framework for Disaster Risk Reduction 2015‐2030, holistic disaster risk
management at all levels

Key negotiation points: Definitions
Definitions, international cooperation

Global Centre for Disaster Statistics

Outlines• Relationship among the centre and associated organizations/agencies• Situations of the global framework and the basic idea
⁃ Development of disaster loss database has become a “Must thing” for countries⁃ How can countries have good disaster management strategy without knowing what ishappening in their country?

UNDP

United Nations
Development Plan

• UNDP• Assistance for developing
p gdisaster loss databases• Policy Advice• Institutional capacitypdevelopment⁃ Has been supporting development of disaster loss database in more then 30 countries⁃ Country‐based support is available with strong relationship between government andrelated agencies in developing countries

Support• IRIDeS⁃ Experts in more than 37 academic disciplines andcapable to carry out analysis based on disaster data
⁃ Strong relationship between relatedorganization/agencies/research institute in Japan

Global Centre for Disaster Statistics

Outlines

Global Centre for Disaster Statistics

UNDP

Countries• Establishment of
National Disaster
Loss Database• Data collection

IRIDeS- 125 -

Cooperation

Tohoku University
International ResearchInstitute of Disaster
Science

Global Centre forDisaster Statistics• National data assembly• Archives mechanism• Analysis (adding values)• Policy recommendations• Provide data to the public
in a user‐friendly platform

Global Database

United Nation
UNISDR

United Nations Secretariat forInternational Strategy for
Disaster Reduction

ESCAP

Economic and Social Commission
for Asia and the Pacific

JICA

Japan International
Cooperation Agency

ICHARM

International Centre for Water
Hazard and Risk Management

ADRC

Asia Disaster
Reduction Center

IRP

International Recovery
PlatformGlobal Centre for Disaster Statistics

Global Centre for Disaster Statistics

Five distinctive points of the Centre

Five distinctive points of the Centre

Point1:
Scope and coverage

Point4:
Analysisy based on collected disaster loss data

• Including extensive disaster data

• Macro‐economic analysis• Analysis based on disaggregated data
• Provide inputs
p to advocacyy material on disaster risk reduction

Point2:
Data Source from National/Local Governments
in collaboration with UNDP

Point5:
Supporting National/Local Governments• Disaster data from official sources• Provide incentives to data entry efforts• Empowering technical and institutional capacity• Supporting disaster data reporting for policy making

Point3:P
i 3
Supporting the Monitoring process of the SFDRR• A new global database can serve as a platform for UN to monitor SFDRR and
SDGs• The data will be archived in a standardized format and application

Global Centre for Disaster Statistics

Global Centre for Disaster Statistics
Point2:
Data Source from National/Local Governments
in collaboration with UNDP

Five distinctive points of the centre
Point1:
Scope andd coverage(utilizing DesInventar methodology)*Provided by National Disaster Management Offices
Reporting from local officials*Reporting*UNDP’s country‐based support• Including extensive disasters
data*Capture small‐scale disaster data(the missing data)*Data disaggregated bydemographic,g psocial,and economic characteristics• Meta data (nature and source ofdata is known)

Global Centre forDisaster Statistics• DisasterDi t datad t ffrom official
ffi i l sources

NatCatSERVICE

Large‐scale disaster

Country C• Provide
P id iincentives
titot datad t entryt efforts
ff t*See impacts of entering data by themselves can be agood incentive

Insurance: Developed countries
EM‐DatGlobal Database

Middle‐scale disaster

Country B

100 affected or 10 deaths
Missing Data!!

Small‐scale
Small
scale disaster

Country A

Global Centre for Disaster Statistics

National Database
Inputs from local officials

Global Centre for Disaster Statistics

Point3:
Supporting Monitoring of progress in the SFDRR

Point4:
Analysis based on collected disaster loss data

• A new database developed
p under the centre can serve as aplatform for international data sharing• The data will be archived in a standardized format and application• Macro‐economic
Macro economic analysis*One example of the analysis to evaluate theeffect of pre‐disaster investment*Contribute in monitoring the target bellow*Support countries based on Indicators and standards to be agreed(ongoing process led by UNISDR)• Analysis based on disaggregated data

*Data disaggregated by social, demographic,
and economic characteristics(a) Substantially reduce global disaster mortality by 2030, aiming to lower the average per 100,000 global
mortality rate in the decade 2020–2030 compared to the period 2005– 2015;(b) Substantially reduce the number of affected people globally by 2030, aiming to lower the average
global figure per 100,000 in the decade 2020–2030 compared to the period 2005–2015(c) Reduce direct disaster economic loss in relation to global gross domestic product (GDP) by 2030((d)) Substantiallyy reduce disaster damage
g to critical infrastructure and disruption
pof basic services,, amongg
them health and educational facilities, including through developing their resilience by 2030
※Partly possible to monitor- 126 -

DR2AD model, developed by JICA, to quantitatively estimate theeffect of pre‐disaster investment to economic development

Global Centre for Disaster Statistics

Global Centre for Disaster Statistics

Point4:
Analysis based on collected disaster loss data

Point5:
Supporting National/Local Governments• Provide inputs to advocacy material on disaster risk reduction• Empowering technical and institutional capacity*Supporting UNISDR by providing materials to the
Global Assessment Report (GAR)*Trainings for disaster damage data collection*Technical advisory on disaster statistics analysis• Supporting disaster data reporting for policy making*Develop
Develop a system to output a Annual Report*Semi‐automatically, officers could complete local/national White Book on
Disaster Risk Reduction*whichwhich motivates local officials to collect disaster damage data
*Such Reports would generate effective policy on DRR

Global Centre for Disaster Statistics
Point5:
Supporting National/Local Governments[Image of the Annual Report]
Disaster in 2015

⁃ Map, Chart, Graph etc.

Disaster Worst 5
1.Earthquake xx,Feb Death: x,x00
2.Drought
xx,Aug Death: xx0
3 Fl d3.Floodxx,OctO t D
Death:
th x
Economic loss: xxmillionUS$
4.・・・・
Photo
Photo
5.・・・・

Poverty and Disaster

Progress on SDGs & SFDRR
SDGs
Goal X
Target A

OO% achieved
Goall Y
Target B

Comments
According to the
database ・・・・・・・・・・
database,
・・・・・・・ is needed to by
developed.

SFDRR
Goal 1

X,XX00 person
Bellow target
Goal 2

Target C

XX,X00 person
Achieved

Bellow Target

Goal 3

Achieved

Contents of the Report•Trend of the Disasters

Annual Report

X,XXX,X00 US$
Bellow Target•Details of disasters⁃ Information of disasters⁃ Photo Reports•Analysis Results⁃ Correlational Analysis of Disaster Statistics and Other
Statistics such as Census
Ex. Relation between poverty and number ofdeaths caused byy disaster•Achieve situation report of disaster‐related
global targets⁃ Sustainable Development Goals (SDGｓ)⁃ Sendai Framework on Disaster Risk Reduction (SFDRR )Data – Analysis ‐ Insight- 127 -

Conclusions• The existing database is not enough for capturing the wholesituations of the disaster risk in countries• SFDRR and SDGs are important drivers for advancing thedevelopment of disaster statistics• The monitoring process has been discussed at the UN• IRIDeS and UNDP jointly developed the new centre (Global Centre forDisaster Statistics))• The concept of the new centre is to support countries to monitor the
progress in SFDRR and to enhance the capacity of governance indisaster risk reduction for countries• These supports are essential for promoting the efforts to develop
disaster statistics in the world

Lecture 7:

Fundamentals in river basin modelling

Yoshinobu SATO (Faculty of Agriculture, Ehime University)
The aim of this lecture is to provide fundamental knowledge about river basinmodelling for distributed hydrological simulations. River basin is the entire
geographical land area drained by a river and its tributaries or an area characterized by
all runoff being conveyed to the same outlet. Generally, river basin have typical futures,
these include:
Tributaries: smaller rivers flowing into a larger river.
A watershed: an area or ridge of land that separates waters flowing to different rivers.
A confluence: where a river joins another river.
Source: the start of a river.
Mouth: where a river meets a lake, the sea or an ocean.
In many regions of the world, digital elevation/ terrain maps (DEM/DTM) arebecoming available at a resolution fine enough to represent the form of hillslopes. In
this lecture, a DEM with a fixed grid size (raster data) is used for the analysis.
However, in fact, most raster DEMs have been built by interpolating from digitizedcounters and as a result may be, in places, cause to significant error, particularly where,
in flat topography there are few counters, or where there are short steep slopes.
Therefore, in this lecture, the procedure of how to delete depressed area in a flat zone
and how to create flow direction map in the specific river basin will be introduced.
Physically, waters does have tendency to flow downhill, at least for shallow river
systems, so that obtaining information about the form of topography should have some
utility in hydrological modelling. Distributed hydrological model often uses this typeof topological data directory. But, a coarse-resolution DEM will not able to provide anadequate description of hillslope flow pathways. For the better hydrological simulation,
all the topological information in a fine resolution are required. Variables derived from
topographic data, calibrated parameter values, and the result of model simulations by a
distributed models will based on the grid resolution of the DEMs. In the case of Japan,
50m DEM created by the GSI (Geophysical Survey Institute of Japan) are available
and in the case of global scale, the SRTM30 which have horizontal spacing of 30
arc-second (Approx. 1km) generated by NASA, Shuttle Radar Topography Mission(http://www2.jpl.nasa.gov/srtm) are available. River basin boundary and channel
network information will be obtained from GDBD (Global Drainage Basin Database),consist of six GIS (Geographical Information System) data: 1. Drainage basin- 129 -

boundary data, 2. River network data, 3. Discharge gauging station data, 4. Naturallake data, 5. Dam lake data, and 6. Flow direction data, developed by CCGR-NIES(Center for Global Environment Research, National Institute for Environmental
Studies: http://www.cger.nies.go.jp/db/gdbd/gdbd_index_e.html). Each data can beextracted by ArcGIS software. For the interpolation, IDW (Inverse Distance
Weighting) method is used. For each grid cell, there are four/eight possible flow
directions assuming that the river channels run through the lowest part of each gridmesh and flow to the adjacent mesh with the steepest slope. If a depression sink occurs
before the river mouth is reached, the elevation of mesh is modified to allow flow
down to the nearest lowest mesh among four/eight directions. The detailed procedure
for the river basin modelling such as the definition of grid cell coding (coordination
system), calculation order, elevation data preparation, hillslope and river channel slope
preparation, flow direction map, channel network, river channel order etc. will be
explained in this lecture.

- 130 -

Class A river basin in Japanp
Lecture 7
Fundamentals in river basin modelling

Y hi b SATO
Yoshinobu

Hydrometeorology for Environmental Engineering
Dept of Rural Engineering,
Dept.
Engineering Faculty of Agriculture
Ehime University@ gjp
sato@agr.ehime-u.ac.jp

River basin modellinggRiver basin modellingg toolInput Data

SourceTributary
ChannelW t h dWatershed

BASIN.csv

Confluence

Program

Output Data

Mesh list
Mesh_list

CHANNEL.csv

GEO.f90
Reference point

DEM.txt

Main
ChannelLanduse.txt

Flow of the modellingg
Geographical Survey Institute (GSI)
data set
Basin boundary
Channel location
Land-use type
50m DEM

GEO

Mouth

Flow of the modellingg
1. Total area selection↓

Basin Parameter initial setting

2. Basin area determination↓

target area setting
GSI'ss river code
GSI
GSI's prefecture codebasin name

3. Flow direction determination↓
4. Flow direction modification↓

1. Extract Land-use type data
2 D
2.
Detectt t basinb i bboundaryd

5. Flow direction map↓

3. Check river channel location
4. Extract DEM information

6. a) Land use classificationb) Slope angle determination

5. Flow direction (river channels)
6. Flow direction (hillslopes)
7. Data Output- 131 -

Flow routinggElevation data

Cell concentrated type2 Hill slope & 1 Channel
Average
g
20
data

400
data

Hill slope flow direction

20
data

Channel flow direction

Elevation data (SRTM30)
(
)
W140

N90

W60

GSIʼs mesh code system
y

E100

E20

GSI (Geophysical Survey Institute, Japan)

E130

W180
N90

W140
N90

W100
N90

W060
N90

W020
N90

E020
N90

E060
N90

E100
N90

E140
N90

W180
N40

W140
N40

W100
N40

W060
N40

W020
N40

E020
N40

E060
N40

E100
N40

E140
N40

W180
S10

W140
S10

W100
S10

W060
S10

W020
S10

E020
S10

E060
S10

E100
S10

E140
S10

LowestE140

Ishikari River basin
81‐007‐xxx

N40

N46

N42

W100

W20

E60

N40
6000 Rows

E60

N36

Kiso River basin
85‐054‐xxx

E140

Yoshino River basin
88‐084‐xxx

N30 Y350deg =30arc sec x 6000 mesh40deg =30arc sec x4800 mesh

E140

E130

N28

N26

Region code 81(Hokkaido),82(Tohoku),83(Kanto),84(Hokuriku),85(Chubu)
86(Kinki),87(Chugoku),88(Shikoku),89(Kyushu),90(Okinawa)

Basin boundary
y

7
6
5
4
3
2
1
0

2nd mesh code
Y2X2=42
Y2X2
42
5min
0

1

2

3

4

5

6

7

X2

N34

N32

River code system=xx‐xxx‐xxx
region‐basin‐river

4800 Columns

S10
E20

Y2

Nagara River basin
85‐055‐xxx

Row 1:Data 1,2,…,4800
Row 2:Data 4801,4802,…
.
Row 6000:Data …28,800,000（4800x6000)

1degree

N40

N38

W180

40min

N44, E145

Mogami River basin
82‐024‐xxx

S10

1st mesh code
Y1X1=6645

Y1 = (lat x1.5)x1 5)=44x1.5=66
X1 = (lon‐100)=145‐100=45

N44

9
8
7
6
5
4
3
2
1
0

30sec
0 1 2 3 4 5 6 7 8 9Mesh code=6645‐42‐23

Y=240 cells
L k Bi
Lake
Biwa

Kyoto
Uji
Osaka

0: Outside of the Basin
1: Inside of the Basin

Katsura River

Yodo River basin

Uji RiverBasin: 8121 cells

Kizu River

Total: 160 x 240= 38400 cells

0: not channel
1: Channel

X=160 cells- 132 -

45sec

X3

River channels

Lake Biwa

7min30sec

3rd mesh code
Y3X3=23

Basin modellingg toolCoordination systemy(x y)=(1 240)(x,y)=(1,240)(x y)=(160 240)(x,y)=(160,240)

240 cellsllOriginStarting point

30sec

5135
51/1.5=34
35+100=135

45sec

N:34.0E:135.0(x y) (160 1)(x,y)=(160,1)(x,y)=(1,1)

Slope
p data160 cells

Resolution
10km

Elevation
Distance
Slope gradient= Elev.(m)/Dist.(m)Ele (m)/Di t (m)20Slope1 

i 1

1km
k

20

20


Slope 2 

H i  Lidij 1

H j  Lj
dj
20

Resolution

Land use classification
Mogami River basin
39.2

Ishikari River basin

44.5

39

Green: forest
Orange:g ppaddyyYellow: grass
Purple: urban
Blue: water

38.8

44
38.6

1km

20km

38.4

43.5

38.2

38

37.8

43

37 6
37.6

37.4

141.5
139.2

139.4

139.6

139.8

140

140.2

140.4

140.6

142

142.5

143

Nagara River basin

Yoshino River basin
34.6

34.4

34.2

143.5

140.8

36.6

36.6

36 4
36.4

36 4
36.4

36.2

36.2

36

36

35.8

35.8

35.6

35.6

35.4

35.4

35.2

35.2

Kiso River basin

34

33.8

33.6
35

133.2

- 133 -

35

34.8

33.4
133.4

133.6

133.8

134

134.2

134.4

134.6

134.8

34.8

136.2

136.4

136.6

136.8

137

137.2

137.4

137.6

137.8

136.2

136.4

136.6

136.8

137

137.2

137.4

137.6

137.8

Data format
DEM.txt

River mouth

Landuse.txt-999.90/0.0000:
Outsidex
average(m)y

Slope1(m/m)
Lowest(m)

Forest:1
Paddy:2
Grass:3
Urban:4
Water:5

In the case of Yodo River basin,
the river mouth position is((x,y)y) = ((34,82))
x=34

xSlope2(m/m)

Land use
Type

y

y=82

Outputp dataOutputp datamesh_list.txt
GEO.txt

Mesh ID.
Calculation Order
Mesh CodeXXX YYY

Mesh code

Upstream mesh
ID1, ID2, ID3

Downstream
Mesh ID.

Upstream
Channel Order Catchment Area (km2)

Downstream
Mesh code

Elevation(m)
Latitude(deg.)

Elevation(m)Longitude(deg.)

39 2
39.2

43.5

38.6

38.4

43

38.2

38

141.5

37.8

34.6

142

142.5

Nagara River basin

139.4

139.6

139.8

140

140.2

140.4

140.6

Yoshino River basin

140.8

143

36.6

36.6

36.4

36.4

36.2

36.2

36

36

35.8

35.8

35.6

35.6

35.4

35.4

35.2

35.2

143.5

Kiso River basin

35

35

34.8

34.8

• Depression is corrected automatically bychanging mesh elevation

34

33.8

136.2

136.4

136.6

136.8

137

137.2

137.4

137.6

137.8

136.2

136.4

136.6

136.8

137

137.2

137.4

137.6

137.8

33.6

33.4
133.2

133.4

133.6

133.8

134

134.2

134.4

134.6

Hill Slope
Gradient (m/m)• Follow the actual river channel position

34.4

34.2

River Channel
Gradient (m/m)• River channel runs through the mesh lowest
elevation• direction type: 4/8 direction• flow direction: Steepest
p
slope
p

44
39

38.8

139.2

Forest, Paddy, Grass, Urban, Water

Summary(River basin modelling)

44.5

Mogami River basin

37.4

Land use (ratio)

22

Channel network

37.6

Area
((km2))

Mesh ID.

134.8- 134 -

Lecture 8: Fundamentals in Optimum Operation of Reservoir Systems
Tomoharu HORI
(Water Resources Research Center, Disaster Prevention Research Institute, Kyoto University)
A reservoir system is one of the most powerful and commonly used tools for water resourcesmanagement. It regulates the river discharge in order to increase the availability of water resources and also
to prevent flood disasters. Because the temporal distribution of river discharge, especially the extreme value,brings water-related disasters, the operation of reservoirs have been of great concern in the field ofoperational hydrology. It has been pointed out recently that the distribution of precipitation will change
according to the impact of climate change. This implies that the design flood with the return period of one
hundred years, for example, will come to be the one with shorter return period in future. It is not easy,
however, to construct new facilities to cope with the situation and then the non-facility-based
countermeasures such as higher degree application of dam reservoirs are getting more important.
From these points of view, a lot of research works have been done so far about reservoir operation.
Many techniques and algorithms have been proposed and huge amount of case studies have been reported
in research journals. When trying to study about reservoir operation, beginners may find some difficulty toknow where to start. This course will introduce the fundamentals in optimum reservoir operation theory,
which may be of great help for class participants to do more detailed study. The lecture comprises of threeparts; the introduction of reservoir operation, optimization framework of reservoir operation and measures
to cope with uncertainty.
In the first part, basic concept related to reservoir operation is introduced. Various purposes of dam
reservoirs are summarized and how the operation policy can differ according to the purposes. The
difference of on-line real time control and off-line control is also discussed. Some examples of actual
reservoir operation will be shown before going into theoretical approach.
In the second part, the typical reservoir control problem is formulated in mathematical expression.
Types of objective function and constraints peculiar to reservoir control problems are shown. Then it isdiscussed how the sequence of release discharge which gives the best value of objective function can beobtained. Dynamic programming (DP) for deterministic treatment of inflow discharge is introduced as themost fundamental optimum operation scheme. Recurrence function formula of DP application is derived
for the optimum release sequence. The computational burden to obtain optimum solution is also discussed
to understand the effeteness and limitations DP approach.
In the third part, coping with uncertainty in reservoir operation is the main concern. In order toconsider the uncertainty of inflow to the reservoir, first-order Markov chain is introduced and formulationof operation is modified. Then the algorithm to derive the optimum release policy is discussed and thesolution search process, which is called Stochastic Dynamic Programming (SDP), is introduced. It is also
shown that introduction of stochastic information requires a lot of memory area in the solution search
process (curse of dimensionality). Some techniques to avoid this problem are briefly explained at the end of
the class.

- 137 -

Modeling of the Interaction
between
Water Resources and Socio-economic systems

Fundamentals in Optimum
Reservoir Operation

Tomoharu HORI, Professor
Water Resources Research Center,
Disaster Prevention Research Institute,
Institute
Kyoto University

Recent Research Interest

T. Hori
Water Resources Research Center
Disaster Prevention Research InstituteKyoto University

 Modeling of the Interaction Between Water Resources andSocio-economic Systems
 Optimal Reservoir Operation scheme using Global Information
 Modeling of Human Response to Flood Emergency and Quantitative
Performance Evaluation of Non-facility-based Countermeasures Optimal Design Framework of a Flood Control System Including Infloodplain Countermeasures Based on Distributed Risk Assessment

contents

What is a reservoir for ?

1 Functions
1.
F ti
off R
Reservoirs
idischarge

Time‐discharge curve = hydrographm3 / s

2 Multi
2.
Multi‐purpose
purpose reservoir ‐ an example –Flood control

3. Mathematical expression of reservoir operation
4. Optimal operation by deterministic dynamic
programmingi

Water supply

New Resource level
Natural Resource level

5 Optimal operation by stochastic dynamic programming
5.

Time (year)
N l developed
Newlyd l d watert© T. Hori, Kyoto University

A sample of multi‐purpose dam reservoir‐ Hiyoshi dam – (1)What is a reservoir for ?
1. to protect flood disastersK
Keepreservoir
i empty during
d i non‐flood
fl d periodi d
2 to supply water
2.
water, to generate power
Keep reservoir full during non‐drought period(J(Japanwater
t agency))© T. Hori, Kyoto University© T. Hori, Kyoto University- 139 -

A sample of multi‐purpose dam reservoir‐ Hiyoshi dam – (2)A sample of multi‐purpose dam reservoir‐ Hiyoshi dam – (3)(Japan water agency)(J(Japanwater
t agency))© T. Hori, Kyoto University© T. Hori, Kyoto University

A sample of multi‐purpose dam reservoir‐ Hiyoshi dam – (3)Capacity allocation of multi‐purpose
multi purpose dam reservoir(Japan water agency)(Japan water agency)© T. Hori, Kyoto University© T. Hori, Kyoto University(Japan water agency)(Japan water agency)© T. Hori, Kyoto University© T. Hori, Kyoto University- 140 -

Objectivee Function
Objectu ct o (1)( )Key variables to describe state of a reservoir

Evaluation function
Damage or Loss in case of flood control
Minimization problem

Q(t ) : Inflow discharge at time t
S (t ) : Storage volume at time t

Benefit or Income in case of water supply and power
generation
Maximization problem

R (t ) : Release discharge at time t

Conceptually can be expressed as an functionof release discharge and storage

Continuity
y equationqof a reservoirdS (t )
 Q(t )  R (t )dt
© T. Hori, Kyoto University© T. Hori, Kyoto University

Objectivee Function
Objectu ct o (2)( )Formulationo u at o

Evaluation of the performance of operation

 T J ( R (t ),) S (t )) dt 
 0


max

Analyticaly expressed when the assessment can bedone directly in terms of reservoir variables:

R (t )
0t TJ ( R(t ), S (t ))  {R(t )  Rtarget }2  {S (t )  S target }2

subject todS (t )
 Q(t )  R (t )dt
0  R(t )  Rmax

In many cases, some simulation process such as floodrouting and runoff is included :2
 Q (t )  D(t )J ( R(t ), S (t ))   ref100 D(t )Smin  S (t )  S max(0  t  T )where D(t ) denotes the demand at intake (reference) point
J ( R(t ), S (t ))  f (mximum inundation depth)
R (t )  flood flow  inundation© T. Hori, Kyoto University© T. Hori, Kyoto University

Designes g ope
operation
at o aandd Realea ttimee ope
operation
at oDiscretization
sc et at o in timet e aandd volumeou eFrom actual viewpoint, release cannot be changed
continuously in time
Discrete time system is introduced

Inflow discharge sequence Q (t ) : 0  t  T
Known for all time horizon
Design operation (off‐line operation) : to derive
optimum release sequence for historical hydrographs

Qi

i 1

Unknown in future from the current timei

S i 1

Si

Reall timeioperationi(on‐line( li operation)i ) : to derived i
optimum release at current time in consideration with
future income (current release which maximizes total
benefit for the period between current time and time
horizon)i 1Ri
Hydrologic variables such as storage,
storageinflow, release are also discretized inlevel expression© T. Hori, Kyoto University

level 2level 1

© T. Hori, Kyoto University- 141 -

Deterministic DP operation

Thee mostost fundamentalu da e ta case

Problem Formulation

The case where values of inflow discharge sequence
Qi (i  1,, I ) is known (given).

Si  Si 1 
)
2


I

max   J ( R ,
i

i 1

Ri
i 1,, I

subject to Design
D i operationi Inflow values (levels) are explicitly given‐>> Deterministic
 Single reservoir system

Si 1  Si  Qi  Ri
0  Ri  Rmax
S min  Si  S max

Deterministici i i Dynamic
i Programmingi
Operation (referred as DDP hereafter)(i  1, , I )where

Qi andd Ri : inflow
i fl andd releasel
att stept i, Si :storaget
att theth beginningb i i off stept i
J (, ) : assessment function
Rmax :upper limit of release,release S min and S max : lower and upper limit of storage.
storage© T. Hori, Kyoto University© T. Hori, Kyoto University

Deterministic DP operationImax   J ( R ,
i

i 1

Ri
i 1,
1 , I

Deterministic DP operation

Si  Si 1 
)
2




SI 1

I

i

Si  Si 1 )2
S  Si 1
Q  RI 1 
 I 1)  J ( RI , S I  I) max   J ( Ri , i
22Ri i 1i 1,, I

12

i 1

Ri
i 1,
1 , IS  Si 1
Q  RI 1 
 I 1
 max   J ( Ri , i)  J ( RI , S I  I
)
2
2
Ri
 i 1SI

I

max   J ( R ,i 1,, I

SI

I 1

5
12

SI 1

I

12
7
18

7

18
6

10

15
© T. Hori, Kyoto University© T. Hori, Kyoto University

Deterministic DP operation
 I 1max   J ( R ,Rii 1,, I

i 1

i

I 1

Deterministic DP operation

Si  Si 1
Q  RI 1 
)  J ( RI , S I  I
)
2
2


Q  RI 1 f I ( S I )  max  J ( RI , S I  I
)
2
RI



 I 1S  Si 1
QI  RI 1   max   J ( Ri , i)  max)ma J ( RI , S I 


2
2
R


i

1
I
Ri

for given S Ii 1,, I 

for given S ISIS  Si 1 I 1 max   J ( Ri , i)  f I (SI ) 2 i 1Ri

Ii 1,, I

f I ( S I  level 0) 

12

where

f I ( S I  level 1) 18

f I ( S I  level 2) 

1010f I ( S I  level 3) 

88Q  RI 1 f I ( S I )  max  J ( RI , S I  I
)
2
RI


for given S I© T. Hori, Kyoto University

SI 1

I 1

12
18

© T. Hori, Kyoto University- 142 -

I

max   J ( R ,Rii 1,, I



i

i 1

Si  Si 1 )2
Si  Si 1)2

 I 2max  J ( R ,Rii 1,, I  2



i

i 1

max  J ( R , S
R I 1for given S I 1



I 1

SI 1

I 1

Defining

Q  RI 1
f I 1 ( S I 1 )  max  J ( RI 1 , S I 1  I 1
)  f I ( S I 1  QI 1  RI 1 ) 
2R I 1QI 1  RI 1
)  f I ( S I 1  QI 1  RI 1 ) 
2


S
I
I
I 1

for given S I 1

producesRii 1,, I

30

15
12

I

max   J ( R ,

12



18



10

Si  Si 1)2

 I 3ii 1max  J ( RR I 2for given S I 2

20

Si  Si 1 )2
max  J ( R ,Rii 1,, I  3

9

i

i 1



I 2

, SI 2 
QI  2  RI  2
)  f I 1 ( S I  2  QI  2  RI  2 )  .
2



8
© T. Hori, Kyoto University© T. Hori, Kyoto University

Recursive functions derived

Consideration of uncertainty

Q  RI i
f I i ( S I i )  max  J ( RI i , S I i  I i
)  f I i 1 ( S I i  QI i  RI i ) 
2R I iIn the Deterministic DP model, values of inflow level are
given. Actually the inflow level differs year by year eveni theinh same day
d in
i the
h year.

f givenfori
S I ifor i  0, , I  1
f I 1 ( S I 1 )  0.

Stochastic approach is required.

Applying the recursive function backward ｆｒｏm the end
period to the beginning one gives the optimal release as
the function of storage levels at the beginning of eachperiod.

Inflow level in each period is not independent: Highcorrelation between inflow levels at neighboringgg timeperiods is usually observed.

© T. Hori, Kyoto University© T. Hori, Kyoto University

One‐order Markov Chain

One‐order Markov Chain

 0.6 0.3 0.1 
Pr[ Qi | Qi 1 ]   00.3
3 00.55 00.22
 0.3 0.3 0.4 Conditional probability of inflow levels during time period i, Qi ,T

 0.3 If the probability distribution of Qi 1 is given as Pr[ Qi 1 ]   0.5   0.2  
you can get the probability distribution of Qi as follows

for the levels of inflow in the previous perid,
perid Qi 1.
Pr[ Qi | Qi 1 ]

 Pr[Qi  1| Qi 1  1] Pr[Qi  2 | Qi 1  1] Pr[Qi  3 | Qi 1  1]   Pr[Qi  1| Qi 1  2] Pr[Qi  2 | Qi 1  2] Pr[Qi  3 | Qi 1  2]  Pr[Q  1| Q  3] Pr[Q  2 | Q  3] Pr[Q  3 | Q  3] 
i
i 1
i
i 1
i
i 1



 0.6 0.3 0.1 
P [Qi ]  P
Pr[
Pr[[Qi | Qi 1 ]  P
Pr[[Qi ]  (0(0.3
3 00.55 00.2)2)   00.3
3 00.55 00.22
 0.3 0.3 0.4  0.6 0.3 0.1   00.33 0.5
0 5 0.2
0 2
 0.3 0.3 0.4 

T

0 3  0.6
0 6  0.5
0 5  0.3
0 3  00.2
2  00.33
 0.3
  0.3  0.3  0.5  0.5  0.2  0.3 


 0.3
1  00.5
5  00.2
2  00.2
2  00.4
4  0 3  00.1
© T. Hori, Kyoto University© T. Hori, Kyoto University- 143 -

One‐order Markov Chain

Stochastic Dynamic Programming

Once you have observed that the inflow discharge at the previous time

Then the expected benefit when you select release discharge during period iperiod,i d Qi 1 , isi levell l 2,
2 thenh you can obtainb i theh probability
b bili distribution
di ib ias level 1 in case the inflow level has been observed as level 2
will be given byof inflow at current time stage as:
Pr[Qi 1 | Qi 1  level 1]=  0.3
0 3 0.50 5 0.20 2.

5  0.3+12
0 3+12  00.5+7
5+7  00.2=8.9.
2 89

SINote that if yyou specify
p y the release dischargeg at level 2 when the storage
g levelat the beginning of period i is level 2, the storage level at the beginning of
period i  1 cannot be specified uniquely. We can get instead the probability
distribution of Si 1 as:

SI 1

I

I 1

5
12
7

 0.3 (for S i 1  2  1  2  1) 
Pr[ S i 1 ]   0.5 (for S i 1  2  2  2  2) 
 0.2 (for S  2  3  2  3) 
i 1



© T. Hori, Kyoto University© T. Hori, Kyoto University

Stochastic Dynamic Programming

Stochastic Dynamic Programming

Then the expected benefit when you select release discharge during period i
as level 1 in case the inflow level has been observed as level 2
will be given byf I i ( S I i , QI i 1 )5  0.3+12
0 3+12  00.5+7
5+7  00.2=8.9.
2 89

SI

SI 1

I



I 1

max

R I ifor given S I i , QI i 1

QI i  RI i K)  Pr[Q I i | QI i 1 ]   J ( RI i , S I i 
2QI i 1

 f I i 1 ( S I i  QI i  RI i ) 

( S I  2, QI 1  2)

for i  0,0 , I  1

5127© T. Hori, Kyoto University© T. Hori, Kyoto University

Summary ThereTh
are ttwo ttypes off operationaltil approach:h namelyl design
d ioperation and real time operation according to the inflow
information.
 The most fundamental approach to optimal reservoir operation
is optimization be deterministic dynamic programming.
 Uncertainty is inherent in reservoir operation and one of thecommonly used optimum control under the uncertainty iscalled stochastic dynamic programming, which employ the one–order Markov chain as the model of inflow variations.

© T. Hori, Kyoto University- 144 -

Lecture 9: Wadi Flash Floods Risk Management under Changing
Climate in the Arid Regions
Sameh Ahmed KANTOUSH(Water Resources Research Center, Disaster Prevention Research Institute, Kyoto University)
1.

Definition of Wadi System:

The water resources availability in the arid regions is questionable over time due to a number offactors such as climate change and associated impacts such as uneven rainfall patterns, frequent
extreme events in the last ten years (flash floods and droughts) and high evaporation rates. Wadi in
Figure 1 is a dry river in the arid regions that can discharge large water volumes after heavy rainfall
either to coastal or desert plateau. Wadi Flash Floods (WFF) occurs with little warning, causing deaths,
roads, houses and properties damages. The main cause of WFF is not limited to weather or wadi
conditions, but also the distribution of human populations near water. The rapid increase of population,
urbanization and economic developments have pushed people for building in high disaster risk zones
such as on wadi flood plains. WFF are known from other types of urban and river flooding by the shortduration and the small spatial scales inside the wadi in which flooding occurs.

Flood Retention Dam

Figure 1: Main channel of Wadi system with and without flash flood
2.

Key Questions and Challenges of Wadi Flash Floods (WFF):

The associated social and environmental risks after WFF of drylands receive little attention due to therelative infrequent occurrence of runoff events and the society awareness is very weak as WFF notconsidered as a disaster until it occurs. Figure 2 shows WFF challenges and management options. In
such drylands, the law enforcement is absent and random settlement of the immigrants is common
inside wadi. What is measured, is managed WFF as the data is discontinuous and never enough due tothe lack of rainfall and runoff continuous monitoring. Therefore, the hydrological modelling for theseungauged catchments is neither calibrated nor verified and the consequent hydrological analyses are
fraught with great deal of uncertainty. Decision makers have no tools to assist them in the planningagainst WFF risks. Furthermore, wadi local people and nearby urban area are not entirely involved in- 147 -

the planning of the flash flood mitigation strategies. Flood protection structures are not common and isdone on individual level no integrated grouping of Wadi

2

How quickly are
the flow and how
much volume?
how to improve
HydroMet?

1

3 4
What are Wadi
classifications,strategies, anddrainagechannels?

5

How muchrainfall intensity,
duration,frequency?

69What climate change
impacts will Wadi
experience? How
will affect Wadi
development?

8

What are the impacts on
ecology and human? How
can the strategies befunded, staffed andmonitored?

What is theinfiltration rates
and suitable
harvesting
technology?

7

How is thesiltation currently?
Which sedimentstrategy should be
implemented?

Which impacts
require actions toaddress them?

What are thestrategies shouldbe pursued toaddress mitigationand development
needs?

Figure 2: Key questions, challenges and strategies for Wadi Flash Floods in the arid regions
3.

Hydrological Modelling and Early Warning System:
To face data limitation on arid environment, a physical-based distributed hydrological model andremote sensing data as well as GIS technique have been integrated to simulate flash floods of target
wadi in the arid regions. Hydro-BEAM (Hydrological River Basin Environmental Assessment Model)
which was first developed by Kojiri, et al. 2002, and it was also adopted as Hydro-BEAM-WaS(Hydrological River Basin Environmental Assessment Model Incorporating Wadi System) by Saber et
al. 2010, to be applicable at Wadi system in arid regions.
4.

Integrated Strategies of WFF in the Arid Regions: Mitigation and Harvesting
There are several flash flood mitigation measures in the arid wadi’s as storage dams, recharging

dams, artificial lakes and embankments, although soft mitigation measures as early warning systems
still very rare. Not all flash floods are destructives and harvest of floodwater and rainfall runoff forhuman, livestock usage and agricultural developments, should be considered in the management byconstruction underground dams, artificial lakes, recharge dams and off-stream structures. Until today,
no comprehensive proper strategy, mitigation and water resources management for wadi systems in
the arid regions. There is an urgent need to establish a guideline and integrated method for assessmentand mitigation of potential wadi’s for future developments projects mitigate and utilize floodwater as a
new supply to sustain a minimum water resources base in rural desert areas. Important researchchallenges include, understanding the processes that drive the extreme rainfall events, determining
how frequent such events occurred, defining proper mitigation measures, and developing reliablehazard maps and warning system.

- 148 -

International Hydrological Programme (IHP)

Background and Problematic

Risk Management of Water-related Disasters under Changing Climate
The Twenty-fifth IHP Training Course (30 November - 11 December
December, 2015
2015, Kyoto
Kyoto, Japan)

Lecture 9: Wadi Flash Floods Risk
Management
Changing
Mt UnderU d Chi
Climate in the Arid Regions
gSameh Kantoush
Water Resources Research Center
Disaster Prevention Research InstituteKyoto University
Email: kantoush.samehahmed.2n@Kyoto-u.ac.jp

The availability of water resources in the arid regions are under increasingpressure due to climate change impacts as droughts and floods.



Rapid increase of population and urbanization has pushed people to highdisaster risk zones such as Wadi flood plain.
plain



The frequency of WFF between 2009 and 2015, has increased markedly in the
Arab regions, as has the estimated economic damage they cause.



Huge efforts by governments of the Arab countries to enhance flash floods
monitoring, modelling, install mitigation structures and early warning systems

WFF riski k assessmentt should
h ld beb incorporatedi
t d ini developmentdlt projects
j t forfdisaster risk reduction and develop integrated management strategies.



Until today,
properand water resourcesy, no comprehensivep
p p strategy,
gy, mitigationgmanagement for wadi systems in the Arabian region.



There is an urgent need to establish a guideline for assessment and mitigation
and utilize floodwater as a new supply.
supply

Important research challenges include, improved data collection techniques,definingg flash flood typesyand the reasons for their occurrences, spatial andtemporal distribution, forecasting, warning systems and mitigation measures,
sedimentation, wadi ecosystem, water reuse and harvesting techniques.
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Types of Disaster Risk in the Arab Region

Definition of Wadi Flash Floods (WFF)

W di isi a dryd riverbedib d thatth t can discharge
di hlt volumeslft heavyhi f ll
Wadi
large
water
after
rainfalleither to the coastal or to the desert plateau.

g, causingg deaths,, roads,, and ppg
WFF occurs with little warning,properties
damages.



The main cause of WFF is not limited to weather or wadi conditions, but also the
distribution of human populations near water.



WFF are known by the short duration and the small spatial scales inside thewadi in which flooding occurs.

Droughts
Hot driving
Windstorms

Dust and Sand
StormsNaturalDisastersin ArabRegionLandslides

Desertification

Flash
Fl d
FloodsFlood Retention Dam
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan2

3

Environmental
Ch
Changes

ClimateChanges

Earthquake

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Objectives
We would like to stress on the existing gaps for
integrated strategy and guideline for country affected byflash floods, to provide suggestions on:
FLOOD

MISSING

How to improve community understanding of theeffects of flash floods,

How to upgrade the current design guidelines,
protection measures, and implementation strategies,



Steps that can be taken to establish a detailed code fordecision makers and to ensure that communities andlocal government are aware of the hazards associatedwith flash floods and are prepared to deal with themthrough appropriate mitigation strategies.
strategies

FLASH
TO

DUE

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan- 149 -

6

Flash Flood Governing Parameters

Characteristics of Arid RegionsSeveral factors contribute strongly to wadi Flash Floods among them are thehigh permeability of the desert, high recharge of ground surfaces, and steep
slopes.



A majority of flash-flood-related deaths occur in motor vehicles as people seek
shelter and/or try to escape from rising waters.



No integrated /standardized methodology for flash flood protection &harvesting.

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, JapanThe hydrology of arid areas is very different from that of humid areas and muchless well understoodData capture is problematic - events are infrequent and damaging,damaging networks aresparse and record lengths limited (Experimental networks are limited)

Rainfall events in arid areas are hard to forecast, uneven patterns, highlyvariablei bl ini space andd time.
tiand time and often highly localized.

W di FlashWadi
Fl h floods
fl d cause severe damagesdandd difficult
diffi lt tto quantify.
tif

Absence of base flow.

S
it off plantl t cover.
SparsityHigh transmission losses.



High
and
Hi h potential
t ti l evaporationti
d evapotranspiration.
t
i ti



Water harvesting and groundwater recharge near Wadi channel is unreliable.

M t off Wadi’s
W di’ havehhi h infiltration
i filt ti
t in
i the
th sandy
d areas andd lowli filt ti
Most
high
rates
infiltration
rates in the rocky areas.
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Typical Wadis Hydrograph (Wadi Abu Hamra – Western Sudan)

Africa is suffering from recent Drought – Kenya 06
Abd ll Abdelsalam
AbdallaAbd l lAh d Flash
Ahmed,Fl h Floods
Fl d ini AridA id anddSemi-arid Region: Overview of Sudan Experience, FirstInternational Symposium on Flash Floods, 2015, Kyoto,Japan

Source: Abdalla Abdelsalam Ahmed, Flash Floods in Arid andSemi-arid Region: Overview of Sudan Experience, FirstInternational Symposium on Flash Floods, 2015, Kyoto, Japan

Source of pictures
pictures:: Prof Dr Abdalla Abdelsalam
Ahmed, UNESCOUNESCO-CWR
9

●
●
● ●

2002

● ●

2003

●
●

●
●

2007

Somalia

Libya

KuwaitEgypt

Djibouti

Comoros
C

Algeria2011

Lebanon
L

● ● ●
●
●
●
●
2012
● ●
2013 ●
●
●
● ●
2014
2010

●
●
●
● ● ● ● ● ● ● ● ●
● ●
●
●
● ●
●
●
● ● ●
● ● ● ● ● ●● ● ● ●
● ● ● ●
● ● ●
● ● ●
Iraq

2009

11

●

●

Jordan2008●

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan- 150 -

●

●
●
●
● ● ●
●
●
●
●
● ● ● ● ●
● ●
● ●

12

UAE

●

Qatar

2006

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

●
●
●

2005

Yemen

●

Syria

● ●

Saud
di Arabia

2004

Effective management is essential, and this requires appropriate understandingof the hydrological processes in these areas.

Tunisia

2001

Bahrain



2000

Oman

Flash floods in these Wadis are p
produced byy warm,, moist air masses that
produce intense rains as they are pushed over the coastal ranges

PalestinePFlash Floods Under Changing Climate

MoroccoM

Rapid developed flood in few minutes or hours of excessive rainfall without
visible signs of rain, or accident as dam or levee break.

Sudan

Arab Region Between Drought and Flash Floods


10

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Maauritania

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Source of pictures
pictures:: Prof Dr Abdalla Abdelsalam
Ahmed, UNESCOUNESCO-CWR

Frequency of Flash Floods

Flash Flood Damages in 2010 Saudi Arabia & Egypt
Saudi Arabia

 The frequency of WFF extreme
events have become more
frequentq
in Egypt,
gyp , Oman,, Saudi
Arabia, Sudan,…etc.

Egypt

Sinai Egypt

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Source of pictures from Prof. Gamal Koteb, NWRC, MWRI, Egypt

13

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Flood Damage in Sinai in 2004 and 2010

14

Wadi ElArish

Flood Damage in Sinai

Delta Nuweba in Wadi Watir

Delta Nuweba in Wadi Watir

Destroyed mitigation structure(Baffles block)Source of pictures from Prof. Gamal Koteb, NWRC, MWRI, Egypt

Source of pictures from Prof. Gamal Koteb, NWRC, MWRI, Egypt

Delta Nuweba in Wadi Watir

Road Damages and undercutting
Destroyed mitigation structure(detention dams gabions)
Source of pictures from Prof. Gamal Koteb, NWRC, MWRI, Egypt
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Source of pictures from Prof. Gamal Koteb, NWRC, MWRI, Egypt

Examples of Flash Flood Damages
Flash flood in Morocco's,
Morocco s, 2014

Spillway of El-Rawafaa Dam during
fl h fld in
i 2010,2010 ini Sinai
Si i
flash
flood

Source: Breaking news.ie

Source of pictures from Prof. Gamal Koteb, NWRC, MWRI, Egypt

Flash flood in Saudi Arabia, 2013
Source:riyadhconnect.com
Flash flood in Morocco's,
2013

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan- 151 -

18

Historical Records of Flash Floods in Egypt
Area

21 February

Sinai, Red Sea region

Damages and Ref.
Mediterranean Sea

2015
Dam failure Sohag, Road

Ombo

damages in Kom-Ombo,

January 2010

Along the Red Sea

Heavy rainfall associatedMay 1997

Safaga and El Qusier

Heavy rainfall associatedNovember

Hurghada and Marsa

with thunderstorms.

_
Cairo^

fS

Sohag, Aswan, Kom

2014

lf o

March

Gu

Aswan

1996

Alam

Destroy roods, 200

November

Sohage, Qena, Red

death, damage of

1994

S (S
Sea
(Safagaf
andd El

vehicleshi l

W. Abadi

Eastern Desert

Nile River Basin Wadi
Red Sea Basin Wadi

W. Umm_Buweirat

Gulf of Aqaba Basin Wadi
Gulf of Suez Basin Wadi
Mediterranean Sea Basin Wadi

Wadi General Flow
50 Direction
100
200
300
400

Km

Sudan

Marsa Alam

20 October

Wadi El Gemal

1990

between Marsa Alam

October 1979

and Shalateen
Marsa Alam and El
QusierSouth Sinai

Historical Records of Flash Floods in Oman

W. Ambagi

Red
Legend-Reports of Red Sea
Governorate 1994
Governorate,
1994.

-

Red Sea
Environmental
Profile 2008

27°0'E

30°0'E

33°0'E

Eastern Deseret& Sinai Peninsula main
wadis showing different wadi systems
based on the downstream feature(wadis in red outline were visited at thefi ld investigation)fielditi ti )Wadi
El-Arish197525th IHP Training
death / Road
Course, 30 November – 20
11 December
2015,roblems
Kyoto University, Kyoto, Japan

19

Saud

W. El_Baroud
W. Safaga
W. Gasous

W. Qena

Weastern Desert

0Qusier)August 1991

October 1987

W. Assuiti
W. Abu_Shieh

L ib yawith thunderstorms.

u ez

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Date

aba

Fatalities
Damages
122 killed and more than 270 million USD, at
350 were missing
least 3000 vehicles
were swept away ordamagedJan. 2011 Jeddah
10 people died, 18 000
people displaced
May 2012
20 people died
April and Riyadh, Baha Taiif, 24 deaths, 4 missed 4544
evacuatedMay,y, 2013 and Hail
peoplepppeoples,p
p , hundreds cars
damage, Tabalah dam
collapse (capacity 68
million m3)
Nov. 2013 Riyadh, Arar and 11 deaths, 4 missed
AlBahapeopleJan. 2014 MekkahMar. 2014 Hail and Riyadh
4 deathsMar. 2015 Meccah,Asir, 11 deaths
over 400 vehicles have
Najran and Riyadh
been trapped in floodwatertAlmazroui, 2011of A qAffected Area
JeddahGu lf

History of Flash Floods in Saudi Arabia
Date
Nov. 200920

Departurre (mm)

MUSCAT 5-YEAR RAINFALL 1895-2010
DEPARTURE FROM MEAN (98 mm)

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Source: A. Al Barwani, Flash Flood Mitigation and Harvesting Oman case study, First International Symposium on Flash Floods, 2015,22
Kyoto, Japan

21

Flash Floods Caused by Thunder Storms

Flash Floods Caused by Cyclones

Thunder storms are common in Oman and when associated withheavy rains they cause flash floodsOn Dec. 1995 - 283 mm recorded in Musandam for the month. It
y floods washed roads, 100 leave their homes.
rained for 11 days,
 November 1997 –very heavy rainfall causing floods most part of thecountry. 83 mm of rainfall was recorded in 1hr causing flash floods.
 6-7 March, 1999 - Heavy rainfall causing flash floods especially inSur. Gauging station recorded 1250 m3/sec. The village of Sur wascoveredd withi h more thanh 1 meter flooding.
fl di On the 14th April 2003 a line of thunderstorms. Rainfall of 82 mm
was recorded
d d ini BahlaB hl within
ithi lesslth 12 hours.
thanhDDrowningioffaround 20 people, Swept cars, uprooted trees, destroyed roads, anddamaged electricity and telephone cables














On theh 5thh June 2007 Tropicali l Cyclonel((Gonu)) approached
h d theh coast off Sharqiya
h i Region.
i
The most disastrous Tropical cyclone ever recorded in the history of Oman TC record
T C Gonu was associated with Heavyy Thunder storms, strong
g winds, highg sea and floodinggSurface wind reached to Category 4 : 211-250 km/h.
49 people left died, and estimated loss of 1.5 billion R.O ($4 billion).
On the 4th June 2010 Tropical Cyclone (Phet) approached the coast of Oman near Qalhat
and proceeded to Muscat and Batinah region.
The 2nd most disastrous Tropical cyclone ever recorded in the history of Oman TC record
Phet was associated with Heavy Thunder storms, strong winds, high sea and flooding.
Surface wind reached to Category 4 : 211-250 km/h at sea.
6 people reported dead, and estimated loss of R.O 780 million ($2 billion).

Sources: A. Al Barwani, Flash Flood Mitigationand Harvesting Oman case study, FirstInternational Symposium on Flash Floods,
Floods 2015,
2015
Kyoto, Japan

Sources: A. Al Barwani, Flash Flood Mitigation and Harvesting Oman case study, First International Symposium on Flash Floods, 2015,23
Kyoto, Japan

24- 152 -

Flood Damages in Oman

Source: A. Al Barwani, Flash Flood Mitigation and Harvesting Oman case study, First International Symposium on
25FlashFloods, 2015, Kyoto, Japan

Sources: A. Al Barwani, Flash Flood Mitigation and Harvesting Oman case study, First International Symposium on Flash
Floods, 2015, Kyoto, Japan

Source: A. Al Barwani, Flash Flood Mitigation and Harvesting Oman case study, First International Symposium on Flash Floods,
2015, Kyoto, Japan

Source: A. Al Barwani, Flash Flood Mitigation and Harvesting Oman case study, First International Symposium on Flash
Floods, 2015, Kyoto, Japan

Khartoum Flash Flood 2013

Flash Floods Season 2013 – Khartoum State

Khartoum, Sudan, 2014

Abdalla Abdelsalam Ahmed, Flash Floods in Arid and Semi-arid Region: Overview of Sudan Experience, FirstInternational Symposium on Flash Floods, 2015, Kyoto, Japan

Abdalla Abdelsalam Ahmed, Flash Floods in Arid and Semi-arid Region: Overview of Sudan Experience, First International
Symposium on Flash Floods, 2015, Kyoto, Japan- 153 -

Key Questions for Wadi FF and Strategy Development

Methodologies

2 3 4

1

What areWadi
classifications, strategies,
and drainage
channels?

How quickly
are the flow
and how muchvolume? how
to improve
HydroMet?

To simulate physical processes
Spatially distributed rainfall

69What climatechange impacts
will Wadi
experience? How
will affect Wadi
development?
What are the impacts
on ecologylanddhuman? How can thestrategies be funded,
staffed and
it
d?

8

Precipitation

Evapo-transpiration
surfa

5

How muchrainfallte s ty,intensity,
duration,frequency?

Recovery flow Waste
Ａ layerce run

off

water
Riv

Ｂ layerw

Spatially distributed hydrologicalmodel (HydroBeam-Was)

infiltration GW runoff

How is thesiltation
currently?
Which sedimentstrategy shouldbe
implemented?

Ｄ layer
Transmission losses

Wadi System

Hydrological River Basin Environmental Assessment Model Incorporating Wadi
SystemModeled cell
(1km, 2km, …etc)

1Dhhydraulicdli modeld lWadi flow+
Hydraulic structures

What are thestrategies
shouldh ld bbepursued toaddress
mitigation anddltModel componentspMeteorological Data KinematicKi
i wave methodh dMeteorological Data

Linear storage model

Hydro-BEAM
H d l i l river
Hyd
Hydrologicali
B i Environment
Basin
E i
entt

Geomorphologic model

Geographical Data

precipitation

evapo-transpiration

surface runoff

ＡＡlayerlayer

recovery flow

waste water

Topographic maps

Source: Thesis of Dr.
Dr
Mohamed Saber, 2010

Precipitation

Digital Elevation
Map (DEM)

Model components
Ki
ti wave Model
M d lKinematic((Surfaceflow))
GeographicalG
hi l DataD
SRTM (100m); GIS

layer
infiltration
ＣＣlayer
Ｄ layer

Ｄ layer

１㎞ 2Surface flow
direction determination

groundwaterrunoffTranmission losses

Runoff observed Data

Flow direction map

Hydro-BEAM

Linear storage model(Subsurface flow)
SCS Method(Initial loss)

Meteorological Data
Walter’s Eq.
NCDC or GSMaP
25 IHP Training Course, 30 November – 11 December 2015, Kyoto (Transmission
University, Kyoto, JapanLoss)thTemperature

Thornthwaitellayer
ＢＢlayer

Source: Thesis of Dr.
Mohamed Saber, 201032

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Hydro-BEAM incorporating Wadi system (Hydro-BEAM-WaS)

Assessment Modeler floInitial losses

Ｃ layer

Which impacts
require actions
to address
them?

7

Geographic andLand use Inputp

Rainfall Input

What is theinfiltration
rates andsuitable
harvestinggy
technology?

methodDistributed Surface
runoff modelLand use classification

Initial andtransmission losses

Slope angle determination

Groundwater
Recharge
Distributed
Groundwater
M d l
Model

Evaporation &Evapotranspira
E
t
i
tion

Surface/groundwateri tinteraction
ti M
Model
d l

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

33

Watershed Modeling W. Al-khoud (Oman)

Flash flooding simulation of Wadi Elarish; Sinai

p.5072

Downstream point

p.5108p.5058

p. 3811

p. 3315pp.3256p.2401

p.2399Discretized by(1 km X 1 Km) Cell Size

Simulated events:
1‐
1 Jan.
J 18‐20,
18 20 2010
2‐ 2 km Resolution

W.Alkhoud Area : 1874.84 Km2Catchment area
is 20,700 km2

Watershed Modeling Using GIS

Saber et al. 2010
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan- 154 -

36

36

Flood protection in downstream (W. Abadi)

Flash flooding simulation of Wadi Elarish; Sinai, Egypt (Dr. M. Saber Thesis, 2010)
El‐Arish Cityy

3500



El‐Rawafaa Dam

p.5108

Rainfall0

Embankment, flood guiding

40

p.507
2

3000
5108‐
downstreampoint

p.505880

Point 5058
2500

p. 3811
Point 3811
120

p. 3315

Drainage channel inside ofembankment

p.3256point 3256

160

Point 2401
1500

200

Rainfall (mmm)

Dischargee (m3/s)

point‐3315
2000

Point 2399

El‐Rawafaa‐Dam
240

1000

p.2399pp.2401280

500

M AbdelM.
Abdel-Fattah,
Fattah S.
S Kantoush
Kantoush, T
T. SumiSumi,
Annual Report of DPRI, 2014

320
0
1

11

21

31

41

51

61

Time (hour)

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Possible Research Wadi
Flash
Flood

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan38

Study Area

Wadi Abadi
near Idfu

Dry damSpillwaypy

Qdam

Bottom outlet

Qdam• Inflow design hydrograph : 10 yr, 50 yr, 100 yr …….
• Outlet dimension: When the dam is full
full, outflow
discharge (Qdam)

Location Map of Wadi Abadi

Wadi Abadi is located in the Eastern
Desert of Egypt of 6,800 Km2
M Abdel-Fattah
M.
Abdel-Fattah, S.
S Kantoush
Kantoush, T
T. SumiSumi,
Annual Report of DPRI, 2014
201030DPRI
Annual–symposium,– 24 Kyoto
February
2010, UjiKyoto, Japan
25th IHP Training Course,
November
11 December232015,
University,

39

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Precipitation Data

Distributed or Concentrated Dams?
Upstream damsIntermediate dams

D
Downstream
d
dams



Th usedThe
d PrecipitationP
i it ti datad t isi GSMaP
GSM P (Global(Gl b l Satellite
S t llit MappingM
iof Precipitation) which developed by GSMap Project. (TRMM
Real-Time Office E. O., 2013)

M. Abdel-Fattah, S. Kantoush, T. Sumi, Variable
DomainAnnual Report of DPRI, 2014
Grid resolution
Temporal resolution2v i = storage
volumeoff individual
damstl
i di id l d40

Rainfall rate (mm/hr)
Global (60N - 60S)
0.1 degree
1 hour

M. Abdel-Fattah, S. Kantoush, T. Sumi,
AnnualAnn al Report of DPRI,DPRI 2014

Constant volume & equal weight for each placement
Hourly Distribution Map of Precipitation at Wadi
Abadi 17‐18/1/2010

Outlet dimension : Qdam = 2 yrs return period (m3/s)
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

41

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan- 155 -

Cross Sections

Simulated Discharge Vs. Precipitation

M. Abdel-Fattah,, S. Kantoush,, T. Sumi,,
Annual Report of DPRI, 2014

January 2010
Flash Flood event
Wadi Abadi.

Point 6

Precipitation

Point 8
Point 3

Discharge

44
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, JapanKey Questions for WFF
1 Rainfall
1-

Objectives
Until today, no proper protection from flash floods proposed for all
Wadi basins in Egypt:

8- Climate Change

Rainfall Intensity, Frequency, Duration?
Rainfall Monitoring and Past Records
Hydrological Modelling for Predication
How much Volume, and How Fast?

To clarify the occurrence and processes of Flash floods;
To mitigate and utilize floodwater as a new supply to sustain aminimum water resources base in rural desert areas;
Set-up potential hazard map with flash flood warning system;
Prioritize Wadi systems based on risk assessment;
Define obstacles to flash flood flows and prepare protection plan;

2- Wadi Catchment
Wadi Characteristics andClassifications

3- Groundwater
5- Drainage Channel Source: Sumi &Kantoush, 1 ISFF 2015,
and Target point
Kyoto, Japan.
st6- Hazards, Social & Wadi Ecosystem
7- Land Use Planning
Industrial, Urban, Agricultural, Touristic,
Energy, and Development Areas

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Master planning of flash flood management

Wadi Flash Floods Management Strategies
1 StructuralSt
tl Measures
M
1Dams (blockage, storage,
 retention, recharge)
 Distributed or Concentrated?
Embankments and Artificial Lakes
Drainage Channels Improving
 and Cleaning

46

Distributed
Di
ib d
Dams

Integrated Approachis required

3 Water
3W t
Temporally water storage
Harvesting
Groundwater Recharge
Underground dams
Agricultural development

2- Non-structural Measures

Concentrated Dam
Channel Improvement
Source: Sumi & Kantoush, 1st ISFF 2015, Kyoto, Japan.

Target protection area

Monitoring and Early Warning
Guidelines
Hazard Maps
Maps, Risk Maps
Public Awareness and Education
Training

Catchment area
D
Dam
C
Constructiontti
Wadi channel improvement
Source: Sumi & Kantoush, 1st ISFF 2015, Kyoto, Japan.

- 156 -

Wadi Flassh Floo
ods Manage
ement

Main dam(Retention dam)

Detention dam(Obstacle dam)
Source: Sumi &Kantoush, 1st ISFF
2015, Kyoto,
Japan.

Channel Improvement

Hard
Structural
Measures

Mitigation and Flood Protection Measures

Flood Retention Dams
Storage Dam and
Groundwater recharge

D iDrainage
Channels
Sediment Trap

Soft
Non‐Structural

Measures

Monitoring &Hydro‐Met
Risk
Assessment
Early Warning
Guidelines/Planning
PublicAwareness

Development
Projects

Embankment

Blockage Dams

Dams

Underground Dams
Embankments
US Dikes and Baffles
Rainfall and Water levels
Topographic and SedimentHydrological Modelling
Dissemination andcommunication
Atlas of Flood Maps
Ecologicalll andd Human

Water
Harvesting

Agricultural

Road, Railway&
Infrastructures

Industry and Tourism
Urban and Energy

Reservoir sedimentation
Sedimentation will be crucial for sustainability ofdams and artificial lakes
 Some dams have already losing capacity andflood retention functions
 DepositedD
i d clay
l can beb usedd forf making
ki bricksbi k Dam should have outlets or slit for flood flow
sluicing
l i i tot minimizei i i sedimentdit depositiond
itiFlood Retention Basin of
Ligistbach, Styria, Austria
O d Dam
Orden
D
S
Switzerland
it l dHochwasserrückhaltebeckeni GermaninG
Masudagawa dam, Shimane, Japan

Flood Retention Basinsin Switzerland and Europe
T a ylo rsville D a m

Dry dam in USA

R
Ryusuigata
i t damdini JapanJR e servo ir

E m bankm ent dam

S p illw a y

Stilling Basin

B o tto m outlet

Source: Sumi & Kantoush, 1st ISFF 2015, Kyoto, Japan.

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan 51

52

Najran Dam

Dams in Wadi Jizan in Kingdom of Saudi Arabia
Before 1975 (1395 AH), there were 16 dams; the number had
increased to 230 by end of 2006 (1426/27 AH), with a storage
capacity of 850.33 million cubic meters.

Source: http://www.saudinf.com/main/f44.htm

Source: http://www.saudinf.com/main/f44.htm
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan- 157 -

Flood Mitigation Dams in OmanOman Dams Location

 Fourteen f lood mitigation dams,
dams 44 groundwater recharge

dams and more than 97 storage dams in various region andgovernorates of the Sultanate.
g Reservoir capacity of 1928 MCM where 57% of this water
recharged the groundwater aquifers.
Storage Dam

Recharge Dam

Flood MitigationgDam

Source: A. Al Barwani, Flash Flood Mitigation and Harvesting Oman case study, First International Symposium on Flash
Floods, 2015, Kyoto, Japan
Source: A. Al Barwani, Flash Flood Mitigation and Harvesting Oman case study, First International Symposium on Flash
Floods, 2015, Kyoto, Japan

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Improvement of Drainage Systems

Flood Mitigation and Harvesting in Egypt

The drainage system in many cities of the country are either poor
managed or not available.
Actions have been taken to clean the available drainage and open thewadi
di channels.
hl
The Muscat Municipality has just completed a master plan on
drainage for the greater Muscat area.

Obstacle dam : low dam(less than 4-6m), only for
reducing flood impact(velocity reduction)
 Flood retention dam : middle height dam (around 10m),
flood retention and additionally water harvesting Artificial lake : excavated, depth(2-4m), flood retention
and additionally water harvestingObstacle dam

Flood retention dam
Artificial lake

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

58

Development Projects
Agriculture in upper wadi system (W(W. Qena)

Drip irrigation for watermelon by groundwaterp p gpumping

Flood Damage in Wadi El-Sheih Dam
i Marchin
M h 2014

Dam Abutment failure

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan59

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan- 158 -

Integrated Flash Floods Management “PARADIGM SHIFT”

Design Guidelines• StructuralSt
tl Measures
M
(Dry
(D d
dams,
artificial lake, storage dams,
blockage dams
• Environmental Management• Comprehensive Land Use Plan
• No-structural measures
Mitigation:• Risk proofing
Risk Reduction /• Financial
Fi
i l toolst lPrevention• Hazard identification &• Vulnerability Analysis• Capacity Analysis① Grouping Wadi based on Rainfall events patterns and Regional Importance② Classify Importance of Wadi System based on Wadi Class③ Select Design Return Period④ Assess Past Records of Rainfall patterns⑤ Maximum
M i
Extension
E t i Rate
R t

Field
Investigations• Topographic• Hydrological• Geomorphological• Society education
for water
harvesting andinfrastructure
maintenance⑥ Design Necessary by Using the Planned Rainfall⑦ Simulated Hydrograph⑧ Target Design Necessary Hydrograph⑨ Propose Wadi Discharge Scenarios
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

61

SocioSocioEconomic
and Funds• Economic analysis• Cost- Benefit analysisy• Public awareness• Public commitment• Community actions• Education & Training• Alternative development

GlobalWarning • GIS and Remote sensing data
System • Hazard map• Hydrological modelling• Set-up an early warningysystem• Education and training:
Society flood education• community training about
disaster and for evacuation

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

Conculsions

Comprehensive Vision for Wadi Flash Floods in Arab Region

Wadi region’s new residents and investors generally lack direct
experience with Wadi flooding and its potential hazards
hazards, andmany road constructions, power plants, tourist developments andp have been sited in wadis to take advantage
g ofsolid waste dumps
flat, easily-worked ground.
 Optimalpdevelopmentpppatterns would cluster developmentpfacilities around Wadi’s and avoid building in wadis channel.
 Geomorphic features, precipitation and runoff analysis, digitizedterrain data and hydrologic models can inform land-use suitability
maps at scales appropriate to guide future development.
 ThereTh
are ffew stationsiforf monitoringi i andd forf some W.
W no data.
d Mitigation structural and no-structural measures with harvestingMitigation
MititiStrategies

Risk
Ri
k
Assessment: Flood
Fl d hazardhd Design guideline;map;Early warning Hard measures;

systemt

 Implementation

SocioEconomic

Wadi
Development andWater Resources
Management

 Training,
Training meeting
and workshop;
Capacity building Water harvesting;Road and industrial
construction

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan64ً‫شكرا‬Future Challenges










Combinations of several mitigation measure as detention,
detention storage artificial lake,
lake
and harvesting techniques;
The key question is small distributed dams or medium size of large dams
concept is better?
Also for water harvesting by recharging groundwater, construction
underground tank, and off-stream
off stream structures;
Upgrading the current design guidelines, protection measures and construction
methods;
Establish detailed code for decision makers
There is a need to enhance the knowledge of the local engineers, and decision
makers byy training,g, workshop,
p, FF code and Atlas.
Upgrading the design guidelines and construction method with natural
materials and inexpensive efficient techniques;
Warning system by hydrological modeling, monitoring, rainfall andmeteorological stations covering Wadi’s in target area;
Finallyy the target
g projectp j will be gguideline for Wadi basin p
protection work,, inEgypt, Saudi Arabia, Oman…arid region.
25th IHP Training Course, 30 November – 11 December 2015, Kyoto University, Kyoto, Japan

ありがとうございます

Th nk you
Thank
u for
f r yourur attention
tt nti n

65

1st International Symposium on Flash Floods in Wadi Systems, 14 October 2015, Kyoto University, Japan- 159 -

66

Lecture 10: Flood Risk Assessment toward Flood Risk Management
Hirokazu TATANO (Disaster Prevention Research Institute, Kyoto University)
In this class, basic procedure for flood risk management will be explained. Firstly, three componentsof flood risk are illustrated, i.e., hazard, exposure and vulnerability. To assess flood risk, not only
hazard analysis but also exposure and vulnerability assessment are needed. Hazard analysis isconducted to relate frequency and magnitude. The magnitude can be enumerated as heights of water,
flow volumes and/or velocity in the focused river or/and floodplain. To estimate flood damage, themagnitude of flood should be transformed to loss(es). To do this, we need to know population andassets distribution over the space. The procedure to estimate spatial distribution of the population
and assets which threatened by floods are called exposure analysis. Simultaneously, we need toassess the vulnerability of the exposures. In the narrow sense, Vulnerability of exposure is definedas probability of occurrence of loss conditional on the magnitude of a flood. A typical representationof vulnerability is a loss function which relates water depth to loss ratio of property. Finally, by
combing with the analyses of hazard, exposure and vulnerability, we can obtain flood risk
distribution over the space, i.e., the relationship between flood frequency and loss(es), which can be
represented as exceedance probability of economic loss caused by floods.
After explaining the procedure, students will be asked to design a flood risk assessment
procedure for their own country, and develop questionnaire survey after a flood event to develop
data needed for the flood risk assessment. At the end of class, group discussion will be conducted tofind the way to improve flood risk assessment to be utilized for effective flood risk management for
their own countries.

- 161 -

Lecture 11: Integrated sediment and floating debris management
Tetsuya SUMI (Professor, Disaster Prevention Research Institute, Kyoto University)
Abstract:
Reservoir sedimentation is one of the most crucial issues for reservoir sustainability in theworld. In many countries, various countermeasures have been implemented to decrease sedimentaccumulation and loss of storage capacity. They are (i) reduce sediment inflow, (ii) route sedimentsand (iii) sediment removal. In order to select suitable sediment management methodologies,
combination of flow and sediment release should be appropriately designed to meet demands ofvarious functions based on data of hydrology, water quality, river morphology and ecosystem, etc.
Furthermore, the integrated sediment management approach should be considered in a sedimentrouting system which covers not only a river basin but also coastal areas. Among several updated
methodologies, effective and ecofriendly sediment flushing, bypassing and replenishment techniques
have been intensively developing in Japan. Even though target volume of sediment is very muchdifferent between these approaches, positive influences should be addressed from both point ofviews of reservoir sustainability and downstream environmental improvement.
Not only sediment, we should consider intensive yield and management of floating woody
debris from upstream catchment. In recent years, localized heavy rain tends to increase. These
rainfall events have caused an increase in deep landslides and severe riverbank erosions associatedwith the increase of large amount of floating debris which flows into the downstream dam reservoirs.
They are causing adverse effects such as increasing the removal cost and causing many troubles fordam operation safely. On the other hand, floating debris captured by the dam can be pointed out thatthey are prevented the risk of expanding the flood risk by clogging at the bridges downstream river.
Recently, positive effects on river environment have been addressed. Floating woody debris has a setof ecological roles such as trophic sources in the riparian and coastal ecosystems, and provision of
habitats for various aquatic organisms. In consideration with the ecological functions, some amountof woody debris should be allowed to retain on the riverbed in river management.
Nowadays, it is required to consider comprehensive management both on sediment and floating
debris in a river basin.

- 163 -Yasuoka dam (1936,11MCM)
Hiraoka dam (1951,43MCM)

The 25th UNESCO - IHP Training Course on
Risk Management of Water-related Disasters under
Changing Climate, Nov.30-Dec.11, 2015.

Sakuma dam(1956 327MCM)(1956,327MCM)

Integrated sediment and floating debris
management
Miwa dam

Tetsuya SUMI
Disaster Prevention Research Institute, Kyoto University
Flushing, Dashidaira dam

Sediment Replenishment, Nunome dam

Sakuma dam

Akiba dam(1958,35MCM)
Funagira dam(1958,11MCM)

T
TenryuRi
River,A
A=5,090km
5 090k 2

Reservoir sedimentation in Sakuma dam

J-Power (EPDC) 1956 Power generation
Gravity concrete Height=155.5 m

Data from the GRanD database © Global Water System Project, 2011

Dam Impacts caused by reservoir sedimentation

 Dam construction dramatically influences the river basinb lbalanceforf watert / sedimentdit inflow
i fl andd outflow.
tfl
Dam Impacts• Discontinuity of

sediment transport
downstream• Modification of flow
regimesgdownstream

Sedimentation in reservoir• Reduction of storage capacity

Downstream geomorphology• Bedd armouring
i andd ddegradationd i

Downstream hydrology• Water table lowering, changes
in seasonal flow, flood
frequency and magnitude

Downstream ecosystem• Reduced ecosystem health((Biodiversity,y, QualityQy and qquantityyof food resources, Water quality)

Walling and Webb, 1983

- 165 -

River channel is formed by natural fluvial process caused by
combination of ‘water flow and sediment transport’p(Leopold 1957)

River continuum
concept
Vannote et al.
1980

Rich sediment

Naturall flow
flregimePoff et al. 1997Floodl d pulse
l concept
Junk et al. 1989

River channel as conveyor belt for
sediment (Kondolf 1994)

Fluvial river systems composed of 6
segments (Takemon 2010)

Source: S. A Kantoush,T. Sumi and Y. Takemon
MAY 2011,
2011
INTERNATIONALWATER POWER & DAM
CONSTRUCTION
Source: Reservoir Sedimentation handbook, Morris and Fan(1996)

Source: Reservoir
Sedimentation
handbook, Morrisand Fan(1996)

General shape of a reservoir storage‐yield curve, in which yield asymptotically approaches themean watershed yield, less evaporative and seepage losses, as storage becomes very large.
Two yield curves are shown for different levels of service. The impact of sedimentaccumulationlin theh conservation pooll may b
be expressedd as either
h a reductiondin yield
ld at agiven level of reliability (point 1 to 2) or a reduction in reliability at a fixed yield (1 to 3).

Source: Reservoir Sedimentation handbook, Morris and Fan(1996)- 166 -

Q
Quenchingg the Thirst: Sustainable Water Supply
pp y and Climate Change,g , GeorgegAnnandale(2013)

Sediment
impacts
assessment

Namingof priority
dam

Actions

Sedimentation
process
assessment

Economicassessment

Screening of
Sediment
management
Implementation
countermeasures
Monitoringof control
measures

Long‐term
Sustainability
off reservoiri

Criteria

Maximizing
sediment
releaseCost andbenefits
Feasibility of
sediment
strategy

SafetyEnvironmental
and social
Biodiversity
Water
quality

Food
supplylEnvironmental
compatibility
Habitatdiversity

Source: T.Sumi and S.A.
Kantoush, ICOLD Annual
Meeting, Luzern 2011

Quenching the Thirst: Sustainable
Water Supply and Climate Change,
ChangeGeorge Annandale(2013)• River channel change– Fixed sand bars, degradation ofwater course– Degradation of river channel– Tree growth in river channels• Riverbed material change– Armoring（granulation）– Decrease of small porosity

Quenching the Thirst: Sustainable
Water Supply and Climate Change,
George Annandale(2013)- 167 -

Sediment
supplyBalancing of sedimenttransport from thesource of the river to thecoast

Yasuoka dam (1936)
Hiraoka dam (1951)

1946

St
Storagereservoir

Sakuma dam (1956)
Miwa dam (1959)

Riverbedgdegradation

Lack ofsedimentdit supplyl

Akiba dam (1958)

1961
Dec
Decreaseease

Koshibu dam (1969)

Check dam

Sedimentation

Sediment flow
monitoring
Bed load
Suspended load
Wash loadRiver
environmentchangeCoastal erosion

2001

35.0%

• 1958
1958,, Technical Criteria for River Works, Ministry of
C t tiConstruction

32%
30.0%

– Necessary volume for reservoir sedimentation is 100 years
Sttoragelosss by sedim
mentation
Sedimeentation
Rate(%）nR• 1966,
1966, Reservoir
Reser oir sedimentation database gguideline,
ideline MOC– To check riverbed aggradation in order to prevent flooding risks
upstreampof reservoirs caused byy sedimentation• 1982
1982,, National reservoir sedimentation database, MOC– All reservoirs over 1 MCM should report accumulated sedimentation volumeof B, below LWL, and A, LWL to NWL, separately and sedimentation profile
almost annually to MOC.
In Japan, 2730 dams (>15m high)with 23 BCM capacity.

20%

20 0%
20.0%

Multipurpose
dams(MLIT)

14%

15.0%

13%

12%

Others
9%

10.0%

9%

6%
5.0%

Targett damsTd:
971 dams of 18.2 BCM capacity

Hydropower
dams

25.0%

3%

5%

5%

8%
6%

7%

4%

3%

8%
6%
4% 4%

6%
4% 4%
3%
2%

8%
5%

4%

6%

1% 1%

0%

0.0%
HokkaidoTohoku Kanto Hokuriku Chubu

11%

Kinki ChugokuShikoku Kyushu Okinawa Japan

Japan

Sediment check dam
Diversion weir

Afforestation

E
Excavatingti

Sediment bypass tunnel

Trucking
Dredging
Reducing
Sediment Inflow

Density current
ventingti
Sediment scoring gate
Sediment supply(augmentation)- 168 -

Sediment Routing
Sediment Removal

Earth's Future, Volume 2, Issue 5, pages
256 280 May 2014
256–280,

SBT• G. Mathias Kondolf
Kondolf,, Yongxuan Gao, George
W. Annandale, Gregory L. Morris, Enhui
Jiang,
g, Junhua Zhang,g, YongtaogCao,, Paul
Carlin, Kaidao Fu, Qingchao Guo,
Guo, Rollin
Hotchkiss, Christophe Peteuil
Peteuil,, TetsuyaSumi,, HsiaoSumi
Hsiao-WenWang,
WenWang, ZhongmeiWang,
ZhongmeiWang,
ZhilinWei,, Baosheng Wu, CaipingWu,
ZhilinWei
CaipingWu, andChih Ted Yang

Check damKondolf et al. 2014

The riverine flow
Closing the bottom outlet to
R fill andd
RefillDraw Reduction of water Erosion formed with highrefill the reservoir. During
restoredown level, to gradual and flushingvelocities scour finespecific period incoming
Stage rapid emptying of the Stage sediment and transport water water is discharged forreservoir to initiate
reservoir,
StStage
through flushing gate
rinsing DS riverbed
flushing

Name of Dam

Dam
Dam
Heightcompleted(m)Country

Japan

1985

Unazuki

Japan

2001

Gebidem

Switzerland

1968

250

220

Unazuki dam
245.000

-

40

-

-

-

-

30

⑤

①
EL.232

②

①

② ③ ④
12hr
44hr

15hr

24hr

③

⑤

④

⑤

68h

68hr

Coordinated water and sediment management is verymuch important. Rainy season, June‐July, and natural
flood timing is suitable for flushing.

Shimokurobe
20④ EL.235m

44hr

Dashidaira dam
343.000

Aimoto

24hr

③

17hr

400-

5hr

12hr

29hr
29hr
53hr
53hr

U
Unazuki
ki

② 230
210

-

12
12
70
30
20
12
36
40
168
672
72
1272
120
120
72
24
2400
2900
2900
36

EL.327240

Koyadaira dam
529.400

10

200
300
15
600
90
80
600
90
6
2
5
12.2
80
125
0.8
2.16
1000
2000
100
50

T≥12hrs

12hr

Sen-nin dam
851.000

600-

H=97m, 24.7 MCM (2001)

14.5
25.7
18.0
45.5
85.0
28.8
72.6
32.0
13.3
11.3
37.5
30.4
37.8
53.5
12.3
2.0
4.3
6.0
35.2
15.2

300

800-

0

0.00674
0.014
0.021
0.001440.000473
0.000180.00467
0.004890.00465
0.842
0.00667
N.A.
1.5
0.04330.6
0.002180.00376
0.224
0.352
0.0186③

310

Kurobe dam
1,448.000

H=76.7m,
9 MCM(1985)

1,000-

Altitute

Dashidaira

330
320

H=185m199MCM (1961),
1,200-

200-

0.62
0.96
0.5
0.33
0.02
0.008
0.73
0.3
0.07
1.18
0.08
0.23
60
1.7
0.7
5.7
13
1600
50
0.63

①
340

②

1,400-

InitialMean Annual １／(MeanReservoi Average
Storage
Flushing Flushing
Sediment
Annualr LifeFlushing
Capacity
Duration Frequency
I fl
Inflow(MAS)
Runoff))((=CAP// Discharge
(CAP)
（hrs）
(1/ yr)
3 １）（=CAP/MAR) MAS)
(m3/s）(million m )3(million m )

76.7
9.01
97
24.7
113
9
VerboisSwitzerland
1943
32
15
Barenburg
Switzerland
1960
64
1.7
Innerferrera
Switzerland
1961
28
0.23
Genissiat
France
1948
104
53
Baira
India
1981
51
9.6
Gmund
Austria
1945
37
0.93
２）
China
1966
65
13.3
HengshanSanto Domingo Venezuela
1974
47
3
２）
Taiwan1938
30
7
Jen-shan-pei
Guanting
China
1953
43
2270
Guernsey
USA
1927
28.6
91
Heisonglin
China
1959
30
8.6
Ichari
India
1975
36.8
11.6
２）
1961
35
56
Ouchi-Kurgan Former USSR
２）
China
1960
45
9640
Sanmenxia
２）
Iran
1962
82
1760
Sefid-Rud
Shuicaozi
China
1958
28
9.6
１）Average after dam completion，
２）Sluicing dams
Dashidaira

Catchment area= 682 km2,
River length= 85 km
m
1,600-

Flushing

50

60 km

The distance from the sea.

- 169 -

1
1
1
3
5
5
3
1
N.A.
2～3
N.A.
1
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.

• Dashidaira dam 1985
• First flushingg in Dec. 1991
Severe water quality problem• Sediment flushing operation rule from 2001.
2001• Tentatively stopped• Sediment Flushing Evaluation
Committee 1992
• Extreme Flood 1995
• Restart of flushing 1995, 96, 97
Gross storage: 9×106m3④ 1720×103 m3

8000⑤ 800×103 m3

7000⑫ 280×103 m3

The biggest flood in 1995

6000

⑧ 700×103 m3（3450×103m3）⑩ 60×103 m3• After 1998, general flushing
operation started

5000② 80×103 m3

4000
3000

• Unazuki dam 2001
• Coordinated flushing started
2001⑨ 590×103 m3

2000⑦ 340×103 m3

3

3

'93

'94

③ 20×103 m3

1000⑥ 460×103 m3⑪ 90×103 m3① 460×10 m

'03

'02

‘04

'01

'00

'99

'98

'97

'96

95.11

'95.7

'95.6

'92

'91

'90

'89

'88

'87

'86

0

Unazuki Dam

 Flushing efficiency=scoured sediment volume / water use

 Flushing effect= scoured sediment volume / total depositedp
sediment volume before flushing

Dashidaira Dam

 Environmental impacts～ the influences of SS rising and DO dropping SS: Suspended solid concentration
duration time etc.
DO: Dissolved oxygen

0
5
10
15

Dashidaira

600

330

Inflow discharge[m3/s]
Outflow discharge[m3/s]
Water level[EL.m]

500
400

325
320

300

315

200

310

0
700

Wate rlevel(EL . m)

Discharge(mm 3 /s)

335

100

Discharge (m 3 /s)

Maximum Discharge Average Discharge Flushing Volume
Maximum SS (mg/l) Average SS (mg/l)
Inflow (m3/s)
Inflow (m3/s)(103 m3)
2001 Flushing
333
277
590
90,00015,0002001 Sluicing
491
273
29,0006,700
2002 Flushing
Fl hi
362
215
60
22 00022,000
4 500
4,500
2003 Flushing
777
217
90
69,0007,100
2004 Flushing
356
229
280
42,00010,0002004 Sluicing
1,152
281
16,0007,300
2005 Flushing
958
290
510
47,00017,0002005 Sluicing 1835
275
90,00016,0002005 Sluicing 2790
250
40,0007,300
2006 Flushing
308
246
240
2 000
27,000
6 00
6,500
2006 Sluicing 1378
203
12,0002,500
2006 Sluicing 2685
264
27,0005,200
2006 Sluicing 3
529
196
7 400
7,400
1 800
1,800
2007 Flushing
418
245
120
25,0003,500
Average of Flushing
502
246
270
46,0009,100
Average of Sluicing
694
249
31,6006,700
Average of All Data
598
247
270
38,8007,900

Average hourly rainfall in Dashidaira basin [mm]

20
700

305
300
250

UnazukiInflow discharge[m3/s]
Outflow discharge[m3/s]
Water level[EL.m]

600
500

245
240

400

235

300

230

200

225

100

220

0

Wate rlevel(E L .m)

A ve ra ge ho ur ly
ra in fa l l( mm )Study on sediment discharge process during flushing
operations from quantity and quality point of view isvery important.

215

30,00025,000SSS(mg/l)

'85

3

3

Sedimentation Amount （××10 m ）

9000

SS(Dashidaira dam)[mg/l]
SS(Unazuki dam)[mg/l]
SS(Shimokurobe)[mg/l]

20,00015,000
10 00010,0005,000
002006/7/1

3

6

9

12

15

18

21

0
7/2

3

6

9

12

15

18

21

0
7/3

- 170 -

Sea
Water quality(DO, SS etc.)
Mud quality
Aquatic species
Cross sections

N Minimize negative environmental impacts
 WaterW t quality
lit changeshcausedd byb didischarging
h i accumulated
l t d
sediment containing organic matters and those impacts on
the aquatic ecosystem

-250
250
-150
-50

Shimokurobe

0- Estimate the influences of SS rising and DO dropping- Establish minimizing measures （ex. Sediment Discharge Rule）

5

10 kkmUnazuki dam Re-depositionpof fine sediment on the river and the coastal
areas and those impacts on the aquatic ecosystem

River
Water quality(DO, SS etc.)
Mudd qualityl
Aquatic species
Cross sections- Estimate the influences of fine sediment on river channel,
mouth and coastal areas- Establish minimizing measures （ex. Rinsing Flow Operation）

 Evaluate positive impacts correctly
 Contribution to the comprehensive management of the
Sediment Routing System
 Restoration of supplying nutrients and other materials to theriver channel and the sea

Reservoir

Dashidaira dam Water quality(DO, SS etc.)
Mud quality
Cross sections

Sediment flushing①②
Draw down

Flushing（Free flow）

SS(mg/l）50,000Year
Jul-95Oct-95
Jun-96
Jul-97
Jun-98
Jul-98
Sep-99③

Suspended sediment (SS)100,000mg/l

15Jun-01℃

Water temparetureSS(mg/l)10Jul-01DO(mgg/l）

Jul-02Di l d O
Disolved
OxygenJun-03
Jul-0410
4Jul-04Jul
04

NH +(mg/l）

NH

+

4

6mg/l
Time
①Opening ofscoring gate

②Reservoir
empty

DO(mg/l)Jul-04

Event
Flood
Flushing
Flushing
Flushing
Flushing
Flood
Flushing
Coordinatedflushing
Coordinatedsluicing
Coordinatedflushing
Coordinatedflushing
Coordinatedflushing
Flood
Coordinatedsluicing

DO（mg/l）Amount ofDashidaira（Minimum value)
sediment
ShimoDashidaira Unazuki
flushing
kurobe－0.7MCM

－
6.0

11.3
9.7
10.3
9.2
7.0
10.5
5.8

－
161,000

3,700
29,4009,470
28,9009,400
6,090
52,1000.59MCM

7.2

11.4

10.2

90,000

2,500

－

11.1

10.6

9.6

29,000

3,700

2,200

0.06MCM

9.5

10.5

9.5

22,000

5,400

2,800

11.8

11.3

9.6

69,00017,00010,000

9.3
10 8
10.8

10.2
11 2
11.2

9.8
10 3
10.3

42,00030 00030,0006,800
12 00012,00011,000
14 00014,000

10.6

11.2

9.6

16,00017,00021,000－1.72MCM
0.8MCM
0.46MCM
0.34MCM

－
8.8
10.7
9.8
8.2

0.09MCM
0.28MCM
－
－

10.5
8.9
9.8
9.3
7.3
9.5
6.5

SS（mg/l）（Maximum value）
ShimoUnazuki
kurobeDashidaira－
103,500
56,80093,20044,7001,800
26,000
6,770
4,330
6,750
5,260
25,7001,500

Manual samplingp g in everyy one hour
Continuous monitoring method for DO and SS is necessary③Side bank erosion
or collapse

Oct-95
Jun-01

Jun-96
Jul-01

Jul-97
Jul-02Jun-98Jun-03

Sep-99Jul-04Stress Index (Newcombe and
MacDonald 1991; Newcombe
and Jensen 1996)‘A (鮎)’,‘Ayu)’ (S(Sweett fifish,h 香魚,銀魚: PlecoglossusPllaltivelis) Typical fish in Japanese rivers

SI＝log e（SS concentration(mg/l)×Duration time (hr)）

SSS concentrration (mgg/l)

1,000,000

SI=11100,000
10,0001,000

Dashidaira
Dashidaira

SI=10

100
0.1

1

10

100

Duration time (hr)Oct-95
Jun-01
Jun
01

Jun-96
Jul-01
Jul
01

Jul-97
Jul-02
Jul02

Jun-98Jun-03Jun
03

Oct-95
Jun-01

Sep-99Jul-04Jul04

Jun-96
Jul-01

Jul-97
Jul-02Jun-98Jun-03

Sep-99Jul-04

100,000

100,000

SI=11

SI=11
10,000

10 00010,000

1,000

1,000

U
Unazukiki
UUnazukiki

Ca. 20cm

Shimokurobe
Shimokurobe

SI=10

100

- 171 -

SI=10

100

0.1

1

10

100

0.1

1

10

100

Based on 1980

Average river bed level

1.00

Dashidaira dam
completion 1985

-1.00
-1.50

Water use

20009

20007

20005

20003

20001

19999

19997

19995

19993

19991

19889

19887

19885

19883

19881

1979

2007

2005

2003

2001

1999

1997

1995

1993

1991

River channel bed is gradually recovering in the river mouth area.

1947
1977

1981

1979

-2.50

Reservoir draw down
Enough flushing discharge water
Rinsing discharge after flushing, etc.

Ave. 3.2-6.2k
Ave. 11.4-13.2k
Ave. 15.8-20.4k
1989

Ave. 0-3.0k
Ave. 6.4-11.2k
Ave. 13.4-15.6k
Ave. of all section

-2.00

1987

Low sedimentconcentration

0.00

1985

Flushing naturally
M h water= MuchFlushing efficiently= Little waterHigh sedimentconcentration

Unazuki dam
completion 2000

Start off DashidairaS
D hid idam flushing 1991

-0.50

1983

Variation ffrom 1980 (m)0.50River mouth

1998

10

Start of Dashidaira
dam flushing 1991

Unazuki dam
completion 2000

1975

2005

1985

2010

D60(mm)Dashidaira dam
completion 1985

100

0－3k
7-11k
14-15k
Ave of all section

1000

4-6k
12-13k
16-20k

Downstream river bed is receiving mainly sandy materials.

Shoreline is gradually recovering after 2000.
Source: MLIT

1) Periodical sediment flushing is important.

7) In order to prevent
accumulationoffine
sedimentonthedownstream gravel bars,
the rinsing discharge fromboth dams is performed
after recovering reservoir
water revel.
8) Evacuation channels havebeen prepared as shelters
for many species of fish,
such as Ayu, during highturbidity in the main stemd toduet sedimentdit flushing.
fl hi

IIn theth Kurobe
K b River,Rib th sedimentbothdit flushing
fl hi andd sluicing
l i i are performedfdapproximately once a year. It reduces flushing sediment volume in one time.

2)) SeSelectingec g su
suitableb e timingg ccan reduce
educe iss important.
po.
Appropriate sediment flushing seasons (June to August), and times when
the natural water flow rate exceeds a certain level, 300 (250 in some specialcase)) m3/s
/ or more forf sedimentdit flushing,
fl hi
and
d 480 m3/s
/ or more forf sedimentditsluicing.

3)) Flushingg efficiencyy at Dashidaira Dam is about 0.01 to 0.03.
4) Discharged SS at the Dashidaira Dam during a sediment flushing
operation was 8,000 mg/l on average and 40,000 mg/l on the peak.
5) Dashidaira Dam is currently at an equilibrium state by passing ofapproximately 1 MCM sediment annually.
6) By
B supplings ppling sediment,
sediment downstream
do nstream riverbed
ri erbed aggradation and bed
armoring as well as coastal erosion have been recovered gradually.

- 172 -

Ock et al. (2013)(1) Early augmentation (1970-80s): simply toconstr ct ‘spa
construct‘spawning
ning riffles’ mechanicall
mechanically(2) Modern augmentation (after 2000s): tosupport geomorphic processes forchannel complexity and substrate quality

Spawning Riffle Construction

Check dam

Replenishment(Augmentation)

Kondolf et al. 201450

Sediment management should be designed with thegoperationsp
combination of flow regime
Sediment supply
manipulation

Flow regime
operation
Peak cut, flushing flow,
environmental flow

Sediment replenishment,
Erosion control andenhancement

Geomorphic measures
Bar types,t pes bar area，B/h,area B/h wetted
etted
perimeter，hydraulic radius，sineoucity，riffle-pool ratio，baserock area, grain size, etc.

Management objects

Habitat measures
Temporary pools, backwaters,
moss mats, litter packs, loose bed,
hyporheic flow, DO concentration,
ab a longevity,o ge y, e
etc.
chabitat

Biodiversity measures

Management
objectives

Species diversity, life forms，
functional feeding groups
groups，trophic
trophic
levels, RDB species, alien species,
etc.

Material cycle measures
Retention time of POM, Filtering
efficiency of POM,
POM Trophic origin oforganic matters,

Sakurai et al. (2013)

Check Dam

Reservoir

Nunome Dam
Location ofHigh-flow
injection(Ock et al.
al 2013)(2) Frequency: 1-3 times a year depending on
natural flood-runoff
Peak duration: 2-4
2 4 hours
Ｈ=0.7ｍ
0.7

2006

2007

2008

(a)

9:30
Q=9.46m3/s

(c)

13：00
Q=5.71m3/s

(b)

Ｂ=30ｍ

Flow disccharge(1) Source of sediment: Upstream end of reservoir

Source: Takemon

2009

11：50
Q=15.36m3/s((3)) Volume of sediment: dependingp
g on release flow ((uptop 500 m3 pper event))
Size of sediment : mixed sand and gravel (0.075-19mm)- 173 -

Ｈ=5ｍ

(d)100m3 のうち約 40m

1) In order to reduce sediment inflow, mainly coarse bed load sedimentis trapped by a low check dam constructed at the end of reservoir
and regularly excavated mechanically.
mechanically. Recently
Recently,, sediment
replenishment has been carried out more than 20 dams in Japan.
Japan.
2) Percentages of sediment replenishment are very limited, ranging
between 0.1 to 10 % of annual reservoir sedimentation since theseprojects
j t are stilltill ini trialt i l stage.
stage
t .
3) Sediment replenishment volume and grain size are recognized as
key factors for a successful management in the river basin to create
and maintain physical habitats, aquatic and riparian ecosystems
ecosystems..
4) Creating new habitats for spawning and other fish life stages are
new challenging topics in Japan.
Japan. Long riffles supplemented by highhigh-flow stockpile could function as an important natural filter for
removing reservoir plankton
plankton, subsequently contributed tomacroinvertebrates’ richness and functional feeding group.
group.

SBT

Kondolf et al. 2014

55

Ikuta river
Dam

Kobe
Designood
Flood

DesignSediment
Si
Size
（mm)
dm/d90Comp
letionCross
section

Diameter
B×H(m)

Length
g

(m)

Slope
（％）

Design
Di h
Disch
arge

Nunobiki
Gohonmatsu

Japan
p

1908

Hood

2.9×2.9

258

1.3

39

7

Asahi

Japan

1998

Hood

3.8×3.8

2,350

2.9

140

12

50/300

13

Miwa

Japan

2004

Horse
shoe

2r = 7.8

4,300

1

300

10

WL only

2～3Matsukawa

Japan

UC

Hood

5.2×5.2

1,417

4

200

15

Velocity(m/s)

Operation（Day／yr）

Koshibu

Japan

UC

Horse
shoe

2r = 7.9

3,982

2

370

9

(5/30)

-

Swiss

1922

Horse
shoe

A =21.0m2

280

3

220

14

250/2700

ca.200Dam: 1900
D
Bypass Tunnel: 1908

Drinking
water supply

Diversion weir

Usually平常時はflow
Rokko Mountains
deep weathered granite, steep slopes into reservoir
貯水池へ流入生田川

A 9 8k 2
A=9.8km2-

Pfaffensprung

Outline of Nunobiki Dam
Purpose:

Osaka

Coun
try

N
Name

Kyoto

H=3m, B=12m
分水堰
トンネル呑口Tunnel inlet

洪水時には
Ｌ＝258mＬ
258m
バイパストンネルへ
バイパストンネルがDiverted water flow into
設置されるまでの旧河道

a bypass tunnel

A=0.47km2

トンネル

Tunnel
吐口 outlet

分水堰地点

Runcahez

Swiss

1961

Horse
shoe

3.8×4.5

572

1.4

110

9

230/500

4

Palagnedra

Swiss

1974

Horse
shoe

2r = 6.2

1,800

2

110

13

74/1602～5Egschi

Swiss

1976

Circler = 2.8

360

2.6

74

10

100/300

10

Rempen

Swiss

1983

Horse
shoe

3.5×3.3

450

4

80

12

60/200

1～5

Solis

Swiss

2012

Horse
shoe

4.4×4.68

973

1.8

170

11

60/1501～10

布引ダム貯水池N
Nunobikibiki
布引ダム貯水池
Reservoir

集水面積
・全体 9.83k㎡
・分水堰より下流0.47k㎡

V 759,521m
V=759
521m3
H=33.3m

Usually spilled water
平常時はflow to余水吐きから下流へ
downstream

Sediment Bypass Tunnel of Nunobiki dam

Diversion weir
H 3 B=12m
H=3m,
B 12

Inlet

Daily sediment inflow in ca.100 yrs can be calculated by(AshidaA hid andd OkOkumura 1977)
1977).
D   A  R  I 
where, D ：sediment yield during a flood event(m3)，event(m3)， A ：catchment area(km2)，
area(km2)， R ：daily
dail rainfall(mm)，rainfall(mm)， I ：aaverage
erage
riverbed slope in 200 m upstream from the calculating point，point，
α , β ：constants.

•

A1=9.83km2 : before a sediment bypass , A2 = 0.47km2 : after abypassR : actual record 1900
1900--2000, I = 0.044 by the geographicalsurveyβ is usually fixed as 2.0 (Ashida(Ashida and Okumura 1977), α isestimated as 6.0 by real accumulated sediment volumes.

OutletTunnel Slope=1/75
Cross Section

I l t
Inlet

•

•

A= 8.0m2

Outlet•1.4

Tunnel length L=258m

85m
1.485mBefore a bypass
completion

Tunnel discharge：Ｑ＝1.11～39m3/s※ InletI l t andd OutletO tl t sectionsti
are covered
d withith natural
t lgranite stones, while other sections have no lining
materials.

2 969
2.969m- 174 -

After a bypass
completion

Mean annual sediment inflow

30,000 m3/yr

1,500 m3/yr

Reservoir life

ca.30 yrs

ca.500 yrs

貯水池
池内堆砂量
量
(m (m3)Sedim
mentatioon volumme)

3,500,000
Measurement
Without bypass tunnelWith bypass tunnel
Reservoir Capacity

33,000,000
2,500,000

Bypass tunnel

Weir

Gradient ：1/34
Heightg：3.8m
Width ：3.8m

Height ：13.5 m
Crest Length：45 m

2,000,000

Fully sedimentation
バイパスがない場合
Bypasswithout a bypass
バイパス
1926年付近で満砂
u etunnel
トンネル設置tunnel in 1926
completion
1908年
1,000,000 in 1908
Reservoir Capacity (V=759×103 m3)
1 500 000
1,500,000

Length : 2,350m
Max. discharge：140m3/s

500 000
500,000
1910

1920

1930

1940

1950

1960

1970

1980

Before a bypass
completion

1990

Outlet2000

After a bypass
completion

Mean annual sediment inflow

30,000 m3/yr

1,500 m3/yr

Reservoir life

ca.30 yrs

ca.500 yrs

Actual sedim
mentation voluume(×10 3m3)

20

Rainfall30
40

Amount of accumulative rainfall
in 5:00 on Sept 4th : 1
1,507
507 mm50
60

W ater leevel

Punping up

Water Level

455
445
435

Generating

425

Dischaarge

Spillway gate open
Spillway gate open

Inflow
Discharge
Discharge with the bypass system

Inflow
Outflow
BypassingMaximum inflow（3th 10:13 pm）
Q=954.04m3/s
Maximum discharge（3th 10:30 pm）
Q=527.79m3/s

3500
3000

Accumulated sedimentation volume (Estimated value)
Accumulated sedimentation volume (Measured value)

500

2500

400

2000

300

1500
(216)

200
100

1000
(83)

(83) (70)

(78)

500
(14) (8)(0)
043 133 123 131 27 79 57 32 -2 10 10 4 25 9 8 14 6 6 9 16 23 51 226
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
(40)

0

(28)

(53)

( )
(15)

(46)

-500
-100
100
500
•AnnualAnnualsediment volume and bypassed sediment in 2011 werethe largest volume since the start of the bypass operation.

•Without bypass systemsystem, about 74% of sediment volume would
have been accumulated in the reservoir. This sediment have
now positive effects on downstream river environment.

8/31 21:00
8/31 23:00
9/1 1:00
9/1 3:00
9/1 5:00
9/1 7:00
9/1 9:00
9/1 11:00
9/1 13:00
9/1 15:00
9/1 17:00
9/1 19:00
9/1 21:00
9/1 23:00
9/2 1:00
9/2 3:00
9/2 5:00
9/2 7:00
9/2 9:00
9/2 11:00
9/2 13:00
9/2 15:00
9/2 17:00
9/2 19:00
9/2 21:00
9/2 23:00
9/3 1:00
9/3 3:00
9/3 5:00
9/3 7:00
9/3 9:00
9/3 11:00
9/3 13:00
9/3 15:00
9/3 17:00
9/3 19:00
9/3 21:00
9/3 23:00
9/4 1:00
9/4 3:00
9/4 5:00
9/4 7:00
9/4 9:00
9/4 11:00
9/4 13:00
9/4 15:00
9/4 17:00
9/4 19:00
9/4 21:00
9/4 23:00
9/5 1:00
9/5 3:00
9/5 5:00
9/5 7:00
9/5 9:00
9/5 11:00
9/5 13:00
9/5 15:00
9/5 17:00
9/5 19:00
9/5 21:00
9/5 23:00

1000
900
800
700
600
500
400
300
200
100
0

Actual sedimentation volume

600

10

465

(440)
Bypassed sediment

9/5 23:00

9/5 21:00

9/5 17:00

9/5 19:00

9/5 15:00

9/5 13:00

9/5 9:00

9/5 11:00

9/5 7:00

9/5 5:00

9/5 3:00

9/4 23:00

9/5 1:00

9/4 21:00

9/4 19:00

9/4 15:00

9/4 17:00

9/4 13:00

9/4 11:00

9/4 9:00

9/4 5:00

9/4 7:00

9/4 3:00

9/4 1:00

9/3 21:00

9/3 23:00

9/3 19:00

9/3 17:00

9/3 15:00

9/3 11:00

9/3 13:00

9/3 9:00

9/3 7:00

9/3 3:00

9/3 5:00

9/3 1:00

9/2 23:00

9/2 21:00

9/2 17:00

9/2 19:00

9/2 15:00

9/2 13:00

9/2 9:00

9/2 11:00

9/2 7:00

9/2 5:00

9/2 3:00

9/1 23:00

9/2 1:00

9/1 21:00

9/1 19:00

9/1 15:00

9/1 17:00

9/1 13:00

9/1 11:00

9/1 9:00

9/1 5:00

9/1 7:00

9/1 3:00

9/1 1:00

8/31 21:00

8/31 23:00

Amoount of rainfall per hour

7000Acccumulated seedimentation vvolume(×103m3)0
1900

R=3.95m,
R
3.95m, A
A=49m
49m2

Pressure fluctuation

Diversion,
Diversion
Gate operation
・・・etcUpstream

L=3,982m, S=2%, Q=370m3/s- 175 -

Sediment
deposition

Abrasion

Tunnel inside

Riverbed geomorphology,
Water quality, Aquatic
environmenti
etc

Downstream

Appropriate selection of reservoir sedimentmanagement strategy
strateg (Japanese Case St
Study)
d )
Draw
down
level

Frequency

NormalImpoundment

○

None

Sediment
Augmentation

○

None

Sediment
Flushing○

Sediment
Sluicing○

Sediment
Bypassing○Dry dam

None

Decommiss‐ioning

None

Sediment passing
Coarse

Fine

None

None

Small

Severaltimes/year

Partially

Partially(Concentrated)(Concentrated)

All(Empty)Once/yr

All

All(Concentrated)(Concentrated)

Half

Severaltimes/yr

Almost all

All(Distributed)

None(Partiallyconcentrated)

Severaltimes/yr

Almost all(Distributed)

Partially(Distributed)

All(Empty)－Almost all(Naturally)All(Naturally)

－

－

All

All

CAP: Original total storage capacity volume, MAR: Mean
Capacity-inflow ratio and Reservoir Lifeannual runoff, MAS: Mean annual sediment inflow
100000

Check dam

segment

1
2
1
1 Mountain

2

2 1
3 2

Subbasin
1

1

Flushing
S i gate
Scoringt

Koshibu

Scoring pipe

Matsukawa

Bypassing

1000⑱

Unazuki
⑥

④

100
⑫ ⑤ ③

10
0.001

Excavating

⑦
⑭
⑨

①
⑬

②
⑧

⑲

⑮
⑪
⑰

⑳

⑩

Dredging⑯Sedimentcheckdam,

Sedimentreplenishment

Sedimentbypass,Sluicing
Sedimentflushing,Sedimentscoringgate
g,
gg

0.01
0.1
1
Capacity-inflow ratio=CAP/MAR

10

1 大井 Oi
D, SP
2 千頭 Senzu
SG
3 泰阜 Yasuoka SG, D, PF
4 笠置 Kasagi
SG, PF5 大間 Ooma
SG, PF
6 常盤 Tokiwa
T ki
SG
7 三浦 MiuraSP
8 平岡 Hiraoka D, PF
9 佐久間 Sakuma D
10 八久和 Hakuwa E, D
11 井川 Ikawa
Ik
SP E
SP,
E, D
D, PF
12 山口 YamaguchiSG, PF
13 秋葉 Akiba
E, D, PF
14 美和 Miwa
B, E, D, CD
15 牧尾 Makio
E, D, CD
16 小渋 Koshibu E,
E CD
17 松川 MatsukawaB, E, CD
18 旭
Asahi
B
19 出し平 DashidairaF
20 宇奈月 Unazuki F
CD ： Check dam
F ： Flushing (Draw down)
PF ： Flushing (Partial draw down
SG ： Scoring gate
SP ： Scoring pipe
B ： Bypassing
E ： Excavating
D ： Dredging• Sediment management is important both for reservoir and river
basin sustainability.
• Sediment supply downstream will be beneficial to improve rivergeomorpholog b
geomorphology
by creating ssuitableitable habitat
habitat, and maintaining
biodiversity and suitable material cycles.
Flushing,, sluicing, bypassing and replenishment (augmentation)• Flushing
are attractive options for reservoir sediment management.
• These options should be appropriately selected based onreservoir size(CAP) vs annual sediment(MAS) and water
inflows(MAR).
• In order to maximize the benefit
benefit, incorporating with flushingflows (magnitude, frequency and timing), determining quantity(amount added) and quality (grain size and source materials) ofcoarse sediment are key factors.

Comprehensive management of floating woody
debris in a river basin
1

Miwa

Sakuma10000

DashidairaQuenching the Thirst:
Sustainable Water Supply
and Climate Change,
George Annandale(2013)

Production of WD
1 1
Basin Headwater
1

N measures(frominnerfactors)
No
(f
i
f t )

Asahi
Reservoir Liffe=CAP/MA
R
AS

NormalImpoundp‐mentClassification of
options

Tropical typhoon Talas, 2011

Inflow, trapping
pp g and outflow
of WD in dams

22
Watershed
Mountain segment2yValleyLake
1Reservoir 3
segment
2
1
Alluvialfun
1
Alluvial
3
1
1
Dam
Tributaryplanesegment
2
4
Grand
water
2 Flood plane
BranchAlluvial fun
segment
channelriver
iDelta segement
Main
Spring4
river
Oxbow 4
Spring-fed river lake Flood plane Delta
Natural leveeRivermouth
Back marsh
Sandy flat
Sandy
beach
(Takemon)900 km72 hours
V=12 5km/h
V=12.5km/h

Sep.5

Sep.4

Sep.3
Sep.2

Hydraulicsyand ecologicalgfunctions of WD in river
channels

Transport and depositing
mechanism of WD in river
channels

Sep.1
Aug.31
Aug.30- 176 -

Rainfall (mm)Aug.30-Sep.6

98 p
persons
death ormissing

Upstreamp
regionsg

Downstream regions

MLIT

Kazaya Dam

MLIT

Debris flow

Land slide dam

Japan Railway bridge

Causes of woody debris production and theirpercentagespg ((Asuwa river 2004))

Bridge Damages by driftwood
Asuwa River, Fukui(2004）

Tamarai River, Oita （2011）

Damages of Railway Bridges in Asuwa River（2004）▲：Damages75((b)) Lateral erosion of mountain stream（32%）(c) Side bank erosion of river
channel （55%）(d) Land slide（4%）

Rainfall Intensity

2013 MAN-YI WIPHA

Accumulated
ccu u ated rainfall,a a , Sep
Sep.14-Sep.16
Sep 6

Large Wood Debris

H=67.4m,
V=66MCM
1997, Japan
Water Agency(a) Longitudinalo g tud a eerosionos o oof mountainou ta
stream（9%）

KatsuraHiyoshi Dam

KameokaDeath toll from typhoontriggered Debris Flow on
Izu Oshima island has
With damreached 25, with 21
people still missing.

Hozubashi Arashiyama
Without dam

Hotsu
River

Kyoto City

C-Band Rader, MLIT
7
7

Fukuchiyama- 177 -

Woody debris trapping at the Hiyoshi dam
upstream of the Katsura River at Typhoon Man-yi,
Man yi 2013
SWL
C t off S
Crest
Seki
ki D
Dam

Catchment area and woody debris volume
Vg（Woody debris volume（m3））

Hiyoshiydam

NWL

100 000
100,000

Miwa Dam1982

Dam Foundation

10,000Hiyoshi Damm

Seki Dam

LWL

Yahagi Dam2000
Nibutani Dam2003

Dashidaira Dam 1995

Flood Season NWL

Palagnedra Dam (Swiss) 1978
Gosho Dam 2013
Hiyoshi Dam
2013

Vg=10A Hiyoshi Dam
A=290km2
裾花・奥裾花ダム1995
3
部子川2004（福井豪雨）
Vg=13,500m①②
Vg=47A③④

Vg=100A

1,000

Seki dam

真名川ダム, 2004(福井豪雨）
⑤
⑥

⑦
⑨
⑪

一乗川2004（福井豪雨）

100

蔵作川2004（福井豪雨）
三ツ俣川2004（福井豪雨）

Gosho Dam
A=635km2
Vg=23,500m3
Vg=37A

⑧
⑩

10
1

10

100

1 000
1,000

10 00010,000

A（Catchment area （km2））

Concept of woody debris management in theriver channel

Three questions for floating woody debris management• WD trapping ratio would change depending on the dam
types ?

• How
H mechanism
h i off
woody debris
transport has beenstudied in the river
channel ?
• Woody debris are
only functioning
negatively in theriver channel? Howabout creating
habitats ?

(Flood control/water use only, with/without of log boom etc.)
Is it possible to WD countermeasures in case of drawdown
sediment
flushing or sediment sluicing ?

• How WD trapping is reducing flood risks below dams?
• Is flood control itself also reducing WD additional
production?
• Discharge and deposition of WD are only functioning
negatively in the river channel or the coastal area?

TP

AP
BH

BT

PC
SC

Habitat classification
After Terada and Takemon

Classification of organic
matterEcological roles of woody debris
1. Trophic sources in river and coastal ecosystems

Woody debris（WD）is the largest POM in river
ecosystemst

2 Provision of habitats for aquatic organisms
2.
as their substrates and/or refuges
3 Creation
3.
C ti off pooll habitatsh bit t forf aquaticti organismsi
4. Conditioning of habitats by trapping organic
matter such as CPOM and FPOM

Woody debris is required to be installed inrivers under human impacts

Subjects to be investigated
Location of woody debris suitable forpromoting ecological functions

After Yoshimura et al.
（2006）- 178 -

After Terada and Takemon

Exercise 2: Fundamentals of Data Processing
Toshio HAMAGUCHI (Disaster Prevention Research Institute, Kyoto University)
This lecture aims that you get the fundamental knowledge of data processing using the FORTRAN
language. The FORTRAN language is a useful and powerful tool for scientifically numerical calculations.
First, this lecture starts with the introduction of digital data and historical transition of the interpreters of themachine language (the computer mother language). Second, the installation process of a certain publicdomain software of FORTRAN compiling system in Windows 7/8/8.1 is visually shown. Third, some
practical exercises are conducted via a output command, a looping operation one, a conditional one, adimension one and a data arrangement. Fourth and last, some assignment exercises and their suggested
answers are given. Through this lecture, the preparation for subsequent exercise lecturesis completed. In
other words, a GCM/historical data analysis, a flood frequency analysis, a flood simulation with rainfallrunoff-inundation model, reservoir operation’s optimization are applicable based on this lecture’s
knowledge. It can be helpfully performed in any computational cases.

- 181 -

What is digital data?

UNESCO IHP Training Course
Exercise 2

Digital data in Engineering fields:
serially discrete digitals during observation,scattered statistics in space or time,
random numbers adjoining true numbers,numerical results of scientific calculation with a machine language,
 t
etc

Fundamentals of Data Processing

Toshio Hamaguchi
Disaster Prevention Research Institute,
Institute
Kyoto University

We need to deal with digital data on the screen/paper tofind out some features in statistics or in numericalanalysis and to grasp/interpret scientific implicitness
from the features.
featuresInterpreter of the machine language (binary code)

FORTRAN language
Deveolped by John Warner Backus in 1954.

Binary codes; 0 or 1 / off- or on- electric signal

Main purpose : use in scientific digitals

Hexadecimal codes; 0, 1, 2, … , 9, A, B, …, F, 10, 11, …

Formulation change process:It differs from decimal codes; 0,1, 2, … , 9, 10, 11, …

FORTRAN(1957)→FORTRAN II(1958)→FORTRAN III(1958)

Character code: defined with 2-orderd hexadecimal codes→FORTRAN IV(1961)→FORTRAN66→FORTRAN77

BASIC developed by Bill Gates; IF THEN,
THEN PUT,
PUT PRINT
PRINT, GOTO
GOTO,
NEXT, RUN, LOCATE, DATA, …

→FORTRAN90→ FORTRAN95→ FORTRAN2003→FORTRAN2008→?
FORTRAN2008 ?

Others: FORTRAN, C, Pascal, perl, Ruby, JAVA, csh, bash…

We use it herein!

Preliminary work before FORTRAN installation

Fundamentals

Preliminary work before FORTRAN installation (Cont.)

Change the filename view setting because you’ll get
confused execution file with execution one after compiling
FORTRAN source codes if you cannot view filename
extension.
In case of Windows7

Cli k “Appearance
Click
“A
and
d
Personalization” in Control Panel.

Click the box of “Hide
Hide extension for known file types
types” totake off the check mark.

Click “Folder Options.”- 183 -

Preliminary work before FORTRAN installation (Cont.)

How to install gfortran system (1)In case of Windows8/8.1

Please browse the following webpage :http://tdm-gcc.tdragon.netpgg
and download the installation package of TDM-GCC.

Run Explorer in a certain folder
and click “view” Tab.

Click the box of“File nameextensions” tocheck the markin it.

for 32bit OS
for 64bit OS

How to install gfortran system (2)How to install gfortran system (3)Run the downloaded execution file,g window.
and click the [[Create]] button in the following

Choose and click the radio button according your OS,
and click the [Next] button in the bottom
bottom.

for 32bit OS

Click herefor 64bit OS

Click here after
the above choice.

How to install gfortran system (4)How to install gfortran system (5)Ex. in case of 64bit OS, input the folder name to be installed.

Choose the download site.
((Default one would be better.))Ch
Chooseth
the DL siteitfrom the candidates.

Input the folder name.

Click here after
the above input.

Click here after
the above choice.

- 184 -

How to install gfortran system (6)How to install gfortran system (7)Click the plus mark in front of “gcc” to selectthe compilingp g languages
g g to be installed.

Additionally click the mark in the box of “fortran.”((Preferred languages
g g can be installed at the same time.))

Click this box of“fortran”.

Click the “+” markin front of “gcc”.

Click here after theabove check(s).

How to install gfortran system (8)How to install gfortran system (9)Wait for finishing the download and installation process.

When you get to the following window, you can successfully
finish the installation of “TDM-GCC.”

Click here.

How to install gfortran system (10)How to examine the feasibility of gfortran system (1)Open the command prompt window of “TDM-GCC”
your screen.
in “START” menu on yThis is the last window of installation.

Click here.

- 185 -

How to examine the feasibility of gfortran system (2)Exercise 1: output command

Input “gfortran -v” on the prompt. If you get the following
outputspon the screen,, you’llysuccessfullyy install it.

1. Start the text-editor such as “Notepad.”
2 you try to open a new file and input the2.
following 4-line codes in it:
write(*,*)( ) ’Hello,’
UNESCOSCO IHP TC’
C’
write(6,*) ’I am ???’
stop
end

Please input yourpart.
name in “???” p

where “write(n,m)”( ) : “n” is the number off outputtedplace. “m” is the number of prescribed format.
3. After saving it as “test.f90” in the current folder,
enter the command of “gfortrangfortran test.f90 (enter)”(enter)on the command line.

Exercise 1: output command (Cont.)

Exercise 1: output command (Cont.)

Edit the test.f90 as follows:

So that, you can get the execution file “a.exe”
and run a(.exe).
a( exe)write(*,*) ’Hello, UNESCO IHP TC’
write(16,*)(16 ) ’I
’ am ???’
stopend

If successful, you can display the following
outputt t on th
the command
d line.
li

Recompile “test.f90” (gfortran test.f90) and run it.
You can findf1-line output

Hello, UNESCO IHP TC
I am ???

Hello, UNESCO IHP TC

What is “write(6,*)”,
different from comparedpwith“write(*,*)” ?  (Ans.) No difference“*”* : used as a wild card (Default)(Default). Default of
outputted place and format are given as “onthe display (prompt line)line)” and “proper
proper format.
format.”So that, “write(*,*)” means “write(6,*)”and moreover the new file “fort.16” in the current
folder byy inputtingpg the command “dir /w”.
After that, input the command “type fort.16”
I am ???

to display the contents of “fort.16”

Exercise 1: output command (Cont.)

Exercise 1: output command (Cont.)

Re-edit the test.f90 as follows:

Edit the test2.f90 as follows:

write(*,*)
write(**) ’Hello
Hello, UNESCO IHP TC
TC’open(16,file=“out16.dat”)
write(16,*) ’I am ???’
stop
end

write(*,*) ’Input the integer number’read(*,*) I
write(*,*) I+3
stop
t
end

Compile “test2
test2.f90
f90” (gfortran test2
test2.f90)f90) and run itit.
If successful, you will find the following outputs
on the screen.
screenRecompile “test.f90” (gfortran test.f90) and run
it again
again.
You will find the new file “out16.dat” in thecurrent folder.

Input the integer number
58- 186 -

Exercise 2: Looping operation command

Exercise 2: Looping operation command (Cont.)

Default of the REAL and INTEGER numbers- INTEGER type: Number starting with I, J, K, L, M and N- REAL type (number with floating point): Number startingwith the rest of the other letters (A, B, …, H, O, …, Z)

Q1 Program
Q1.
P
the
h iinteger summationi codesd iin any ranges.

The following codes are the summation from 1 to 10.
write(*,*) ‘Summation of integer numbers’
J=0
do I=1,10“Do” loop concerning “I”
J J+I
J=J+I
end dowrite(* *) J
write(*,*)
stop
end

Q2. Program the output codes of an arithmetic sequence “3n-1”
where n: natural numbers.

Exercise 3: Conditional command

Exercise 3: Conditional command (Cont.)

The below codes are to calculate the integersummation from 25 to 75 in the range of 1 to 100.

The below codes are to calculate the integersummation from 25 to 75 in the range of 1 to 100.

write(*,*) ’IF-code test’jj=0
do i=1,100
if (i>=25 .and. i<=75) j=j+i
end dowrite(*,*) j
stop
end

write(*,*) ’IF-code test’jj=0
do i=1,100
if (i>=25 .and. i<=75) thenj=j+i
end ifend dowrite(*,*) j
stopp
end

Exercise 3: Conditional command (Cont.)

Exercise 3: Conditional command (Cont.)

The below codes are the same as previous one
without “integer do-loop” (using “case”).

The below
Th
b l
codes
d are theth same as previousione
without “integer do-loop” (using if-else-then codes).
write(*,*)
i ( ) ’IF-code’IFd test’
’
i=1
j=0
do
if (i>=25 .and. i<=75) thenj=j+ielse if (i==100) thenexiti
end ifi i+1
i=i+1
end dowrite(*,*) j
stop
end

write(*,*) ’IF-code test’
i=1
j=0
do
select case(i)case(100)exit
case(:24)
write(*,*) ’No calc. (n<25)’
case(25:75)
(
)
j=j+i
write(* *) ’Calc
write(*,*)
Calc.’- 187 -

case default
write(*,*) ’No calc. (n>75)’
end selecti=i+1
end dowrite(*,*) j
stopp
end

Exercise 4: Dimension command

Exercise 4: Dimension command (Cont.)

The below codes are to calculate the matrix
production using the Dimension command.

When the input data are prescribed, the below
codes are available.

real, dimension(3,3) :: a=0.0
do i=1,3
real dimension(3) :: b=0.0,c=0.0
real,
b=0 0 c=0 0
do j=1,3
write(*,*) ’Dimension-code test’ c(i)=c(i)+a(i,j)*b(j)do i=1,3
end dodo j=1,3
end dowrite(*,*) ’a(’,i, ’,’,j, ’)=’
write(*,*) ‘c:’,(c(i),i=1,3)
read(*,*)d( ) a(i,
(i j)
stop
end doend
write(* *) ’b(’
write(*,*)
b( ,i,i ’)=’)
1.0  1.0 2.0   1.0   5.0 read(*,*) b(i)

 

 4.0
1.0  2.0   2.0    0.0 end do

real, dimension(3,3)::a=reshape((/1.,4.,-3.,-1.,1.,2.,2.,-2.,1./),(/3,3/))real, dimension(3)::b=(/1., 2., 3./),c=0.0
write(*,*) ’Dimension-code test’
do i=1,3
d jj=1,3do
13c(i)=c(i)+a(i,j)*b(j)end doend do 1.0 1.0 2.0   1.0   5.0    
write(*,*) ’c:’,(c(i),i=1,3)
 4.0 1.0 2.0   2.0    0.0 stop    3.0 2.0 1.0   3.0   4.0 
end

Exercise 5: Data arrangement

Exercise 5: Data arrangement (Cont.)  3.0

2.0

1.0   3.0  4.0 The below codes are to arrange given data fromdisordered one to elder one.
real*8,dimension(21):: adata a/-0.9,0.8,0.3,-0.5,-0.8,0.2,&-0.4,0.6,-1.0,0.9,-0.7,1.0,0.0,0.5,&0406 1009 07100005&-0.6,-0.3,0.7,-0.1,0.4,-0.2,0.1/
write(* *) 'data
write(*,*)
data arrangement test
test'
b=0.0
do i=1,20b=a(i)
mx=i
d j=i+1
do
j=i+1,2121
if (a(j)>b) thenbb=a(j)a(j)mx=jQ3 Program
Q3.
P
the
h ddata arrangement codesd iin younger order.
d

end ifend doif (mx(/ i) then/=tha(mx)=a(i)a(i)=bba(i)end ifend dodo i=1,21
write(*,'(A,I2,A,F6.3)') 'Rank',i,':' (i)',a(i)
end dostop
end

Q4. Program the data arrangement codes to find its median andto calculate its average.

Th k you!!
Thank- 188 -Exercise 3: Data Analysis of GCM and Historical Data
Kenji TANAKA (Water Resources Research Center, DPRI, Kyoto University)
Global/Regional climate model (GCM/RCM) might be indispensable on predicting detailed
climate change. Although GCMs/RCMs have accomplished remarkable development in recent years,
the requirement from user side on the resolution and the accuracy has been increasing more andmore. Thus, there is still a gap between precision realized by models and that required from users.
This exercise aims to introduce the method for detecting and correcting the bias informationof GCMs/RCMs. A new bias correction method that can reproduce the extreme values while keeping
the monthly mean value was developed. This system is designed to be general so that it can easily fit
the difference of grid coordinate or change in model setting.
From the location of observation station and model grids, corresponding model grid isdecided for each station where historical data are available. When there are more than two
observation stations for one grid, average of these available data is used for evaluation.
The results of monthly mean bias show that the model bias varies greatly in time and space.
Many cases can be found from the results that even though the model bias is small enough inmonthly mean value, the shape of frequency distribution might be so different.
There are many river basins in the world where historical meteorological data are not enoughor very limited. So, the information of global precipitation dataset such as GPCC(Global
Precipitation Climatology Center) and GSMaP (Global Satellite Mapping of Precipitation) is
introduced.

- 191 -

25th IHP Training Course(2015/12/4)

Back ground
Recently,y, a sense of impendingpg crisis for the gglobalwarming came to be realized.
Social needs for the concrete description of futureclimateli t hhas bbeen iincreasing.
i
Although RCMs have accomplished remarkabledevelopment in recent years, the requirementfrom user side on the resolution and the accuracyy
has been increasing more and more for detailed
assessment of climate change impact.

Kenji Tanaka
Water Resources Research Center,
DPRI, Kyoto Universityttanaka.kenji.6u@kyoto-u.ac.jp
k k ji 6 @k tjThus, there is still a gap between precision realizedby models and that required from users.
users

Effect of bias correction on the climate changei
impactt assessmentt (from(f
ICCAP)
No correction

Bias corrected

E
Evap

E
Evap

Before bias correction
Unit(mm)Present
Warm-upWarm
up
Diff(W-P)

Prec
1189.6897 0
897.0-292.6

Evap
456.8
481 1
481.1
+24.3

Runoff
804.3
503 1
503.1
-301.2

Irrig
61.4
73 4
73.4
+12.0

After bias correction
Unit(mm)Present
Warm-upDiff(W-P)
ff()

Result of climate change signal can be opposite when raw
RCM output has large bias,
bias suggesting the importance ofbias correction for climate change impact assessment.

 BiasBi evaluationl i ffor eachh prefecturef/ riveri bbasin
i unit
i

1.

Prec
634 0
634.0
464.3
-169.7

Evap
411 3
411.3
373.9
-37.4

Runoff
281 5
281.5
168.9
-112.6

Irrig
53 8
53.8
69.7
+15.9

Creationbasin
C
i off riverib i maskk andd administration
d i i
i
(
)
mask (basmask1.f)

 Not
N t onlyl mean value,
l butb t also
l frequencyfdistribution
di t ib ti
2.
 Designed
D i
d to
t be
b generall so th
thatt it can easily
il fit th
theFinding corresponding grid for each observation
station (stnmatch3.f)

difference of grid coordinate or change in model
settingtti
 ItemsIttto b
be evaluatedl t d (forcing(f i variables
i bl for
f L
Land
d

3.

R
Re-orderingd i model
d l output
t t (readmodel.f)( d d l f)

4.

Detection of model mean bias,, calculatingg
frequency distribution (biaspdfvar3.f)

Surface Hydrological model)
PPrec,
T i Eair,
Tair,
E i SWdown,
SWd
LWd
LWdown,Wi d Psfc
Wind,
P f

5.

- 193 -

Bias correction considering frequency distribution

Originalg
Watershed code

River basin mask and prefecture mask
expressed by MRI-NHM grids

Combined Watershedeachh modeld l grid
id iiscategorized into
each river basin.
basin

each
h modeld l grid
id iiscategorized into
administrative unit.
unit

Findingg corresponding
p
gg
grid
1.

Creationbasin
C
i off riverib i maskk andd administration
d i i
i

grid

(
)
mask (basmask1.f)
2.

Finding corresponding grid for each observation
station (stnmatch3.f)

grid point

3.

R
Re-orderingd i model
d l output
t t (readmodel.f)( d d l f)

4.

Detection of model mean bias,, calculatingg
AMeDAS
stationfrequency distribution (biaspdfvar3.f)5.

Bias correction considering frequency distribution

Available 20km model output
Organization
MRI
NIED
Tsukuba Univ.
Tsukuba Univ.
Univ
MRI

model version name (fmod)
NHM
Ver2
NHMMRI2RAMS
Ver1RAMSDP1RAMS
Ver1RAMSTU1
WRF
Ver1
WRF-TU1
WRF
TU1
GCM
GCM20kmperiod20yr (1985-2004)
29yr (1979-2007)
20yr (1985-2004)
20yr (1985(1985-2004)
2004)
25yr (1979-2003)

Super
p high-resolutiong
GCM（Global: AGCM）

General Circulation Model（Global: AOGCM）

Atmosphere
A GCM
A-GCM
SST

mx
105
131
130
128
130
130
129
163

my
115
121
140
144
140
140
139
162

mr
60
61
62
62
62
62
56
67

mp
60
60
60
60
60
60
60
60

im
54
49
57
57
57
57
47
69

iamax
1076
1047
1094
1094
1094
1094
958
1275

Ocean
O-GCM

Boundary
Condition

SST

name
NHMMRI1
NHMMRI2
RAMSDP0
RAMSDP1
RAMSTU0RAMSTU1
WRF-TU1
GCM20km

MRI-TL959(1920ｘ960ｘ60)
H i
Horizontalt l resolution:l ti
20 kkm
Vertical: 60 layers
IPCC AR4A1B Scenario

1979-2003 2015-2039 2075-2099- 194 -

Diff
Difference(ratio)( ti ) b
betweent
End of 21st C and present

Model bias of 7 surface meteorologicalelements

Bias detection/correction for each prefecture unit
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

Souya
Kamikawa
Rumoi
Ishikari
Sorachi
Shiribeshi
Abashiri
NemuroKushiroTokachi
Iburi
HidakaOshimaHiyamaAomori
Iwate
Miyagi
Akita
Yamagata
F k hi
Fukushima
IbarakiTochigi
Gunma
Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa

宗谷支庁
上川支庁
留萌支庁
石狩支庁
空知支庁
後志支庁
網走支庁
根室支庁
釧路支庁
十勝支庁
胆振支庁
日高支庁
渡島支庁
檜山支庁
青森
岩手
宮城
秋田
山形
福島
茨城
栃木
群馬
埼玉
千葉
東京
神奈川
新潟
富山
石川

31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60

Fukui
Yamanashi
NaganoGifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama
Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
K
KagawaEhime
Kochi
Fukuoka
Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

福井
山梨
長野
岐阜
静岡
愛知
三重
滋賀
京都
大阪
兵庫
奈良
和歌山
鳥取
島根
岡山
広島
山口
徳島
香川
愛媛
高知
福岡
佐賀
長崎
熊本
大分
宮崎
鹿児島
沖縄

Observation:
SDP, AMeDAS
X: month
Y: prefecture

Bias correction system was developedin S-5-3 project (MOE fund)

Prec

Tair

- 195 -

Bias Corrected / raw GCM
Near future / Present
End of 21st C / Present every 1hr data are classified for each monthbias corrected amount iscomparable or even larger
than climate change signal

8 class (Prec) / 10 class (Tair) 2 steps correction

Temporall correction
Ttiwith fixed ratio

Monthly mean value

Adjustment ofcorrection factor
for each class

Histogram (NHMMRI2, Prec, July)

fixed ratio and variable ratio

Count at each class ( Aomori
10

provisional
optimalti l ratiotivariableratio

Frequency distribution

Count at each class ( Miyagi

103

103

10

2

10

101

101

101

Aomori

0

0

5

10

10

15

20

25

0

30

Miyagi
0

5

10

10

Prec(mm/hr)
Count at each class ( Niigata
10

4

5

6

7

Count at each class ( Miyagi

)

)

10

4

100
15

20

25

10

NHMMRI2
Correct1
Correct2
obs(08)

Aomori
90000
80000

NHMMRI2
Correct1
Correct2
obs(08)

80000

3

10

102

1

70000

70000

18000
16000

15
20
Prec(mm/hr)

100
15

20

25

25

30

30

30

)

3

10

3

102

1

10

FukuokaPrec, 1hr, 0.900, July

5

10

25

15

20

25

30

30

NHMMRI2C
Correct1
t1
Correct2
obs(07)

Kagoshima0Prec, 1hr, 0.990, July

5

15
20
Prec(mm/hr)

25

30

99.9 Percentile Prec (07p1991-2004)
70

NHMMRI2
Correct1
Correct2

10Prec, 1hr, 0.999, July

99.0 Percentile Prec (07p1991-2004)

30

10

1

100

15
20
Prec(mm/hr)

5

Prec(mm/hr)
Count at each class ( Kagoshima)

102

0

Osaka
0

NHMMRI2C
Correct1
t1
Correct2
obs(07)90.0 Percentile Prec (07p1991-2004)
NHMMRI2
Correct1
Correct2

25

NHMMRI2
Correct1
Correct2
obs(07)

)
4

100

10

10

10

10

Kouchi55Prec(mm/hr)
Count at each class ( Fukuoka
4

12

)

NHMMRI2
Correct1
Correct2
obs(08)

Tokyo

0

10

20000

Miyagi

30

20

101

Shizuoka)

NHMMRI2C
Correct1
t1
Correct2
obs(07)100

Count at each class ( Tokyo

90000

10

102

Niigata15
Prec(mm/hr)

101

Histogram (NHMMRI2, Tair, August)Count at each class ( Aomori

10

10

4

101

10

5

Count at each class ( Osaka

NHMMRI2
Correct1
Correct2
obs(07)

4

103

5

Tokyo
0

102

0

100000

0

30

102

10

8

25

Count at each class ( Shizuoka )103

Prec(mm/hr)
Count at each class ( Kochi

3

15
20
Prec(mm/hr))

NHMMRI2
Correct1
Correct2
obs(07)

2

103

0

2

)

NHMMRI2
Correct1
Correct2
obs(07)

4100

1Count at each class ( Tokyo
104

2

10

class

)

NHMMRI2
Correct1
Correct2
obs(07)

104

103

10

fixed
ratio

)

NHMMRI2
Correct1
Correct2
obs(07)

4NHMMRI2
Correct1
Correct2

60

10

25

8

20

40

6

15

30

4

10

50

14000

60000

60000

12000
50000

50000

10000
40000

40000

8000
30000

30000

6000

20000

20000

4000

10000

10000

2000

0

0

0

285

290
295
300
Tair (K)Count at each class ( Niigata )

285

305

160000

290
295
300
305
Tair (K)Count at each class ( Shizuoka )290

120000NHMMRI2
Correct1
Correct2
obs(08)

Niigata
140000120000295
300Tair (K)Count at each class ( Osaka305

NHMMRI2:
2)

100000NHMMRI2
Correct1
Correct2
obs(08)

Osaka
16000
14000

80000

40000

2000
0

100000295
300
305
Tair (K)Count at each class ( Fukuoka )

304

310

295
300
305
Tair (K)Count at each class ( Kagoshima)

10

12

F k k
Fukuoka
70000
60000

300
(K)

305

CC= 0.533

RMSE=10.85

Correct1: CC= 0.263
0RMSE=14.98

Correct2: CC= 0.610

15

20

25

30

10

20

30

40

RMSE= 9.78
50

60

70

Tair, 1hr, 0.990, Aug

Tair, 1hr, 0.999, Aug

99.0 Percentile Tair (08p1991-2004)

308

NHMMRI2
Correct1
C
t1
Correct2

99.9 Percentile Tair (08p1991-2004)
312

306

300

304

298

302

NHMMRI2
Correct1
C
t1
Correct2

308

NHMMRI2C
Correct1
t1
Correct2
obs(08)K
Kagoshimahi140000120000

50000

100000

306

40000

80000

30000

60000

20000

40000

10000

20000

0

0

294

20000

Tair

NHMMRI2:

10160000NHMMRI2C
Correct1
t1
Correct2
obs(08)

290

292

294

296

298

Tair

300

(K)

302

304

306

308

290

292

294

296

298

Tair

300

302

304

306

CC= 0.965

RMSE= 1.19

Correct1: CC= 0.990

RMSE= 1.06

Correct2: CC= 0.990

RMSE= 0.45298

CC= 0.917

RMSE= 1.52

Correct1: CC= 0.968

RMSE= 1.51

Correct2: CC= 0.972

RMSE= 0.54

NHMMRI2:
300

296
296

- 196 -

302
NHMMRI2:

292

308

(K)

304

300
NHMMRI2:

295

RMSE= 3.40

310
NHMMRI2
Correct1
C
t1
Correct2

296

290

Correct2: CC= 0.744
10

302

40000

0

RMSE= 8.21

310
290

80000
60000

RMSE= 6.40

0
290

)
80000

K
Kouchihi

8Tair, 1hr, 0.900, Aug

4000
20000

20000

NHMMRI2C
Correct1
t1
Correct2
obs(08)

6

306

6000

40000

120000

CC= 0.231

Correct1: CC= 0.049

090.0 Percentile Tair (08p1991-2004)

8000
60000

305

RMSE= 1.78

4

10000

290
295
300
Tair (K)Count at each class ( Kochi

Correct2: CC= 0.438

20
NHMMRI2:

5

0

60000

285

RMSE= 2.58

12000

1000000

RMSE= 2.00

18000
NHMMRI2
Correct1
Correct2
obs(08)

Shizuoka

80000

CC= 0.316

Correct1: CC= 0.087

298

300

302

304

306

CC= 0.840

RMSE= 1.75

Correct1: CC= 0.920

RMSE= 1.77

Correct2: CC= 0.921

RMSE= 0.79

298
300

302

304

306

308

310

302

304

306

308

310

312

Programg
and Data list
Prec, 1hr
CC (0.900)

Prec, 1hr
CC (0.990)

Prec, 1hr
CC (0.999)

Prec, 1day
CC (0(0.900)900)

Prec, 1day
CC (0(0.990)990)

Prec, 1day
CC (0(0.999)999)

Prec, 10day
CC (0.900)

Prec, 10day
CC (0.990)

Prec, 10day
CC (0.999)

GSI/basmask1.f
basinareaMODEL.txt
prefecareaMODEL txt
prefecareaMODEL.txt
gridriverMODEL.gad
gridprefecMODEL.gad
table-riverMODEL txt
table-riverMODEL.txt
table-prefec.txt
AMEDAS/amedas1hrYYYY.gad
addramd txt
addramd.txt
SDP/sdp1hrYYYY.gad
addrsdp.txt
Work/MODEL/stnmatch3.f
readmodel.fbiaspdfvar3.f
PARAMV.txt (V=P,T,S,…)()
VDIM3.txt
orgMODEL.txt
adpame/VARMODELYYYYA.gad (VAR=Prec, Tair, swdn,…A=r,p)corect/gnuVAR/weightMMA.txt (MM=01～12, A=r,p)biaspdfMM-NNA.txt (MM=01～12, NN=01-60, A=r,p)Available surface meteorological observation

In-situ measurement
i nott enough?
ish?

•SDP (meteorological observatory) 155stations
items: Psfc, Psea, Tair, Eair, RH, Wdir, Wind,
cloud, weather, Tdew, SShr, SWdown, Prec
•AMeDASAM DAS (Automatic(A tti weather
th station)t ti ) ～1600stations
1600 t ti
items: Prec, Tair, SShr, Wdir, Wind
47

47

SDP

AMeDAS

系列1

系列1

42

42

37

37

32

32

27

27

Let’s
Let
s use global products!
It’ free!
It’s
f !

22

22
122

127

132

137

142

147

122

127

132

137

142

147

GPCC (Global Precipitation Climatology Center)

Download page for Monitoring Product

ftp://ftp dwd de/pub/data/gpcc/html/Gate to the GPCC Products html
ftp://ftp.dwd.de/pub/data/gpcc/html/Gate_to_the_GPCC_Products.html

Download p
pageg for Full Data Reanalysisy- 197 -(http://sharaku.eorc.jaxa.jp/GSMaP/)

Procedure of data analysisy
Step0: unpack the data (gzipped file )p select the regiong
for analysisy((GSMaP))
Step1:(GSMAP/daily/cutgsmap.f)
Step2: select the region for analysis (GPCC)(GSMAP/d il /(GSMAP/daily/cutgpcc.f)f)
Step3: calculate climatological value and correction factor(GSMAP/daily/climanal f)(GSMAP/daily/climanal.f)
Step4: select the region for analysis (gsmap hourly)((GSMAP/hourly/cutgsmap.f)yg
p )Step5: change resolution to fit to the model grid
(GSMAP/hourly/chgres.f)
St 6 bias
Step6:
bi correction
ti off GSMaP
GSM P byb GPCC climatology
li t l(GSMAP/hourly/bcgsmap.f)
Step7: re-ordering the data for HydroBEAM (2D  1D)(GSMAP/hourly/hbinput.f)

You can access to the GSMaP products after registration.

- 198 -

Step1: select the region for analysis (GSMaP)(GS(GSMAP/daily/cutgsmap.f)//
f)

Step2: select the region for analysis (GPCC)(GS(GSMAP/daily/cutgpcc.f)//
f)parameter(IDIM=3600, JDIM=1200)
parameter(latN=35.,latS=-5.,lonW=20.,lonE=45.)

parameter(IDIM=360, JDIM=180)
parameter(latN=35.,latS=-5.,lonW=20.,lonE=45.)

j=1

i=1

i1=int(lonW/0.1)+1
i2=int(lonE/0.1)
j1 i t((60 l tN)/0 1) 1
j1=int((60.-latN)/0.1)+1
j2=int((60.-latS)/0.1)
mx=i2-i1+1
my=j2 j1+1
my=j2-j1+1
Size of target area j=JDIME00
Is calculated automatically

Set the analysis region

i=IDIM
j=j1

i=i1

i=i2

mx
my

j=j2
lonW

N60

latN

latS
lonE

do iy=2000,2010
loop for year
write(cy '(i4write(cy,(i4.4)4)')) iy write the year information in cy
open(22,file='NILE/GSMAP-Nile-1mon'//cy//'.gad',form='unformatted',access='direct',recl=mx*my*4)

i=IDIM
j=j1

i1=int(lonW+180.)+1
i2=int(lonE+180.)
j1 i t(l tS 90 ) 1
j1=int(latS+90.)+1
j2=int(latN+90.)mx=i2-i1+1
my=j2 j1+1
my=j2-j1+1

E360

Set the analysis region

i=1
j=JDIM
j=j2

i=i1

i=i2

mx
my

j=j2
j=j1
lonW

latS
lonE

j=1
Size of target area
W180
Is calculated automatically

S60

Use cy (year information) in
file name automaticallyComparison of monthly mean precipitation

Step3: calculate climatological value and correction factor(GSMAP/daily/climanal f)(GSMAP/daily/climanal.f)

Correction
factordo ii=1,mx
loop
p for 1 degreegdata
do jj=1,myjj=1 myjj
i1=(ii-1)*10+1
i2=ii*10
1 degree grid box
j1=(jj-1)*10+1
j1(jj 1) 10+1
j2
j2=jj*10bias(ii,jj,imon)=1.
avevar(ii,jj)=0.
avevar(ii,jj)
0.
j1
I iti li ti ffor averaging
Initializationi
icon2=0i1
i2
do i=i1,i2
loop for 0.1 degree data
do jj=j1,j2
j ,jif(clim2(i,j,imon).ge.0.) thenavevar(ii,jj)=avevar(ii,jj)+clim2(i,j,imon)
icon2=icon2+1
endif
ii
enddo
enddo
avevar(ii,jj)=avevar(ii,jj)/real(icon2) calculate 1 degree average value
if(avevar(ii,jj).ge.1..and.clim1(ii,jj,imon).ge.1.)bias(ii,jj,imon)=clim1(ii,jj,imon)/avevar(ii,jj) Correction factor (GPCC/GSMaP)

Step5: change resolution to fit to the model grid
(GS
(GSMAP/hourly/chgres.f)//f)parameter(mx1=250, my1=400)
parameter(mx2=150 my2=240)
parameter(mx2=150,

GSMaP gridmodel griddo ii=1,mx2
data matching
im(ii)=nint((xlon2(ii)-lonW-0 5*res1)/res1)+1
im(ii)=nint((xlon2(ii)-lonW-0.5
res1)/res1)+1
enddo
do jj=1,my2
jm(jj)=nint((xlat2(jj)-latS-0
jm(jj)
nint((xlat2(jj) latS 0.5
5*res1)/res1)+1
res1)/res1)+1
enddo

S90

E180

do iy=1986,2011
loop for year
write(cy '(i4write(cy,(i4.4)4)')) iy write the year information in cy
open(22,file='NILE/GPCC-Nile-1mon'//cy//'.gad',form='unformatted',access='direct',recl=mx*my*4)

Use cy (year information) in
file name automatically

N90

latN

Step6: bias correction of GSMaP by GPCC climatology(GS(GSMAP/hourly/bcgsmap.f)//f)

3 (im,jm+1)(im,jm 1) 4 (im+1,jm+1)(im 1,jm 1)

parameter(mx1=25, my1=40)
parameter(mx1=25
parameter(mx2=150, my2=240)

model gridcorrection factor (1 degree)model grid (1/6 degree)

do imon=1,12
read(11,rec=imon) ((bias(i,j,imon),i=1,mx1),j=1,my1)
do ii=1
1,mx1
mx1
loop for 1 degree data
do j=1,my1
i1=(i-1)*iave+1ji2=i*iave
j1=(j-1)*iave+1
j2=j*iave
p for model g
grid
loop
do ii=i1,i2(1/6 degree data)
do jj=j1,j2
bias2(ii,jj,imon)=bias(i,j,imon)
if(bias2(ii,jj,imon).gt.5.0) bias2(ii,jj,imon)=5.0
if(bias2(ii,jj,imon).lt.0.2) bias2(ii,jj,imon)=0.2

1 (im,jm) 2 (im+1,jm)

GSMaP ggrid
dis1=(xlon2(ii)-xlon1(im(ii)))**2+(xlat2(jj)-xlat1(jm(jj)))**2(( )( ( )))((jj)(j (jj)))dis2=(xlon2(ii)-xlon1(im(ii)+1))**2+(xlat2(jj)-xlat1(jm(jj)))**2
distance x distance
dis3=(xlon2(ii)-xlon1(im(ii)))**2+(xlat2(jj)-xlat1(jm(jj)+1))**2
dis4=(xlon2(ii)-xlon1(im(ii)+1))**2+(xlat2(jj)(( )( ( ) ))(
(jj)
xlat1(jm(jj)+1))**2
www=1./dis1+1./dis2+1./dis3+1./dis4
weight(ii,jj,1)=1./(dis1*www)weightg for interpolationpweight(ii,jj,2)=1./(dis2*www)
Total weight should be 1
weight(ii,jj,3)=1./(dis3*www)weight(ii,jj,4)=1./(dis4*www)- 199 -

reading correction factor

1 degree grid box

j2
jj1

i1

i2
i

GrADSThe Grid Analysis
y and Display
p y System
y((GrADS))Free Software to display 2 Dimensional data

Install
Activate :grads-2.0.2.oga.2-win32
g
g_superpack.exe
p pThis installer is included in USB flash memory(GSMAP/grads/)
 Source : http://sourceforge.net/projects/opengrads/files/
grads2: OpenGrADS Bundle Distribution(Windows/Mac/Linux/Unix)

How to use “Grads” ?

Please test grads by following command

1.

Activate: Command Prompt- All programs/accessory/command_prompt.exe
2 Change Directory to C:¥GSMAP/hourly/NILE/
2.
3. Activate: Grads
・ Grads・ yes

Data description file (ctl file)
Ex. BC-GSMAP-Nile-20km-1hr200003.ctl
DSET ^BC-GSMAP-Nile-20km-1hr200003.gad
BC GSMAP Nile 20km 1hr200003.gad
TITLE bias corrected GSMaP 20km hourly
UNDEF -9999.9 Undefined value
OPTIONS LITTLE_ENDIAN
LITTLE ENDIAN
XDEF 150 LINEAR 20.05 0.166667
YDEF 240 LINEAR -4.95 0.166667
zdef 1 levels 1000
tdef 744 linear 00:00z01mar2000 1hr
VARS 1 Number of variablesrain 0 99 hourly precip(mm/hr)(/ )ENDVARS
Variable nameTdef = 744(24 hours x 31 days)
Start from 2000 Mar 1

Data file name
Y axis(Latitude)
XDEF = 150
XMIN =
20 05
20.05
DX =
0.166667

DATA

T axis (time)- 200 -

X axis(longitude)

Exercise 6: Optimization of Reservoir Operation
Daisuke NOHARA(Water Resources Research Center, Disaster Prevention Research Institute, Kyoto University)
Abstract:
Optimizing reservoir operation policies considering hydrological data is crucially important foreffective management of reservoirs for both the flood control and water use purposes. In thisexercise, we will learn how to prepare and conduct optimization of reservoir operation by using
dynamic programming (DP) techniques through tackling to sample problems. The exercise willconsist of two parts.
The first part aims at learning a general procedure of calculation to optimize reservoir operation
by DP techniques with a simple example problem. A simplified single multi-purpose reservoir is
employed here, and reservoir operation for water supply is optimized by use of deterministic DP
approach. We will practice typical backward algorithm to estimate optimized release policies of thereservoir.
In the second part of the exercise, we will tackle to practical optimization problems considering
a simplified multi-purpose reservoir, whose hydrological and reservoir data are derived from existing
Sameura Reservoir in the Yoshino River basin in Japan. The exercise problems will deal with
optimization of the reservoir operation for water supply by use of deterministic DP and stochastic
DP models. Through tackling the problems, it will be introduced what we must prepare to set up thecalculation for optimization of reservoir operation, including: choosing numbers of levels todiscretize time step and states of the reservoir; setting objective functions according to the objective
to optimize the reservoir; setting constraints for storage volume or release water defined by thephysical constraints or regulations; preparation of hydrological data and target release of the target
reservoir; and setting the time horizon to optimize the water release strategy. The optimization ofoperation of the target reservoir will be computed and be demonstrated with a computer program
developed for DP based optimization of reservoir operations.

- 203 -

CONTENTS••••

Introduction
General
Simple Example
Exercises
1. Optimization for water supply purpose with deterministic dynamic
programming
(DDP)
Japan
Sea
2 Optimization
2.
for water supply purpose with stochastic dynamic
programming (SDP)• For further discussions

Exercise 6:
OPTIMIZATION OF RESERVOIR OPERATION

Daisuke NOHARA
Water Resources Research Centre
Disaster Prevention Research InstituteKyoto University

1

2

INTRODUCTIONSIMPLE EXAMPLE
Optimization of water supply operation of a single reservoir so as to minimize
drought damage caused by deficit in water supply from the reservoir for
three time steps
qt: Inflow during time step t (MCM)dteOptimization of reservoir operations based on dynamic programming (DP)approaches
 Richard
Ri h d B
Bellman’sll’ P
Principle
i i l off O
Optimality:
ti lit•Original problem can be divided into a set of sub problems which needless computational effort to solvetqt Suitable for reservoir operation simulation, which is sequential
processJapan
Sea to any problem including nonlinear problems which have
 Applicablenon‐linear objective functions such as damage functions
 Compatible to computer‐based solving

st: Storage volume at the beginning of time step t(MCM)
rt: Release amount during time step t (MCM)
et: Loss from the reservoir during time step t(MCM)
Assessed point dt: Demand (target release amount) during time
step t (MCM)

rt
st

R
Reservoiri

Physical constraints of the reservoir:• Smin = 00, Smax = 40
• Rmin = 0, Rmax = 40

Matrix for Inflow / Target release (MCM):
t=1

t=2

t=3

qt

10

10

10

dt

20

30

30

3

4

SIMPLE EXAMPLESIMPLE EXAMPLE
et= 0

qt

et= 0

dt
qt

rt
st

Assessed pointst

Reservoir

Assumptions to simplify the problem:•••

Assessed point
Reservoir

Objective function:3

No consideration
N
id ti off losses
l
from
f
the
th reservoir
i [i.e.
[i et = 0]
The assessed point locates just downstream the reservoir[i.e. rt is identical to flow amount at the assessed point]minrt H (r )tt 1

subject
bj t tto:

Other settings:
•

dt
rt

• Smin = 0, Smax = 40, Smin ≤ st ≤ Smax
• Rmin = 0, Rmax = 40, Rmin ≤ rt ≤ Rmax
• st+1 = st + qt – rt – etDrought damage function:
H( t) = {{max[dH(r[dt – rt, 0]}2
Discretizing states into five levels considered for st, qt and rt with increment of
10 (MCM) from 0 to 40.

Recursive equation:f ( st )  min  H (rt )  f ( st 1 )
r (t )

5f(): Future damage function
6- 205 -

SIMPLE EXAMPLESIMPLE EXAMPLE

Calculation backward consideringg no damage
g after the last time stepp ((t ≥ 4))
t=1

t=2

t=3

Calculation of f(f(3)) for each storageg state s3

t=4

t=1

Storage Level 5(40 MCM)f(4)= 0Storage Level 5(40 MCM)

Storage Level 4(30MCM)f(4)= 0Storage Level 4(30MCM)

Storage Level 3(20MCM)f(4)= 0Storage Level 3(20MCM)

Storage Level 2(10MCM)f(4) 0f(4)=Storage Level 2(10MCM)f(4)= 0Storage Level 1(0MCM)

t=2

t=3

t=4

f(3)= 0f(4)= 0400
100

f(4)= 000f(4)= 0f(4)= 0f(4)= 0Storage Level 1(0MCM)

For Level 5 (s3=40MCM),• r3=0• r3=10• r3=20• r3=30• r3=40

7

Not feasible (s4>smax)
H(r3) = (30‐10)2 =400,
H(r3) = (30‐20)2 =100,
H(r3) =0,
H(r3) = 0,s4=s3+q3‐r3= 40,
s4=30,
s4=20,
s4=10

f’(3) = 400 + f(4|s4=40) = 400
f’(3) = 100 + f(4|s4=30) = 100
f’(3) = 0 + f(4|s4=20) = 0
f’(3) = 0 + f(4|s4=10) = 0
8

Optimal policies for the storage state

SIMPLE EXAMPLESIMPLE EXAMPLE

Calculation of f(f(3)) for each storageg state s3
t=1

t=2

Calculation of f(f(2)) for each storageg state s2
t=3

t=4

Storage Level 5(40 MCM)f(3)= 0f(4)= 0Storage Level 4(30MCM)f(3)= 0f(4)= 0Storage Level 3(20MCM)f(3)= 0f(4)= 0Storage Level 2(10MCM)f(3) 100f(3)=f(4)= 0Storage Level 1(0MCM)

f(3)= 400f(4)= 0

t=1

t=2

t=3

t=4

f(3)= 0f(4)= 0f(3)= 0f(4)= 0f(3)= 0f(4)= 0Storage Level 2(10MCM)f(3) 100
400 f(3)=f(4)= 0Storage Level 1(0MCM)

f(3)= 400f(4)= 0Storage Level 5(40 MCM)f(2)= 100

Storage Level 4(30MCM)

900
400
100

Storage Level 3(20MCM)

100

For Level 4 (s2=30MCM),• r2=0• r2=10• r2=20• r2=30• r2=40

9H(r2) = (30‐0)2 =900,
H(r2) = (30‐10)2 =400,
H(r2) = (30‐20)2 =100,
H(r2) =0,
H(r2) = 0,f’(2) = 900 + f(3|s3=40) = 900
f’(2) = 400 + f(3|s3=30) = 400
f’(2) = 100 + f(3|s3=20) = 100
f’(2) = 0 + f(3|s3=10) = 100
f’(2) = 0 + f(3|s3=10) = 400
10

Optimal policies for the storage state

SIMPLE EXAMPLESIMPLE EXAMPLE

Calculation of f(f(2)) for each storageg state s2
t=1

Calculation of f(f(1)) for each storageg state s2

t=2

t=3

t=4

Storage Level 5(40 MCM)f(2)= 0f(2)f(3)= 0f(4)= 0Storage Level 4(30MCM)f(2)= 100f(3)= 0f(4)= 0Storage Level 4(30MCM)

Storage Level 3(20MCM)f(2)= 200f(3)= 0f(4)= 0Storage Level 3(20MCM)

Storage Level 2(10MCM)f(2) 500f(2)=f(3) 100f(3)=f(4)= 0f(2)= 800f(3)= 400f(4)= 0Storage Level 1(0MCM)

s3=s2+q2‐r2= 40,
s3=30,
s3=20,
s3=10,s3=0

t=1
Storage Level 5(40 MCM)f(1)= 200

t=2

t=3

t=4

f(2)= 0f(2)f(3)= 0f(4)= 0f(2)= 100f(3)= 0f(4)= 0f(2)= 200f(3)= 0f(4)= 0f(2) 500f(2)=f(3) 100f(3)=f(4)= 0f(2)= 800f(3)= 400f(4)= 0

400
200
200
500
800

Storage Level 2(10MCM)
Storage Level 1(0MCM)

For Level 4 (s1=30MCM),• r1=0• r1=10• r1=20• r1=30• r1=40

11

H(r1) = (20‐0)2 =400,
H(r1) = (20‐10)2 =100,
H(r1) =0,
H(r1) =0,
H(r1) = 0,s2=s1+q1‐r1= 40,
s2= 30,s2= 20,s2= 10,s2= 0,

f’(1) = 400 + f(2|s2=40) = 400
f’(1) = 100 + f(2|s2=30) = 200
f’(1) = 0 + f(2|s2=20) = 200
f’(1) = 0 + f(2|s2=10) = 500
f’(1) = 0 + f(2|s2=0) = 800

Optimal policies for the storage state- 206 -

12

SIMPLE EXAMPLESIMPLE EXAMPLE

Calculation of f(f(1)) for each storageg state s2
t=1

t=2

t=3

t=4

Storage Level 5(40 MCM)f(1)= 100f(2)= 0f(2)f(3)= 0f(4)= 0Storage Level 4(30MCM)f(1)= 200f(2)= 100f(3)= 0f(4)= 0Storage Level 3(20MCM)f(1)= 300

f(2)= 200f(3)= 0f(4)= 0Storage Level 2(10MCM)f(1) 600
f(1)=f(2) 500f(2)=f(3) 100f(3)=Storage Level 1(0MCM)

f(1)= 900

f(2)= 800f(3)= 400

Getting optimal policies (decision and state trajectories) for eachstorage level at each time step
If you want to know the optimal release policy for storage level 3 at time step 1…

t=1

t=2

t=3

t=4

Storage Level 5(40 MCM)f(1)= 100f(2)= 0f(3)= 0f(4)= 0f(4)= 0Storage Levell 4(30MCM)f(1)= 200f(2) 100f(2)=f(3)= 0f(4)= 0f(4)= 0Storage Level 3(20MCM)f(1)= 300

f(2)= 200f(3)= 0f(4)= 0Storage Level 2(10MCM)f(1)= 600

f(2)= 500

f(3)= 100f(4)= 0Storage Level 1(0MCM)

f(1)= 900

f(2) 800f(2)=f(3)= 400f(4)= 01314EXERCISES

EXERCISE 1: OPTIMIZATION FOR WATER SUPPLY (1)•Optimize water release strategy from a single reservoir for each storage
state at each time step considering the historical streamflow regime in a
dry year• Consider only water supply operation with the storage capacity for that
purpose
p
p
(173
(
MCM))
• Off‐line optimization (not online optimization)• Optimize with deterministic DP (DDP) for one year from January toDDecemberb

Exercise with the simplified Sameura Reservoir system•••

Located in the Yoshino River basin, Shikoku Island, Japan
A multipurpose reservoir for flood control, power generation and water supply
Controlling seasonal variability in streamflow (not for inter‐annual storage)

et= 0
qt

dt

Allocation of storage capacity for each purpose
to operate Sameura Reservoir
Purposesrt

The Yoshino
st
River

Storage capacity
Dry season

Assessed point
SameuraReservoir

Wet season(Oct. 11th ‐ Jun. 30th)(Jul. 1st ‐ Oct. 10th)

Water supply

173 MCM173 MCM

Flood control

80 MCM

90 MCM

Power gen.

36 MCM

26 MCM

Schematic view of simplified Sameura
Reservoir system
1516

FLOW REGIME AND TARGET RELEASE

SETTING UP OPTIMIZATION PROBLEM

Flow regime in the applied dry year (2008) and assumed water demand justdownstream Sameura Reservoir (assumed target release of Sameura Reservoir)

Objective function
T

min
rt

100
Target Release

Applied Dry Year (2008)

t

t 1

subject to:

Averaged Inflow• Smin ≤ st ≤ Smax
• Rmin ≤ rt ≤ Rmax
• st+1 = st + qt – rt – et

90
80
70

Fllow (m3/s)

 H (r )60

Recursive equation:f ( st )  min  H (rt )  f ( st 1 )

50

r (t )

40
30

Drought damage function:
Employing the one proposed by Ikebuchi et alal. (1990):

20
10

 (dt  rt ) 2dt
H (rt )  

0


0
1

3

5

7

9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73

Time steps (in 5‐day unit)

17(rt  dt )(rt  dt )18

- 207 -

SETTING PARAMETERS FOR SOLVING DP

SETTING PARAMETERS FOR SOLVING DP

Descretization of states and time steps

Future damage function at the last time step of optimization
Necessary to define for backward calculation of f()

The number of descretized levels of states and time steps must
carefully be chosen.
chosen•Enough many to describe reservoir and hydrological states according
to the objective of optimization
But as small as possible to secure feasibility in computation•

For this exercise (drought management):

t=T

StorageLev. lf(T|Strorage Lv. = L‐1) = ??

…

f(T|Strorage Lv. = l) = ??

…

…

…
StorageLev. 1

Constraints:

f(T|Strorage Lv. = L) = ??

…
…

Time Step: 5‐day unit (73 time steps in a year)
Flow (inflow and release): 100 levels (4 m3/s for each level)
Storage: 100 levels (1(1.7373 MCM for each level)

…

…

…

•
•
•

t=2

t=1
StorageLev. L
StorageLev. L‐1…

f(T|Strorage Lv. = 1) = ??

Smin = 0, Smax = 173 MCM
Rmin = 0, Rmax = 400 m3/s (≈ 173MCM for 5 days)
Other assumptions:assumptions
et = 0 (No loss from the reservoir considered)

19

20

SETTING PARAMETERS FOR SOLVING DP

EXERCISE 2: OPTIMIZATION FOR WATER SUPPLY (2)Future damage function at the last time step of optimization•••

Optimize water release strategy from a single reservoir for each storage
state at each time step considering historical streamflow regimes
observedbd for
f 30 years• Consider only water supply operation with the storage capacity for that
purpose
p
p
(173
(
MCM))
• Off‐line optimization (not online optimization)• Optimize with stochastic DP (SDP) for one year from January toDDecemberb

Estimating by optimizing water release for a period (e.g. until end of
the next year)
Adding penalty to small storage levels which can admit drought after
the final time step of the optimization
t=1

t=2

StorageLev. L
StorageLev. L‐1

t=T

…
…

…

f(T|Lv. = 1)

f(T+T*|Lv. = L‐1) = 0

f(T+T*|Lv. = l) = 0…

…

With target flow regime(2008 for this exercise)…

t=T+T*
f( *| = L)) = 0
f(T+T*|Lv.

…

f(T|Lv. = l)

…

…

…

…

…

…

…

StorageLev. 1f(T|Lv. = L‐1)…

…

…
StorageLev. lt=T+1
f(T|Lv.
f(| = L))…

f(T+T*|Lv. = 1) = 0

With flow regime assumed for the next year
(1/2 flow regime for this exercise)

qt

t

21

22

HISTORICAL HYDROLOGICAL DATASETTING UP OPTIMIZATION PROBLEM FOR SDP

Inflow sequences observed for 30 years (1979‐2008)

Objective function
T

400.0

min
rt

350.0

 E  H (r , q )
t 1

qt

t

t

Averaged inflow (m3/ss)

subject to:• Smin ≤ st ≤ Smax
• Rmin ≤ rt ≤ Rmax
• st+1 = st + qt – rt – et

300.0

250.0

Recursive equation:

200.0

150.0

f ( st )  min E  H (rt , qt )  f t 1 ( st )

100.0(Neglecting persistence in streamflow)r ( t ) qt

Drought
g damageg function:
Same as the one employed in Exercise 1:

50 0
50.0

0.0
1

3

5

7

 (dt  rt ) 2dt
H (rt )  0

9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73

Time steps (in 5‐day unit)
23- 208 -(rt  dt )(rt  dt )24

FOR FURTHER DISCUSSIONS
The further discussions about DP based optimization of reservoir operation
can be seen in the following references.
DP based optimization of reservoir operation••

Nandalal, K.D.W. and Bogardi, J.J. (2007): Dynamic programming based operation ofreservoirs – Applicability and limits ‐, UNESCO, Cambridge University Press, 130pp, ISBN
978 0 521 87408 3
978‐0‐521‐87408‐3.
Loucks, D. and Van Beek, E. (2005): Water resources systems planning and management –An introduction to methods, models and applications, Studies and Reports in Hydrology,
UNESCO Publishing,g, 680pp.
pp ((with contributions from J.R. Stedinger,
g , J.P.M. Dijkman
jand M.T.
Villars), ISBN 92‐3‐103998‐9.

Application of DP models to optimize actual reservoir systems••••

Kumar, D.N., Baliarsingh, F. and Raju, Srinivasa (2010): Optimal reservoir operation for flood
control using folded dynamic programming, Water Resources Management, 24, 1045‐1064.
Faber, B.A. and Stedinger, J.R. (2001): Reservoir optimization using sampling SDP withensemble streamflow
streamflo prediction (ESP) forecasts.
forecasts Journal
Jo rnal of Hydrology,
H drolog 249,
249 113‐133.
113 133
Turgeon, A. (1980): Optimal operation of multireservoir power systems with stochastic
inflows, Water Resources Research, 16(2), 275‐283.
Tilmant A.
Tilmant,
A Vanclooster,
Vanclooster M.,
M Duckstein,
Duckstein L.
L and Persoons,
Persoons E.
E (2002): Comparison of fuzzy andnonfuzzy optimal reservoir operation policies, Journal of Water Resources Planning andManagement, 128(6), 390‐398.
25- 209 -

Time Schedule

Brief Guide of the Lake
Biwa and the Uji River
forTechnical excursion of IHPTraining Course on Dec 5(Sat) 2015

Meeting place: Hybrid Space of Obaku Plaza
9:20 Meeting time
9:30 Departure: Travel via local road (two cars)
10:00 Amagase Dam:walking on the dam
10:20 Departure: Travel via highway
11:00 Seta Barrage
11:30 Departure: Travel via local road
12 10 Biwa
12:10
Bi LLakek M
Museum:Lunch at Cafeteria of the Museum
13:00 Free time inside
15:20 Departure: Travel via highway
16:15 DPRI Open Laboratory
Watching the riverbed degradation in the Uji River
16:30 Departure: Travel via local road
17:10 Obaku Plaza Breakup

Edited by
Yasuhiro TAKEMON((DPRI,, KyotoyUniv))

Hybrid Space (Meeting place)

Lake Biwa

Amagase Dam

Lake Biwa is a member of ancientlakes older than 100,000 years.
The Lake Biwa region has a lake
history stretching back four
million years, when Lake
Oyamada formed. Such ancientlakes often contain endemic
species that have evolved in thelakes. The secret to lake longevity
is subsidence of the bedrock.

Setagawa weir (Nango‐Araizeki)

Hazard Map of
Lake Biwa Area

Weir and thecontrol office
Area of < 0.5m inundation
Area of 0.5‐1.0m inundation
Area of 1.0‐2.0m inundation

Top of the Weir is usedsed
as a road

Area of 2.0‐5.0m inundation
Area of >5.0m inundation

The control room we will visit- 211 -

Natural Resources Exploitation in Lake Biwa

Visit of the Lake Biwa Museum

Meeting placel
for lunch 11:30

Meeting place fordeparture 13:30

Fisherman’s life in Lake Biwa

History of flood control in Lake Biwa
Physical Characteristics of
the Lake
The waters of the lake can beclassified into two types
according to light availability
and the potential forphotosynthesis. The upper zone
where light can penetratesufficiently for photosynthesis iscalled the euphotic zone, andbelow that is thedecomposition zone. During
the summer Lake Biwa is
stratified thermally. The sunheats up the top layer of thelake and this warm water rests
on top of the colder water
below During the winter the
below.
top layer of water gets colder
than the underlying waterand because cold water isdenser than warmer water, it
sinks causing vertical mixing of
the water column.

Environmental problems in Lake Biwa

You can observe living planktons- 212 -

You can see a lot of freshwater fishes
off LLakek Bi
Biwa iin th
the aquariumi

Mukaijima area of the Uji River
Severe riverbed degradation has been occurring derived fromsediment discontinuityy byy the Amagaseg
Dam and sedimentdredging in the Yodo River.

- 213 -

2. Construction and operation of dams in Uji River

Sedimentation of Amagase‐dam
1. Upstream watershed and sediment yield

Kisenyama dam(H=91m, V=7.2×10(H=91mV=7 2×106m3)

to Yodo River

SSeta Ri
River isi theh outletl off LakeL k BiBiwa.
The name changes to Uji River fromthe border of Kyoto Prefecture.
Amagase dam is located in the
upstream reach of Uji River. Daido
River is the largest tributary of
Seta/Uji River and the largest sourceof sediment because little sedimentsupply from Lake Biwa outlet.

Uji Riverfrom Lake Biwa

Amagase dam(H=73m, V=26.3×106m3)
O‐mine dam(decommissioned
and submerged)

O‐mine dam (constructed in 1923)
Tanakami
mountains

O‐mine dam was constructed for hydro‐power
after an increase of energy demand of Kansai area.
It was theth fifirstt concrete‐gravitytit d
dam ini JJapan.
The dam was decommissioned (submerged) by the
Amagase dam construction.

Height: 31 m

Tanakami, upstream basin of Daido river, has yield
substantial amount of sediment since >1200 year ago
due to heavy logging (for(f temple construction)) anddeforestation of granite hillslopes.

Daido River
Amagase dam (constructed in 1964)

Vegetation has been recovered after Sabo works, whichstarted in early 20th century. Sediment yield has been
reduced, though Daido River still contributes toiincreasing
i sedimentationdit ti off Amagase‐dam.
AdHeight: 73 m

Seta River

Tanakami mountains in early 20th
century

The construction of Amagase dam was initiatedby a big typhoon in 1953 that caused heavy
damage in the downstream Yodo River and Osaka.
It is a multipurpose dam (hydro‐power, flood
mitigation, drinking water supply) supporting
Kansai area. Pumped storage hydro‐power
station is also installed between Amagase andKi
Kisenyamaddams.
Power generation of
Amagase dam Kisenyama Day time

Sabo works for bothhillslopes and streams

flowhydropower
stationCross section of
Amagase dam

Kisenyama

AmagaseAhydropower
stationNight timeflow
AmagasePresent mountains

Actual Sedimentvolume (103m3)

Annual sedimentation record

4. Dam and environmental issues in Uji River
Downstream area of Amagase dam

Discharge(m3/s)

Annual maximum discharge

Cumulative active sediment volume
Annual sedimentation volume
1967

1980

Tottal number of Checkk dams

Total areea of hillside work (ha)Progress of Sabo works

1990

2000

2009

Amagasedam

Present
Total
Sedimentation
volume= 4.19×106 m3

46.0k site
1964
2009
Grain size (mm)Sedimentation
rate in planned
sedimentation
volume0%= 70%

Bed elevation (m)Planned
Total
Sedimentation
volume= 6×106 m3

Bed grain size distribution
Cumulaative
percenttage

3. Reservoir sedimentation in Amagase dam

Sedimentation rate was greater in
1960s and 70s. Hillside work andcheck dam construction have been
intensively implemented in 1890
1890‐
1970 and 1950‐2000 respectively.
These Sabo works have drasticallyreduced sediment yield from Daido
River to Amagase dam.

Longitudinal bed elevation profile
1957

2007

In the downstream of Amagase
dam, the channel was
degradated (bed lowered for>3 m in the downstream part)due to reduced sediment supply
from upstream and channel
excavation.
iBedd materials
i l havehbeen coarsened.

Distance from the Yodo river mouth (km)Environmental issues and future works
Outbreak of caddisfly
Supply ofplankton‐rich waterplanned sedimentation
Almost 70% of p
volume has been already filled.
Capacity loss of total storage is 16%.

Stable bed material

Invasion of Exotic
Golden Mussel

1948

R d d reed
Reducedd fi
field
ld on flfloodplain
d l i
2009

Less sandy bars

Most sediment deposit can be observed in
the downstream of submerged O‐minedam, where deposit depth is almost 20 m.

Low heterogeneity

Decrease of Pale Chub Zacco platypus

Sandy bars and river‐floodplain ecotones have decreased in area, which negatively
impacted habitats of various aquatic plants and animals, while encouraged increase ofparticular organisms including exotic species. Supply of sediment, especially coarse sandand gravel, is essential for the recovery of biodiversity and ecosystem function of Uji River.
The issues should be solved together with sedimentation of Amagase dam.

Deposited materials change at submerged
O‐mine dam, coarse sediment in the
upstream, while fine one in the downstream.

- 214 -Overview of the Field Workshop
at the Hiyoshi Dam and the
Katsura (Hozu) River
9 December, 2015
The 25th UNESCO‐IHP Training Course

Daisuke NOHARA
Water Resources Research Centre
Disaster Prevention Research InstituteKyoto University

1

KATSURA RIVER BASIN
One of the main tributaries of the Yodo River System

Hiyoshi Dam

LAKE BIWA
Hozu Valley
ArashiyamayKameoka

Uketa

Kyoto

DPRI‐KU
Google Earth(Image: Landsat)

Amagase Dam- 217 -

2

RECENT FLOOD EVENT
Flood caused by Typhoon Man‐yi
in September in 2013
Provided by Dr. T. Hori

Provided by Dr. T. Hori

Japan
Sea
Provided by Dr. T. Hori

3

NASA

DPRI

DPRI

LIST OF ACTIVITIES• Meeting up at Hybrid Space of Obaku Plaza (at 8:40 AM)• Visit and workshop
p at HiyoshiyDam• Field exercises at the Hozu/Katsura River: Visiting the inundated areas by the flood due toTyphoonhMan‐yi in 2013
 Measuring
Japanriver width
at severalSea
locations including Kameoka,
Kameoka Uketa,
Uketa Hozu Valley
and Arashiyama• Breakup at Arashiyama (at 5:00PM)

4- 218 -