International Hydrological Programme

Ecohydrology for River Basin Management under Climate Change-The Twenty-third IHP Training Course1

1

Subbasin

Headwater
segment

1

2

1

1

1
2

Mountain
Lake

2
3

2

Reservoir
2
1

1
2

Mountainsegment

1

Basin

1
2

1

1

Watershed

2
3

Valleysegement
1

Tributary

Dam
Spring
4

Grand
water
lake
Grand
water
channel1

Alluvial fun
segment3

Branchriver

Alluvial fun

SpringFlood plane1
2
2

Alluvial
plane

Flood planesegment
Delta segement
Main
river

4

Oxbow
lake
Natural leveeRivermouth
Sandy flat4

Spring-fed river

Branch
river

Delta

Back marsh
Sandy
beach

Water Resources Research Center, Disaster Prevention Research Institute,
Kyoto University
Supported byHydrospheric Atmospheric Research Center, Nagoya University
Disaster Prevention Research Institute, Kyoto University
Global Center for Education and Research on Sustainability Science for Resilient Society
Adaptable to Extreme Weather Conditions, Kyoto University
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan

International Hydrological Programme

Ecohydrology for River Basin Management under Climate Change-The Twenty-third IHP Training Course2 - 13 December, 2013
Kyoto, Japan

Water Resources Research Center, Disaster Prevention Research Institute,
Kyoto University

Supported byHydrospheric Atmospheric Research Center, Nagoya University
Disaster Prevention Research Institute, Kyoto University
Global Center for Education and Research on Sustainability Science for Resilient Society
Adaptable to Extreme Weather Conditions, Kyoto University
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan

Outline
A short training course on ecohydrology under climate change is programmed forparticipants from Asian-Pacific regions as a part of Japanese contribution to theInternational Hydrological Program (IHP). The course composed of a series of lectures,practice sessions, and field surveys along the Kizu River will be held mainly at Disaster
Prevention Research Institute (DPRI), Kyoto University during the two weeks from 2 to
13 December 2013.
Objectives
Water is our most valuable natural resource. The availability and quality of fresh
water not only impact human health and wellbeing, but also the functioning ofessential ecosystems, including rivers, wetlands, lakes and coastal ecosystems.
Without sound water resources management, human activities can upset the delicate
balance between water resources and environmental sustainability.
Ecohydrology is an integrative science studying the relationships betweenhydrological and ecological processes in soils, rivers and lakes at the catchment scale.
It deals with hydrological factors which determine the dynamics of natural andhuman-driven ecosystems, together with ecological factors which influence water
dynamics and water quality. It proposes a “dual regulation” of a system bysimultaneously studying ecological and hydrological processes to enhance the overall
integrity of aquatic ecosystems in the face of human-driven alterations and Global
Change. River basins have a hierarchical structure and natural boundaries, and can beconsidered as inherent integrators of the effects of many climatic and non-climatic
factors. That is why river basins represent a suitable scale for integrated
ecohydrological studies and modelling.
The 23rd IHP training course is focused on three major objectives: (1) to acquire
the latest knowledge on hydrological and ecological assessment under climatechanges at river basin scale, (2) to make practice for learning the methodologies forassessing the impact of climate change on hydrological and ecological processes, and(3) to discuss the possibility to include the hydrological and ecological responses toclimate change into the water resources managements.

Course Contents
Convener: SUMI, Tetsuya (Disaster Prevention Research Institute, Kyoto University)
Chief assistant: TANAKA, Kenji (Disaster Prevention Research Institute, Kyoto University)

Lectures
Keynote 1

Overall concepts of Ecohydrology

S. Kazama

Keynote 2

Ecohydrology: process oriented thinking towards sustainableenhancement, water resources, biodiversity, ecosystem services
and resilience to climate change

M. Zalewski

Lecture 1

Fundamentals of basin-scale hydrological processes

Y. Tachikawa

Lecture 2

Projected future meteorological environment

E. Nakakita

Lecture 3

Fundamentals of freshwater ecology

Y. Takemon

Lecture 4

Sustainable management of water resources in marginal area:
Study case in Indonesia
Ignasius D. A. Sutapa

Lecture 5

Biodiversity and Ecosystem Services in the Context of Global Change

O. Saito

Lecture 6

Integrated sediment management

T. SumiLecture 7

Interaction between river and coastal ecosystem

Y. Suzuki

Lecture 8

IHP perspectives on ecohydrology and related demonstration projects

S. Khan

Lecture 9

Fundamentals in optimum operation of reservoir systems

T. Hori

Practices
Exercise 1

Fundamentals of Data Processing

Exercise 2

Data analysis of GCM data, historical data

Exercise 3

River basin modelling

Y. SatoExercise 4

Impact assessment by hydrological model

Y. SatoExercise 5

Impact assessment by ecological model

Exercise 6

Optimization of reservoir operation

Field Survey

Ecological field survey at Kizu riverTechnical visits
Lake Biwa, Uji River, Katsura River

T. Hamaguchi
K. Tanaka

S. Kobayashi
D. Nohara
Y. Takemon

Schedule (1 to 14 December, 2013)
1 (Sunday)

Arrival at Kansai Airport and movement to Kyoto (El-INN Kyoto)

2 (Monday)

10:00-11:30 Opening & Registration & Guidance
13:00-14:30 Keynote 1 by S. Kazama
15:00-16:30 Keynote 2 by M. Zalewski
17:00-18:30 Welcome Party (@S217D)

3 (Tuesday)

09:30-12:00 Lecture 1 by Y. Tachikawa
14:00-16:30 Lecture 2 by E. Nakakita

4 (Wednesday) 09:30-12:00 Lecture 3 by Y. Takemon
14:00-16:30 Exercise 1 by T. Hamaguchi
5 (Thursday)

09:30-12:00 Lecture 6 by T. Sumi
14:00-16:30 Exercise 2 by K. Tanaka

6 (Friday)

09:30-12:00 Exercise 3 by Y. Sato
14:00-16:30 Exercise 4 by Y. Sato

7 (Saturday)

09:30-17:00 Technical visits to Lake Biwa and Uji River

8 (Sunday)

09:30-17:00 Technical visits and Cultural exchange with students
at the Katsura river

9 (Monday)

09:30-12:00 Lecture 5 by O. Saito
14:00-16:30 Lecture 4 by I.D.A. Sutapa

10 (Tuesday)

09:30-12:00 Lecture 7 by Y. Suzuki
13:00-14:30 Lecture 8 by S. Khan
15:00-17:30 Exercise 5 by S. Kobayashi

11 (Wednesday) 09:30-17:00 Ecological field survey at Kizu river
12 (Thursday)

09:30-12:00 Lecture 9 by T. Hori
14:00-16:30 Exercise 6 by D. Nohara

13 (Friday)

09:30-11:30 Report presentation by each participant
11:30-12:00 Completion ceremony of this course
13:00-15:00 Farewell party (@S217D)

14 (Saturday)

Departure from Kansai Airport

Keynote 1: Overall Concepts of Ecohydrology-Application of Hydrological Models to EcologySo KAZAMA (Dept. of Civil and Environmental Engineering, Tohoku University,
Water Resources Research Center, DPRI, Kyoto University)
Firstly, firefly habitat was estimated using a geographical information system (GIS) andhydrological simulation in the Natori River basin in Tohoku, Japan. To investigate suitable conditions for
firefly habitat, the relationships between observed firefly habitat and physical environment, such as
geological conditions, hydrological conditions and land use types obtained from digital maps were
determined. Suitability criteria for firefly habitat were then defined based on habitat suitability index (HSI)
estimation. We found that watershed areas smaller than 5000m2 with flat plains (slopes less than 0.15) are
ideal geological conditions for firefly habitat. Further, we found that non-urbanized areas with urban ratios
less than 0.1, deciduous forests, agricultural lands, and paddy fields are the best land use types for firefly
habitat. This evaluation also indicates that fireflies prefer to live in shallow and calm water environments(water depths less than 500mmand velocity less than 1.0 m/s). The habitat locations determined by thesecriteria were validated by comparing them to field observations of firefly habitat. The results indicate that
this model was successful in estimating the existence of firefly habitat in hydroenvironments.
Secondly, the habitats of aquatic organisms, including freshwater fishes (Oncorhynchus masou
masou, Plecoglossus altivelis altivel, and Cyprinus carpio), fireflies (Luciola cruciata and Luciola
lateralis), and frogs (Anura sp.), were evaluated dynamically in the Natori River basin using watertemperature as an environmental index. The HSI (habitat suitability index) and WUA (weighted useablearea) of aquatic organisms were quantitatively calculated using a distributed runoff model coupled with aheat budget model, land cover data, and digital cartographic data. The results showed that the HSI values of
O. masou masou and P. altivelis altivel tended to be high in locations where they were actually observed.
The HSI distribution patterns of L. cruciata and L. lateralis showed different results, even though the two
species are in the same genus and have similar life cycles.
This lecture shows some examples of HSI model application using hydrological models. The habitat
maps are useful for understanding of ecological systems.

Contents
OVERALL CONCEPTS OF ECOHYDROLOGY‐Application of hydrological models to Ecology‐

1. Introduction
2. Objectives
3. Study Area
4. Date Set

So KAZAMA

5. Studied Organisms

Department of Civil and Environmental Engineering, Tohoku University
Water Resources Research Center, DRPI, Kyoto University

6. HSI (Habitat Suitability Index) Model
7. Results
8. Conclutions

1. Introduction2.

・In 1986, ICSU* decided to IGBP* to understand effects of earthsystem from human environment.
Based on IGBP decides, many researchers researched
relationships water environments and ecological systems.
(e.g. Christoph et al., 2012, Wen-Chieh et al., 2011)※e. g. Evaluation of habitat
suitability using ecol model

Recently, many researchers use ecologicalmodels such as PHABSIM* and HEP*
to evaluate of habitat suitability of aquatic
animals.
ICSU* ：The International Council for Science
IGBP* ：International Geosphere-Biosphere Programme
HEP* ：Habitat Evaluation Procedure
PHABSIM* ：Physical Habitat Simulation Model

0

20km

Objectives

In this study, We focus Aquatic Insects
Why focus aquatic insects?
Aquatic insects are used by index of river environment
But・・・
There are few researches on evaluating the habitat
suitability of aquatic insects
Objectives
Evaluate spatial variations of habitat suitability of aquatic
insects quantitatively combining dual approaches of using
HSI* model analysis and hydrothermal simulation model.

0 0.16 0.32 0.48 0.64 0.80 HSI

3. Study Area
・Natori River basin
Located：Miyagi Pref. (Northeastern Japan)
Catchment area：939km2
Arterial river length：55km

HSI*：Habitat Suitability Index

4.

Date Set
Land Cover Date
Digital Cartographic Data

Digital National Land information database of Miyagi Pref.
(generated by MILT*)J-IBIS* (generated by MOE*)

Hydrological Date

The Distributed runoff model and The heatbudget model(Nukazawa et al., 2011)

MILT* ：Ministry of Land, Infrastructure and Transport
J-IBIS* ：Japan Integrated Biodiversity Information System
MOE* ：Ministry of the Environment

5. Studied Organisms
Trichoptera

5. Studied Organisms
Others※We chose 9 aquatic insects

Net-spinners

Burrowers

Swimmers

Ephemera japonica MacLachlan
Hydropsyche orientalis

Stenopsyche marmorata

Attachers

TipulaBaetisHydropsyche albicephale
Case-Bearers

Crawlers

Habitat Dates
We used Hamamoto’s survey results(45 survey points in Natori River Basin, 2006)

Psephenidae
Limnocentropus insolitus

Glossosomatidae

6. HSI(Habitat Suitability Index ) Model

6. HSI(Habitat Suitability Index ) Model

Environmental indicators

Evaluate habitat suitability using environmental indicators①Make up SI(Suitability Index from field survey ②Read environmental indicators
e.g. SI of Water Temp

1

Land Cover Date
・Land usee.g. Water Temp

0：not suitable
1：suitable

0

20km

Digital Cartographic Date・Slope・Urbanization ratio
・Distance to urban areas
・Distance to forests

SI

0.5

This Study
Characteristic08‐10 10‐12 12‐14 14‐16 16‐18

18‐

Hydrological Date

Water Temperture（℃）
8

10

12

14

16

(SI1×SI2×･･･×SIp)1/p=HSI④HSI calculated using thegeometric mean of SI values

The distributed runoff model・Flow velocity
・Water depth

The heatbudget model・Water temp

18 (℃)

Figure is Distributed of Water Temp.

③Decide of SI※Annual mean, annual maximum and annual minimum dates

p is the number of components.

6. HSI(Habitat Suitability Index ) Model
Calculate WUR(Weight Usable Ratio)

Verification of HSI
using Coefficient of determination（R2）＞0.5 and p value＜0.05

We developed a new index

WUR(Weight Usable Ratio)

ExampleWUR  WUA / catchment
area

1 n
 HSI in i

WUR  0 ～ 1

points

WUR = 0 ： The catchment area has no habitat suitability
WUR = 1 ： The catchment area has most habitat suitability

TipulaPopulation density(Population/0.27m2)

Index for evaluate the overall habitat suitability throughout the catchment area

7. Results（Verification）

Regression line(Tipula)40

n=55HSI-18
R2=0.73
p<0.05

20

0
0

0.2

0.4

HSI

0.6

0.8

1

Calculated coefficient of determination and p value using this graph7. Results（Verification）

7. Results

Coefficient of determination（R2）＞0.5 and p value＜0.05
R2=0.57

R2=0.68020km

R2=0.93

Make figures ofspatial distribution of HSI
Black areas show not River Area
Color lines show River Line

Hydropsyche albicephale

Hydropsyche orientalis
R2=0.5Limnocentropus insolitus

R2=0.73

R2=0.56

HSI value is low
the spots have
low habitat suitability

Ephemera japonica MacLachlan

BaetisTipulaHSI value is highthe spots havehigh habitat suitability

Six aquatic insects HSIs are properly made0

7. Results

0.32

0.48 0.64 0.80

HSI

8. Conclusions
Stenopsyche marmorata

Hydropsyche orientalis
00.16

Limnocentropus insolitus

20km

WUR=0.25

WUR=0.26

WUR=0.06

HSI is available analysis to make figure
TipulaEphemera japonicaof
MacLachlanof spatial distribution
habitatsuitability!!

BaetisHighest habitat
suitability!!

WUR=0.16

WUR=0.55

00.16 0.32

0.48 0.64 0.80 HSI

WUR=0.38

We showed HSI amounts about nine aquatic insects in Natori
River basin. Result of verification of HSI, we calculated that six
aquatic insects HSI are properly made.
We made figures of spatial distribution of HSI and evaluatedhabitat suitability using HSI and WUR about six aquatic insects.
As a result, Baetis has the highest habitat suitability in Natori
River basin.

Keynote 2: Ecohydrology: Process Oriented Thinking Towards Sustainable
Enhancement, Water Resources, Biodiversity, Ecosystem Services andResilience to Climate Change
Maciej ZALEWSKI (European Regional Centre for Ecohydrology under auspices of UNESCO,
Department of Applied Ecology , University of Łódż)The fundamental assumption of Ecohydrological theory is that water has been major driver of thebiogeosphere evolution. That is why understanding water biota interplay for sustainable future of the planet.
In the face of global challenges such as changes in geopolitical and economic centers, as well as
demographic processes, combined with progressing degradation of the environment its resources andclimate changes, there is an urgent need to formulate a new proactive strategy that harmonize the humanity
needs with water resources and ecosystem potential. The one of the critical issues provided by the
Ecohydrology to the integrated of the integrated water resources management IWRM is harmonisation of
the human needs with the enhanced ecosystems carrying capacity understood as water, ecosystem services,biodiversity and resilience to climate instability and anthropogenic impacts. The ecosystems characteristics
such as pattern of the distribution of the biomass in the catchment area, and its biodiversity, play importantrole in shaping the water cycle. On the other hand the surface/ground water interplay to great extent
determine biological structure, productivity and related processes in terrestrial, freshwater and coastal zone
ecosystems, with broad range of socioeconomic consequences.
Ecohydrology (EH) is defined as sub-discipline of hydrology that focuses on ecological processes
occurring within the hydrological cycle and strives to utilize such processes for enhancing environmental
sustainability, developed in the framework of the International Hydrological Programme of UNESCO is
focused on regulation of water biota interplay from the top of the river basin up to the bottom of thereservoir and costal zones, toward slowing down transfer of water from sky to the sea, enhance
groundwater resources and maintaining critical habitats for water, energy and nutrients circulation, which
in turn maintain biodiversity. Also for reduction of the input and regulate allocation excess nutrients andpollutants toward reversing ecosystems degradation and improvement of human well being.
Follows the three criteria by ICSU, the science of the XXI century has to be (1) integrative, (2)problem solving and (3) policy oriented. Integrative because integrate ecology and hydrology with thespecial consideration of geomorphology. Problem solving because considering society priorities such as
water quality, food production, flood protection, drought compensation, cultural aesthetic values and truism.
Policy oriented because sustainable development and reversing biosphere degradation is the MDG GOAL.
What kind of “know how” ecohydrology proposes to achieve those three goals?
The general assumption is that water is determinant of carbon retention in terrestrial ecosystems,
biomass and plant production and ecological succession in different climatic (temperature) (Zalewski2002).On the other hand the diversified plant biomass efficiently reduce leakage of nutrients fromterrestrial to aquatic ecosystems and to costal zones. Ecohydrology due to methodological specifics has
been based on interconnected two phases- terrestrial and aquatic. The terrestrial has been focused on water
plant soil/ground waters interactions and plant cover is first important filtering system and systemenhancing the infiltration and stabilizing water circulation within the catchment. In the aquatic phase, thecontent of the nutrients in ground water which supplies freshwater and costal zone ecosystems to great
extent determine its biological productivity and in consequence biodiversity.
As far as Ecohydrology is not only curiosity driven but also problem solving science threeprinciples are provides framework for research and problem solving: Hydrological – focused on
quantification hydrological cycle in the basin scale and identification of threats; Ecological – understanding
water biota interplay; Ecological Engineering – the implementation of understanding water biota interplay“dual regulation” for enhancement caring capacity of WBSR towards harmonization catchment, ecologicalpotential with society needs.

International Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO

Ecohydrology : process oriented thinking towards sustainable
enhencemnt, water resources, biodiversity, ecosystem services andresilience to climate changeInternational Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO
We are living in the Anthropocene Era when almost 80% of our usable ecosphere
has been conditioned, converted, and consumed by humans, usually without understanding the fullconsequences of our actions. Columbus Declaration EcoSummitt 2012

Prof. dr hab. Maciej Zalewski
Director of ERCE u/a UNESCO
Chairman of Department Applied Ecology ,University of àodĪ„Legal system and politics in the range of non material values, moral and believes, changes
much slower than economic processes.” W.F Ogburn 2010

International Institute of the Polish Academy of Sciences

International Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO

European Regional Centre for Ecohydrology u/a UNESCO

WATER is the key driver of biological evolution, food productionand ecosystem services
The forecast of water resources limitation in 2025

To solve sustainability of water, food and ecosystem services problems,
the global processes perspective should be considered.
Degradation of soil resources:
All agricultural soils show signs of degradation

Effect of climate change

Effect of demographic dynamics
and economic development

Index of multiuseof water resources(Vörösmarty i in. 2000)

World map of severity of land degradation – GLASOD (FAO 2000)

International Institute of the Polish Academy of Sciences
International Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO

In the face of global processes such as:• progressing degradation of the environment (water resources and soil quality),• climate change,• demographic processes, and• geopolitical and socio-economic changes

the major challenge for science becomes the harmonization ofhuman needs with ecosystem carrying capacity (CC)European Regional Centre for Ecohydrology u/a UNESCO

DeductivebackgroundofEcohydrologytheory

International Institute of the Polish Academy of Sciences

International Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO

European Regional Centre for Ecohydrology u/a UNESCO
Modification of hydrological mezocycle in Gumera Rver basin

DeductivebackgroundofEcohydrologytheory

Ͳ Deforestation
Ͳ Unificationofagriculturallandscapedryirrigated

Zalewski 2002; 2010

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

InductivebackgroundofEcohydrologytheory

ECOHYDROLOGY - THE MAJOR BODY OF THE THEORY

The regulation of water level in eutrophic reservoir for change ofexcess nutrients allocation toward the reduction of toxic algal blooms

Low water level“DUAL REGULATION”
Regulation of biota
by altering hydrology
and regulation of hydrology
by shaping biota

High water level

BIOTA
REGULATIONHYDROLOGY
HARMONIZATION
of ecohydrological measureswith necessary hydrotechnicalinfrastructure

toxic algal blooms

INTEGRATION
of various regulations acting in asynergistic way to stabilize andimprove the quality of water resources

Toxic algal bloom
ERCE UNESCO , Poland Zalewski 20011

Zalewski et al. 1990

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

Effect of ground water level restoration
on ecosystem structure and productivity (Spain, Donana )I – FIRST PRINCIPLE (Zalewski2010)
Quantification of hydrological cycle as a template forbiogeochemical cycles analysis in a catchment scale, andidentification of threats
Precipitation

November 2000

December 2001

October 2002

November 2003

Evapo-transpiration
Infiltration
Total run off
Retention

Subsurface
run off
Surface run off

Underground run off

Curtuasy of Prof. I. LlamasII – SECOND PRINCIPLEInternational Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO(Zalewski 2008)

Identification of potential areas for enhancement of
ecosystem carrying capacity
RETENTION IN THE CATCHMENT
by enhancement of landscape diversity

TRANSFORMATION
into biomass in land water
ecotones

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCOTHIRD PRINCIPLE - Using biota to regulate hydrological processes andvice versa, using hydrology to regulate biota dynamics – DUAL REGULATIONH

REGULATION

Hot spots

SELFPURIFICATION- mineralization of organic
matter- reduction of spiralingtransport rate

SEDIMENTATION- pondage- at the floodplain

CONSERVATION

Bioenergy
production

DENITRIFICATION
in anaerobic conditions ofwetlands

TRAPPING- in plant biomass (seasonally
removed)- storage in the unavailable
pool in bottom sediments

B

Buffer stripes(ecotones)

Main reservoir

Land reclamations

RECIRCULATION
reduction of resuspension- phosphatase – enzymatic release- zooplankton excretion

BIOFILTRATION- reduction of algae biomassby biota

HYDROLOGICAL CONTROL
hydrological control of biotic feedback towards water quality
improvement in reservoirs

I
Sustainable development

Overexploitation andoverengineering

Harmonization of society needs withenvironmental potential

Approach

Mechanistic-deterministic

Evolutionary-systematic

Paradigm

Reductionistic-sectoral

Holistic-transdisciplinary

Achievable goal

By-pass(fish-ladder)

Ideas determine not only framework for integrationof technologies with environmental potential and society needs
but also identify potential areas for synergies

According to Romer (2006) the dynamics of economy and development in 50% depend on newtechnologies and 50% on ideas and systemic solutions

Progressing degradationof biosphere

ECOLOGICALENGINEERINGconstructed wetlands

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCOCONSEQUENCESRESTORATION

Sediment
releasesystemT
SolutionsTechnologies

M

Methods
Ecosystem structure

Process oriented thinking

IDEAS(Zalewski 2012)

S

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

Transfer of organic matter and pollutants to coastal zone
Tanganika
Lake

Gulf of Mexico

NASA Earth Observatory

Source pollution from agriculture landscapeInternational Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

Proportion of sources contributing to phosphorus andnitrogen input into the Baltic Sea sub-regions

Phosphorus

Molecular biology for Ecohydrology:
methods for identification of threats and early warning

Nitrogen(HELCOM2004)

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

Molecular biology for ecohydrological biotechnologies
Biodegradation of hepatotoxic cyanobacteria

Ecohydrlogical biotechnologies - construction of high efficiency
land water ecotones for reduction of diffuse pollution

Cyanophage can degrade cyanobacterial cells

Denitrification wall
Degradationofaddedorganicmaterialstimulatesgrowthof
denitrifyingbacteria,andthusconversionofnitratetonitrogengasoccurs.

The BLAST homology search revealed 90% similarof the Polish samples to g91 gene of cyanophage
Ma-LMM01, described before by Yoshida et al.
(2008).
(Mankiewicz-Boczek, Gągaáa, Dziadek in progress)

Demosite: denitrificationwallaroundthestorageofmanure
locateddirectlyonthegroundinpigs’farm(stockof200).

Bacteria can degrade microcystins
Bacteria capable of degrading microcystins in the Polish lowland reservoir

NO3 concentration in ground water [mg/l]
wartoĞci Ğrednie (mg/l)

250

The BLAST homology search revealed 95% similar of
the Polish samples to mlrA gene of Sphingopyxis sp.
C1, described before by Okano et al. (2009).

77,9 %

200
150
100
50
0

(Mankiewicz-Boczek, Gągaáa, Dziadek in progress)

I(photo: ERCE)

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

II
stanowisko

III(Bednarek, 2009)

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

Ecohydrological biotechnologies for construction of high efficiency
land-water ecotones for reduction of diffuse pollution
LIFE+ EKOROB project
IDENTIFICATION OF PROBLEMSDEVELOPMENT OF SOLUTIONS

Denitrification wall in the ecotone buffer zone

Reduction of nitrogen pollution from diffuse source by enhancement of plant buffering zones with denitrification walls
3.09.2010

350

30.09.2010

300

3.11.2010

200
150

Critical value
marking polluted wateracc. to Nitrates
Directive

100
50
0

B1

(www.geoportal.gov.pl)

B2

B3

B4

Recreational facilities(jetty)PlantsDenitrification walls

B5

Reduction of phosphorus pollution from diffuse source by enhancement of plant buffering zones with biogeochemical barriers
7

3.09.201030.09.2010
3.11.20109.12.201019.01.2011

6

Sulejow Reservoir

5
4

Phosphorus
loadareafrom recreational

Recreational facilities(jetty)Critical value
for appearance of
cyanobacterial bloom

1
0
Z1

Z2

Z3

Z4

200
180
160
140
120
100
80

Biogeochemicalbarriers

EUNitrates
Directive

60
40
20
0

Plants32

NO3 concentration

9.12.2010250

Sulejow Reservoir

220

in groundwater[mg/l]
stĊĪenie azotanów
w wodzie gruntowej[mg NO 3 /l]Nitrogenload
from agricultural land

400

przed Ğcianą

w Ğcianie

BeforeIndenitrification denitrification
walls
walls

ĝrednia
Min-Maks

Z5(www.geoportal.gov.pl)

Izydorczyk, Frątczak, Zalewski, 2011

(Izydorczyk,Bednarek, Zalewski 2012)International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

QUANTIFICATION OF FLOOD PROCESSES AND SEDIMENTATION
IN THE PILICA VALLEY

Demonstration side of LIFE+ EKOROB project

Model of flooding
for the highest water level

DTM of the 30 km sectionof the Pilica River valley
Identificationof the flooding areasin the valley

W. L.
(260 cm)

Experimental
river floodplain

BeforeAfter

Retentionof nutrientsand sediments’
load

Implementation of EH Biotech - enhancementof water quality, recreation, education
RETENTION

Izydorczyk, Frątczak, Zalewski 2012

Flooding
areas

Sediments
load

TN
load

TP
load

1007 ha

560 tons

8 tons

129 tons

KiedrzyĔska Zalewski 2012)

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

CHANGE OF THE CITY
CHARACTER & DEVELOPMENT

Reduction of dioxins and dioxin-like compunds toxicity alonglarge lowland reservoirs of different type of the catchment use
Agricultu, urban, industrial catchment

Agriculture/urban catchment

Wáocáawski Reservoir

Jeziorsko Reservoir

Concentration[ng/kg d.w.]

middleSulejowski Reservoir

dam

upper

2500

2500

2500

2000

2000

2000

1500

1500

1500

middledam

The City of Lodz in

The City of Lodz in

1928

2006

INDUSTRY

HIGH-THECHNOLOGY,SCIENCE, EDUCATION

*
1000
*
International
Instituteof the Polish Academy1000of Sciences
500
European Regional 500
Centre for Ecohydrology 500u/a UNESCO
**
**
**
0
0
0
Duringthe transport of dioxins
and dioxin-like copmpounds
along the reservoir,
conversion of 20highly toxic to less toxic forms
occured20
20

Change of landscapeand society expectations

1000ĝrodek

Toxicity[ng TEQ/kg d.w.]

middledam

Agriculture catchmentĝrodek

Tama

15

1

Tama

15

15

10

10

2

3

*
*

10

5

5

0

0

5

**

**
ĝrodek

PCDD

Tama

PCDF

dl-PCB

**

0
J1

J2

* Results relevant statistically
Wilcoxon Test

1

2

3

** Results relevant statistically
ANOVA Friedman

Compacted, highly impermeable
historical development

Urbaniak M., Zalewski M.,2010. Large dams as purification systems for toxic PCDD/PCDF and dl-PCB congeners [in:] Hydrocomplexity: New Tools for
Solving Wicked Water Problems, Khan S., Savenije H.H.G., Demuth S., Hubert P., IAHS Publ. 338: 256-257.

International Institute of the Polish Academy of Sciences
International Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO

European Regional Centre for Ecohydrology u/a UNESCO
Ecohydrological biotechnologies for purification of urban stormwoter
àódĨ, Sokoáowka river
UrbanͲruraldifferencesintheprevalenceofasthmaandhayfevernchildreninBodzkieRegion(KuprysͲLipinskaetal.,2009)

p<0,05p<0,05alergyUnpermeable urban surface %(Zalewski 2011)International Institute of the Polish Academy of Sciences

System solutions: BIOTECHNOLOGIES
the sequential biofiltration system (SBS) for urban stormwater purification
on Sokoáówka river

European Regional Centre for Ecohydrology u/a UNESCO
Molecular biology for ecohydrological biotechnologies
Diagnosis of quality of environment
Identification of patogenic bacterias
Increase of the effectiveness of stormwater purification
Bacterial diversity in different zones of the Sequentional Stormwater Purification System (SSPS)

100

80

60

40

20

0

100

80

60

40

20

Rheinheimera soli

0

100

80

60

40

20

0

assessed by the TRS-PCR (1-3) and partial 16S RNA gene sequencing
Pseudomonas benzenivorans (ss)Pseudomonas mohnii

Aeromonas ichthiosmia

Aeromonas allosaccharophila

Pseudomonas mohnii

Bacillus safensis

Pseudomonas kilonensis (ss, sp)
Arthrobacter bergerei (ss)Rheinheimera tangshanensis
Aeromonas tecta
Aeromonas tecta

Acinetobacter johnsonii (ss)Acinetobacter bouvetii (ss, sp)
Aeromonas piscicola

Bacillus herbersteinensis
Aeromonas sobria

Pseudomonas rhodesiae

Aeromonas ichthiosmia

Pseudomonas rhodesiae

Bacillus aerophilus

Pseudomonas rhodesiae

Pseudomonas kilonensis

Pseudomonas rhodesiae

Pseudomonas vancouverensis

Pseudomonas rhodesiae

Rheinheimera soli (ss, sp)

Pseudomonas rhodesiae

Alkanindiges illinoisensis (ss, sp)

Pseudomonas peli

Exiguobacterium sibiricum

Aeromonas tecta

Aeromonas piscicola (ss)Bacillus safensis

Acinetobacter johnsonii (ss)Rheinheimera chironomi (sp)Pseudomonas congelans

Aeromonas hydrophila

Rheinheimera tangshanensis (ss, sp)

Pseudomonas benzenivorans (ss, sp

Rheinheimera tangshanensis

Bacillus simplex

Shewanella putrefaciens

Exiguobacterium sibiricum

Bacillus simplex
Bacillus cereus*

Comamonas aquatica
Rheinheimera chironomi
Exiguobacterium artemiae

Sedimentationzone

Pseudomonas baetica
Pseudomonas rhodesiae

Aeromonas allosaccharophila

Bacillus cereus*

Aeromonas ichthiosmia

Pseudomonas cuatrocienegasensis (Pseudomonas extremaustralis

Pseudomonas peli

Biogeochemical zone

Biofiltration zone

Sequentional Stormwater Purification System(Zalewski, Mankiewicz-Boczek, Parniewski et al., 2012)

International Institute of the Polish Academy of Sciences

International Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO

European Regional Centre for Ecohydrology u/a UNESCO

Ecohydrological biotechnologies for purification of urban waters
The progress in water purification efficiency

Strefasedymentacyjna

Strefa biogeochemiczna

Strefa biofiltracji(Zalewski, Szklarek, Lont 2013)

International Institute of the Polish Academy of Sciences European Regional Centre for
Ecohydrology u/a UNESCO

Conversion of sludge into bioenergy at willow plantations
at the buffer zone of sewage treatment plant

PlantationiComparative experiments on
different species and varieties of
willowDrobniewska, Zalewski in press䣭䣤䢴

I: Salix viminalis clones;
II: Tordis (Salix schwerini x S. viminalis) x S. viminalis;
III: Salix viminalis gigantea;
IV: Salix viminalis (clone 192)

International Institute of the Polish Academy of Sciences

International Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO

European Regional Centre for Ecohydrology u/a UNESCO

Min.

7000

(kg/ha/year)

Willow biomass

8000
Variety 1
Variety 2

6000
5000

470

Variety 3
Variety 4
Variety 5

4000
3000

Max.

2000
1000

8100

0
0,8

0,5

16
14

Min.

12

(mg/kg)9000„EH Dual regulation” : The importance to use of hydrology groundwaterlevel and the selection of the plant variety for optimisation of theprocess of heavy metals phytoextraction

Pb concentration in shoots

Groundwater level regulation as a key factor
for bioenergy yield and economic efficiency

Variety 1
Variety 2
Variety 3
Variety 4
Variety 5

10
8
6
4

3,1
Max.

2

14,5

0
0,8

0,2

0,5

Groundwater level (m)0,2Groundwater level (m)International Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO
Decision support model for efficient conversionof sewage sludge in to bioenergy

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

System solutions for harmonisation of society needs withwater and environment quality and economical sustainability

pH glebygestosc obsady
ilosc sadzonek na ha

SewagepH glebya biomasa~
gestosc obsady a biomasa~

Biomasa

sila ssaca
gleby pF~

procentowa ilosc zaatakowany ch
sadzonek przez szkodniki

czy nnik 1~
przy zy walnosc a poziom wod
gruntowy ch w ciagu 4 lat

f otosy nteza

biomasa azwierzy na~

wilgotnosc
gleby %

ilosc sadzonek zaatakowany chprzez szkodniki na hawzrost biomasy a P
przez 4 lata
srednia miesieczna~
temperatura dla Lodzi oC

wilgotnosc
gleby %

wzrost biomasy a N
przez 4 lata~~
wilgotnosc
a temperatura

CO2 assimilation

Employment
opportunities

Sewage treatment
plant

bioenergy

constructed wetland- willow plantation~~

akty wnosc zwierzy ny
a jej ilosc~
wspolczy nnik wilgotnosci

Reduction of fossil
fuel usedecreases CO2
emissionczy nnik 2

przezy walnosc
a sila ssaca gleby

wzrost biomasy a K
przez 4 lata~
biomasazjedzona przez zwierzy ne
w kolejny ch latachilosczwierzy ny

gestosc obsady
ilosc sadzonek na hautrata biomasy wierzby na skutek
zachwaszczenia w ciagu 4 lat
procent utraty biomasy
przez drugi rok

utrata biomasy wierzby na skutek
zachwaszczenia w 2 roku %wilgotnosc a opady

procent utraty
biomasy przez 1 rok~

utrata biomasa wierzby na skutek
zachwaszczenia w 1roku %Tourism development
stimulates economic growthof the region

water quality
improvement~~
srednie miesieczne
opady dla Lodzi~
srednia miesieczna
temperatura dla Lodzi oC

biomasa chwastow w
pierwszy m roku t s m na ha~poziom wodya opady

biomasa chwastow
w drugim roku t s m na ha(Drobniewska, Zalewski 2010)(Zalewski, 2000)

European Regional Centre for Ecohydrology u/a UNESCO

To achieve the Millennium Development Goals and adapt to global changes
understanding of the water/ biota interplay becomes crucial.
The ecohydrological regulation of water dynamics in catchment mezocycle has to befocused on:.
• slowing down water transfer from catchment to sea ( enhencemnt of water retention
in the landscape towards reduction of floods and droughts);• improving water quality;• maintaining and stabilizing the critical water, energy and nutrient fluxes, tosupport bioproductivity and biodiversity;• restoration of the soil productivity potential;• enhancing groundwater resources.

C
O
N
S
E
R
V
A
T
I
O
N

International Institute of the Polish Academy of Sciences

International Institute of the Polish Academy of Sciences RESTORATIONECOLOGICAL ENGINEERING
European Regional Centre for Ecohydrology u/a UNESCORESTORATIONCONSERVATIONECOHYDROLOGYInternational Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

Use of ecohydrology based systemic solutions for reduction of sedimentation,eutophication and dioxin-induced toxicity in reservoir at the the Asella
Bioeconomy Education Centre, Ethiopia
Polish Aid Progamme for Ethiopia (Zalewski, Urbaniak, Negussie 2013)

EDUCATION for sustainable future
System solutions – Transdisciplinary science

WISDOM

Use of biodegradable geofibers for erosion
controll

Holistic

Use of information andknowledge for problem solving –Formulation of principles for action

Use of sediments forbioenergy production

Experimental testing -interdisciplinary science

Stock watering site and use of manure collected
at the site
as a fertilizer

approach

KNOWLEDGE

Sedimentation

Monitoring – sectoral science

Construction of the sequentional biofiltration system
for turbidity, eutrophication and dioxin toxicity
reduction in the Asella BioFarm Park lake

INFORMATION

Understanding structure,states and relationships
Zalewski 2012 changed
Problem solving

Problem identification

Absorption andconversion ofdioxin in the lesstoxic compounds

Toxicity[ngTEQ/kgd.w.]

Reductionistic

Understanding patterns
and processes,

3

Dioxin toxicity reduction in the Asella
BioFarm Park lake2
1
0
Infl Lit Lit Outf

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

International Institute of the Polish Academy of Sciences
European Regional Centre for Ecohydrology u/a UNESCO

The EH demosite at Asella (Ethiopia) Concentration of thedioxins before and after construction of biofiltration system

The EH demosite at Asella (Ethiopia) – effect of the use sediments collected at
biofiltration system for enhencemnt of food production.

BeforeToxicity [ng TEQ/kg d.w.]

2,5

2

1,5

Toxicity limit ccording to
SQG

1

0,5

0
Inflow

Lake

Otflow

Construction of
the biofiltration
system

2,5
toxicity [ng TEQ/kg d.w.]

After

Construction of
the biofiltration
system2

1,5

Toxicity limit ccording to
SQG

1

0,5

0
Sediment trap

Biofiter

Lake

Polish Aid Progamme for Ethiopia - (Zalewski, Urbaniak, Negussie 2013)
Polish Aid Progamme for Ethiopia - Zalewski, Urbaniak, Negussie 2013

International Institute of the Polish Academy of Sciences

European Regional Centre for Ecohydrology u/a UNESCO
The conditio sine qua non of harmonisation of human needs
with biogeosphere carrying capacity for sustinable future is:Thank you

1. Reduction of energy and resource use per unit of GDP by implementation of theconcept „Factor Four” von Weizsäcker
2. Enhancement of ecosystem carrying capacity (CC) - water resources, biodiversity,
ecosystem services for society and resilience (WABIOSER )The important steps should be :-integration of understanding of the dynamics and regulatory mechanisms of the :socio-economic techno and ecological systems,- development of the holistic, transdisciplinary environmental problem solving science ,based on: integration of ecohydrology , biotechnology , engeenering .
- the translation of highly complex transdisciplinary knowledge to decision makers
by decision support models,- the use of foresight methodologies, to develop vision and scenarios for sustainability.
- education , communication and social dialogue.

The inspiring cooperation of my colleagues from
ERCE PAS u/a UNESCO, Department of Applied Ecology, Uà
and UNESCO Division of Water Sciences and IHP
is highly appreciated and made the introduced projects happened

Lecture 1: Fundamentals of Basin-scale Hydrological Processes
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 Water Resources under a Changing Climate
River discharge in Indochina Peninsula region and Japan was projected using a
distributed flow routing model with kinematic wave flow approximation for three climate
experiments: the present climate (1979-2008), the near future climate (2015 - 2044), and thefuture climate (2075-2104). The flow routing model 1K-FRM was fed with generalcirculation model (GCM) generated runoff data for three climate experiments. The GCM
dataset used was the latest 20km spatial resolution general circulation model(MRI-AGCM3.1S and 3.2S), which was developed by Meteorological Research Institute,Japan Meteorology Agency. The changes of flow in Indochina Peninsula region and Japan
under climate change were analyzed by comparing simulated river discharge for the present
climate, the near future climate, and the future climate experiment. Analysis results show
clearly changes of annual mean discharge, mean of annual maximum discharge and mean of
annual minimum discharge with the degree of changes different according to location. Thechanges, which were detected in the near future climate conditions, become clearer in the
future climate conditions. In some locations, the increase of flood and drought risk was found.
Key words: climate change, general circulation model, river discharge, 1K-FRM

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 " 4      - /         -- $    $ -  $
$

$

    $ -   / -         /        ?  -    4  "  

    

 1  -  $-     "  
$          " "    4          $ /$        "  $ /$   /    /   "  "  $  6       

;

$ 

 



  $ $  6$ $ $  $     "  /   

"  " / 

 - 4 $ 4    "    " *4

- -  - 

¯ 3



¯ 3

 * $     -      

 -- -  5 -    -5             5 *  $  $      

          --    $ /$     $ /<$   4   -    


;

  /  "   $ /$       /" 4  "  "  - /     "    6    4 ..%;   -

"           -    
$ 

¯ "    $¯ $  $ 

 "   

【()!  】 !+            -           0  > $-4 3
$  







       "      

4  4 "   4 "    / -$ 4      :4@ *$4

 .A        # × 2 *$  @ A

     / $"  >      $    : × 2 * 6 : ¢ 2 *$ ; B-- >$  .@A"    " 4
 *4  $    A    / C -  " 4      "    $ -   

"4 4   " 4
 222 A   【()! 】 '   

 (

7   $  /  - /  /4     /  *  " 4  

 " 4 /  * 

 $ - $  --    
4  4

   $  -   4 -/+ /  *  "    "      /  /   / $

 : ¢ 2 *$ ¢ 2 .@D  .
# ¢ 2 *$ ¢ 2 @ /  * "    

" 

 

 

$

  /  /   / $

 




 : ¢ 2 *$ ¢ 2 2D .: $
# ¢ 2 *$ ¢ 2 .

 /  * $ - "   /  /   / $   $ -     $ <  

 : ¢ 2 *$ ¢ 2 2222
D % : $$# ¢ 2 *$ 
 

         -  $-   
6  
 





" 4 --    $- $   / $;  "          / $ '&   (E      $      

 3 "     / $ '&(    

$  3 "     / $ '&   (E'

  / 
F D 6
" F     



;F

 "  $    "  4  D  4  D  4

 "      



 

6  ;

  / $ / $ F 7 
D  4 "    / $  / $ 'G (E    / $  3 "   'G   (E  

 

 

  

Water flowing into
the control volume
per unit time
(Min, mass; I, volume㻕#     

 / $ 3 "   'G   (  /      F6    ;  /

D

F4    

/ $ 

<

6 ;



"       >- " / $4

 " $    "  ,         " ,           


/ -

/ $

   "     

"   

/ -    'G   (- 

-  4           

6 ;






 6  ;;F  *   $!                 $   
F D 6

/ 

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

Water stored in thecontrol volume(M, mass; S, volume)    6 ; 0   'G   (4

4   -- 

 

?

4   




'G   ( 1     6 ; /$

 $

D/      $  1   4  







D

6



H

; 

6 ;
 

 $ / - - 

【()! -】.

 $ 

D

6

H

; 

 $   $ / -- 

6 #;
   !!
"     $/      "  $ /$  " $  -     / $"     :   $ -4 







    /   "   -4    

4  4

     -- 

36 (8%)

Vegitation
2

Surface water360

river flow
groundwater
glacier

RunoffOcean
1,400,000









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

! "     * 
/ $



/ $

12

3          $ ! 

! %%&'+  -- -     --   

.% ¢ 2 $  D
# ¢ 2 ¢ 2 @ *$

3

10 m /year    ! #  

12

10 m

Strorage
Water flow

 



Evaporation

RainfallSnow ice
43,400-   

Atmosphericwater on the ocean
11

Air stream

Evapotranspiration

Groundwater
15,300 

  $ /$  / $ 7   Atmosphericwater on the land
4.5
Rainfall    -- -     --   /  /           


2.. $$¡    /  /            

2@ ¢ 2 $  
D @ $$¡ # ¢ 2 ¢ 2 . *$
  --     / $-     --       /  /         

6.% H 2@; ¢ 2 $  
D ..2 $$¡ # ¢ 2 *$ 

   -- 

4 "    $   , -    @

 -- >$  4@22 $$¡ 

 /   -- 

 



-- 

 



 -      -- >$  ::A6D@ I 2@;  $ / - -          
/ -   

【()! /】.

 $   -



 & *  , -  $  '!  ! 

4  4

     / - - 

-  +  / -       / $-   

/ -   $ 

 $   ¢ 2 $  D
# ¢ 2 ¢ 2 @ *$--     "  7   /   "   -4      /<  / -      / $

- -   

/ - 

-  / /        .. $$¡ 

 $   $    /  /         

@ ¢ 2 $  
D %2 $$¡ # ¢ 2 ¢ 2 . *$
  / - - 

/      / $   -     / -  &        

? -   * 

+- 

 $     

6 H @ ; ¢ 2 $  
D ..2 $$¡ # ¢ 2 *$【()! 】.

/    - $       / &  ?      %     

? -     ?    $      /  /      / $    

: ¢ 2 $  
¢ 2 D  $$¡ # ¢ 2 ¢ 2 . *$
  --  $ - 

6@

%2 D  $$¡  ;  ?    --   / - ?    /  /   $$¡ 
D 2 
@ $$¡          

B  4   "    0 6/ " *;-   

/4 

5

  "   "      

 3 "    / $  B  $  

 -  -  5  "       $    'G (E    --       'G  ( 6/ $--  

-   'G  (  $    -   $ -    ;E    / - 
6 / $"  / -    -   $ -    ;E

3 "    $  'G   (   3 "       $     6 ;   ?  

D   3 "         6 ;4      
D 61     6 :;  $ $  D  6 ;

6 ;D

 

   $   5  

;

6 :;



4  D  H    






 



 $


 







1 "  *    $     $    6 ; D  6 ;4   $







 









 







    6 @; 

 

  

.

channel network
catchment boundary       

0   $ - 4     / $   3 "  

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250
Present-day climate experiment
Observed data in APHRODITE
Precipitation [mm]200

Pasak Riverbasin

Chao Phraya
River basin

150

100

50

0

Pasak Dam

2

4

6
Month

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0
0

0

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1600

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 9

Present-day climate experiment
Near future climate experiment
Future climate experiment

Evapotranspiration [mm]

1200

1200
1000
800
600

1000
800
600
400
200

200

0

0
1980

2000

2020

2040

2060

2080

2100

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1980

2000

2020

Year

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2040
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400

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10

  ($):;/'  <+"Present-day climate experiment
Near future climate experiment
Future climate experiment

1400

Precipitation [mm]

100

8



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.
1500

900

700

1000

standard deviation
standard deviation
standard deviation

600

Storage (MCM)

Daily mean discharge [m3/sec]

Observed storage
Near future climate experiment
Future climate experiment

Present-day climate experiment
Near future climate experiment
Future climate experiment

800

500
400
300
200
100

500

0
1

13

25

37

49

61

73

85

97

109

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Class contents

Fundamentals of Basin-scale
Hydrological Processes

Hydrology and water resources
engineering. (first 90minuts)
Water resources under a changing
climate. (after break)Department of Civil & Earth Resources Engineering,
Kyoto University

Yasuto TACHIKAWA

Exercise. Group discussion andpresentation. (after lunch)

1

What is hydrology?

2What is hydrology?
Water vapor evaporated from the ocean andthe land surface; condenses and rainfall
happens; reaches the ground. Someevaporates again and some flows as surface
flow or groundwater flow, and reach to theocean.

Water continuously circulates
around the Earth by means ofsolar energy and gravitationalenergy, changing its phases(solid, liquid, and gas). We callthe circulation as “hydrologic
cycle”.

To understand the cycles of water and energy,
and clarify the relation with human activities
scientifically is hydrology.
3

4

1.1 The science of Water Cycle: Hydrology

Definition of Hydrology by UNESCO International
Hydrological Decade, 1964

Hydrology is the science which deals with thewaters of the Earth, their occurrence,
circulation and distribution on the planet, their
physical and chemical properties and theirinteractions with the physical and biologicalenvironment, including their responses tohuman activity.
Hydrology is a field which covers the entire
history of the cycle of water on the Earth.
5

6What you will study in this class?

1.2 Hydrologic cycle andwater resources

To understand the basic concept of
the hydrologic cycle.
Based of the understanding of thehydrologic cycle, to learn water
resources under a changing climate.

Example 1.1
Topics of hydrologic cycle and water resources

7

1.3 Water on the Earth8

1.4 Water Budget and Water Resources

Example 1.2
The volume of water on the EarthΔS∆S is change in water storage over the period of interest,
I is inflow to the control volume,
O is outflow from the control volume.

9

1.4.1 Water balance equation10

1.4.2 Global water budget

S is storage of the defined region,
I is inputs to the defined region, andO is outputs from the defined region.
11

12

Example 1.3

Example 1.4

Calculate annual precipitation
per unit area
(in height unit, mm).

Calculate annual evapotranspiration
per unit area
(in height unit, mm).

13

14

1.4.3 Catchment water budget

Example 1.5
Calculate annual runoff and runoff
ratio.

15

Example 1.6 Hydrologic Characteristics
RegionPrecipitation (mm)

Evap. (mm)

CPRB (Thailand)

1144

962

182

Jakarta

1800

900

900

Hanoi

1500

800

700

Dhaka

1300

900

400

16

Example 1.7 Annual water resources

Runoff (mm)Yangon
Kyoto

1,796

708

1088

World (mean)

723

480

243

17

RegionPrec.
(mm/year)

Evap.
(mm/year)

Runoff(mm/year)

Area (km2)

Population(person)Thailand

1,144

962

182

513x10369.5 x 106

Japan

1,796

708

1,099

378x103126.5 x 106

World

723

480

243

148x106

6,968 x 106

18

Example 1.8 Mean residence time inglobal water budget

Example 1.9 Mean residence time indam reservoirs
Dam

Storage capacity Catchment Prec
Evap
(106 m3)
area (km2) (mm/y) (mm/f)

Runoff(mm/y)

BhumibolDam

13,42026,400

1,144

962

182

Tokuyama
Dam

660

254.5

1,796

708

1088

Hiyoshi
Dam

66

290

1,796

708

1088

19

20

Water Resources under Changing
Climate
2.1 Climate Change Scenario
and GCMs
Department of Civil & Earth Resources Engineering, Kyoto
University

Yasuto TACHIKAWA

GCM Projection data for runoff simulation

 Input data

Precipitation (hourly)

Transpirationfrom rootzone (daily)

Present climate experiment: 1979-2008

Near future experiment:
2015-2044
20 km resolution future
Future climate experiment: 2075-2104
climate simulation data,
MRI-AGCM3.2S made by Morological Research Institute, Japan.

Canopy layer
Evaporationfrom soil
layer (daily)

RESEARCH METHODOLOGY

Snow
accumulation
Snowmelt(daily) River flow routing model
Precipitation (daily)

for estimating river flow dischargeusing the future climate data such as
rainfall, evaporation and so on.

Surface runoff generation (daily)
Sub-surface runoff generation (daily)

River Discharge

Soil layer

23 Analyzing
the changes river flow discharge for the assessment of
24
water resources, flood risks and drought risks.

Flow Routing Model using DEMs
1K-FRM/DHM

2.2 Water Resources
Simulation in Thailand

25

26

Changes ; Basic meteorological parameters(a)Rainfall …

Changes ; Basic meteorological parameters(a) Potential evaporation

Percentage difference of the average annual rainfall with respect topresent climate

Percentage difference of the average annual daily potential
evapotranspiration in near future and future climate with respect to thepresent climate

http://hywr.kuciv.kyoto-u.ac.jp/products/1K-DHM/1K-DHM.html% Percentage Difference = 100*(Near future-Present)/Present

1% to 5% decreases of annual
precipitation can be observed in thecentral region of Thailand and 1% to
10% of increase in annual precipitation
can be observed in southern region in
the near future climate (2015-2039).
Future climate conditions, it isexpected to have 1% to 10% of
increase in annual precipitation innorth mountainous region and 5% to
10% increase in northeast region.

Results ; Flood…
NearFuture

Pr esent

Mean Annual
Maximum
Flood

FutureIt can be clearly noticed that the potential evappotranspiration will increase by
4% to 6% in near future climate and 4% to 10% in future climate.

Results ; Water Resources
Pr esent

2.3 Water Resources Simulation
at the Pasak Dam

Change of Future Precipitation andEvapotranspiration Projected by MRI‐GCM3.1S

Evapotranspiration

Precipitation

Projected Inflow to the Pasak Dam Reservoir

Projected Pasak Dam Storage under aChanging Climate

Elapse Month

Runoff Simulation for the EntireJapan

2.4 Water Resources Simulationin Japan• Five regions were set up for
runoff simulation under achanging climate.

Hokkaido

East Japan

Central Japan
KyushuWestern Japan36

Change of 100‐year annual maximum hourly discharge
Flood Risk
Near future climate/Current climate

Future climate/Current climate

2.5 River Flow Simulation in
Indochina Peninsula

Quintiles of river discharge with 100‐year return period
estimated using GEV distribution.
37

109.5o E
34.0o NSTUDY AREA

Change Ratio of Mean of Annual Discharge

SalweenRiver basin
Irrawaddy
River basin

Red Riverbasin

Mekong Riverbasin
Chao Phraya
River basin
39

91.0o E
5.0o N

Change Ratio of Mean of Annual Maximum
Discharge

Near Future Climate to Present Climate

Future Climate to Present Climate

Near Future Climate to Present Climate

Future Climate to Present Climate

Change Ratio of Standard Deviation ofAnnual Maximum Discharge

Near Future Climate to Present Climate

Future Climate to Present Climate

42

Change Ratio of 10‐year Annual Maximum
Discharge

Near Future Climate to Present Climate

Future Climate to Present Climate

Findings
 Clear changes of flood peak discharge,
and monthly discharge were detected;
 The degree of the discharge changes
differs according to location; and The changes appear in the near futureclimate experiment, which become clearer
in the future climate experiment.

Change Ratio of Mean of Annual Minimum
Discharge

Near Future Climate to Present Climate

Future Climate to Present Climate

Summary
1) A regional distributed rainfall‐runoff model for flood
prediction was introduced and a simulation result of 2011
flood in Thailand was presented.
2) A river flow projection at the Chao Phraya River basin
under a changing climate was presented.
3) A water resources simulation for the Pasak Dam
reservoir storage under a changing climate wasdemonstrated.
4) A preliminary river flow simulation for the entire
Indochina region using the latest climate change projection
data was introduced.

Lecture 2: Projected Future Meteorological Environment
Eiichi NAKAKITA (Division of Atmospheric and Hydrospheric Disasters, DPRI, Kyoto University)
Climate change impact assessment on natural hazard such as flood, landslide, strong wind, storm
surge and high wave had been realized under the Innovative Program on Climate Change Projection forthe21st century “Kakushin Program”, supported by the Ministry of Education, Culture, Sports, Science andTechnology. The Kakushin Program was followed by a Program for Risk Information on Climate Change“Sousei Program”, which aims to create risk information more aware of social adaptation to climate change.
The lecture includes what kind of impact assessment in the Kakushin program has been realized, and what
the risk information which aims at the Sousei Program is. (http://www.jamstec.go.jp/sousei/eng/index.html)
DPRI is performing prediction and evaluation of disaster environment in Japan with the sub-project
title of “Integrated assessment of climate change impacts on watersheds in a disaster environment”. Impact
assessment on disasters such as landslides, debris flows, floods, droughts, storm surges, and strong winds.
Moreover, flood risk assessments are being extended to a global scale for cooperation with international
projects on disaster mitigation by ICHARM, PWRI.
One of the important subjects in this sub-project is the interface between GCM and RCM, andvarious models on natural hazard. For example, MRI has changed its GCM and RCM so that they could
output hourly rainfall from GCM, 30-minutes rainfall from 5-km and 2-km RCMs and 10-minutes rainfall
from 1-km RCM for the use of hydrology-related various hazards and disasters.
As an example of flood risk assessment, the magnitude of 100-year flood for near future and the endof the 21st century were estimated using 1-km spatial resolution distributed hydrologic model with the inputof the climate projection computed by the 20-km model. As other examples, changes in frequency oftyphoon generation, approach to Japan, and heavy rainfall occurrence.
However, there are some important points which should be investigated and be thought more before
moving on to adaptation research; (1) GCM and RCM with the super-high spacio-temporal resolutions (20km-1 hour) makes it possible to evaluate extreme hazard (ex. Max. discharge).However, this does not meanthat we can evaluate the changes in such a high spatial resolution. (2) We can get approximate projection onchanges in return period of extreme events. However, there is a risk that the return period does not haveenough accuracy. (3) Also, there is no guarantee that quite extreme events could be projected within thelimited number of ensembles as GCM and nested RCM output. In this sense, it may be difficult to establish
design hazard for water management and so on. (One of the ideas to overcome this for assessing storm
surge will be separately presented.) (4) On the other hand, the risk management deals with phenomena
beyond design hazards. In this sense, it is very important to take into account the result from the worst casescenario as a one of the forcing for risk management on climate change. (One of the ideas of producing theworst case scenario will be presented.)
Taking into consideration above items, I think, it is very and more important for climate change
adaptation to discriminate between planning with uncertain design level and risk management with theworst case scenario.

Outline• Climate change impact assessment
Projected Future Meteorological Environment

Eiichi Nakakita
Atmosphere Hydrosphere Research Division
Disaster Prevention Research InstituteKyoto University
Japan– Impact of AGCM20 on extreme events
climate impact assessment in Japan– Typical climate change assessment on
disaster environment in Japan – projection ofchange in design value– Heading to adaptation :importance of taking aworst case scenario into consideration.

• Real-time ensemble weather forecast

- Disaster Prevention Research Institute (DPRI), Kyoto University- International Centre for Water Hazard and Risk Management (ICHARM),
Public Work Research Institute (PWRI)

Kakushin Team 3

Projection of changes in extremes in the futurewith very‐high resolution atmospheric models
uncertain
information

A． Projection
Extreme event projection byvery high resolution
atmospheric models

data

Meteorological Research Institute (MRI)
Japan Meteorological Agency (JMA)
Advanced Earth Science &Technology Org. (AESTO)

B. Uncertainty
Evaluation and reduction of
uncertainty in global warming
projection
Meteorological Research Institute (MRI)
Japan Meteorological Agency (JMA)
Advanced Earth Science &Technology Org. (AESTO)
uncertain
information

C. Risk assessment
・ Prediction and evaluation of disaster environment
Disaster Prevention Research Institute (DPRI), Kyoto University

・ Assessment of climate‐change impacts on flood risk
and its reduction measures on global and local scales
International Centre for Water Hazard and Risk Management (ICHARM),
Public Works Research Institute16

Extreme event projection by very high resolution atmospheric models

Evaluation and reduction of uncertainty in global warming projection

MRI / JMA / AESTO

MRI / JMA / AESTO
Evaluate, quantify and reduce uncertainty due to SST and cumulus convection scheme by ensemble simulations

AtmosphereOcean model
180km mesh

High-resolution
global atmosphericmodel
20km mesh

Regional cloud
resolving modelby nesting
5km, 1km mesh

East Asian
rainy season

1. 60km-mesh global model
East Asian rainy season
Tropical cyclone (TC) number and truck

2. 5km, 1km-mesh cloud resolving
regional modelTC generation and intensity
Number of TCs

Atmos
phere
SST
Ocean

Predicted
SST

Global & hemispheric numbers of TCs
as simulated in the 20-km-mesh AGCM

Atmos
phere

SST
Boundary
condition

100-50km mesh

Present
SST=Sea Surface
Temperature

100

Boundary
condition
Future(AJ)

Present: MIROC SST

(AX)

80

Future: obs SST + MRI ΔSST

(AK)

60

Future: MIROC SST

Future: obs SST + MIROC ΔSST (AS)
(AY)

40

Near
Future20
0SST
1979-2003 2015-2039

Present: obs SST

2075-2099

Year
17

GLOBAL

N. H.

S. H.

18

Extreme event projection by very high resolution atmospheric models
MRI / JMA / AESTO

AtmosphereOcean model
180km mesh

High-resolution
global atmosphericmodel
20km mesh

Predicted
SST

Atmos
phere

Atmos
phere

SST
Boundary
condition

SST
Ocean
100-50km mesh

Present
SST=Sea Surface
Temperature

Trend byAR4 model

SST condition for preliminary experiment.
AR4 Models A1B
Experiment2001 -

Obs.SST AMIP
1979 - 2003

Regional cloud
resolving modelby nesting
5km, 1km mesh

Boundary
condition
FutureNear
FutureSST
1979-2003 2015-203920 2075-2099

Physically-based down scale by cloud
resolving model (Regional Climate Model)

Increment

ΔT

AR4 models 20th
century experiment

RCM-5km（long-term
integration）- 2000Current 19792003

Future2075

25 years period

Temperature
increment by
AR4 Models

Remove trend from obs. SST
1979 – 2003

Trend by AR4 Models
2075 - 2099

＋

＋

ΔT

2099

25 years period＝（2075 - 2099）-（1979 - 2003）

21

Evaluation and reduction of uncertainty in global warming projection
MRI / JMA / AESTO
Evaluate, quantify and reduce uncertainty due to SST and cumulus convection scheme by ensemble simulations
East Asian
rainy season

1. 60km-mesh global model
East Asian rainy season
Tropical cyclone (TC) number and truck

2. 5km, 1km-mesh cloud resolving
regional modelTC generation and intensity
Number of TCs
Global & hemispheric numbers of TCs
as simulated in the 20-km-mesh AGCM

100

Present: obs SST(AJ)Present: MIROC SST

(AX)

80

Future: obs SST + MRI ΔSST
(AK)
Future: obs SST + MIROC ΔSST (AS)60

Future: MIROC SST

(AY)

40
20
0
GLOBAL

N. H.

S. H.

23

RCM-1km （many heavy
rainfall events）

Comparison
between the
scale

Year

Southeast Asia

East Asia
KAKUSHIN
20-km mesh,hourlyKAKUSHIN
20-km mesh, hourly

Precipitation (mm/h)

Dirnal cycle can be cleary
seen by the hourly output

Precipitation (mm/h)

It is the typhoon resolving output
that has realized the impact
assessment of Japanese disasters

NCAR/NCEP
200-km mesh, 6-hourly

Sea Level Pressure (hPa)

Projection by AGCM20
Annual averaged
temperarute(ºC)

Near Future（2015-2039） End of the 21 Century（2075-2099）

Future－Present

Annual Rainfall(mm)

Near Future （2015-2039） End of the 21 Century（2075-2099）

Future/Present

Tanaka et. al, 2009

Changes in annual maximum weekly rainfall

Occurring month of Max. D [days] rainfall
GCM Present 1979‐2003

100 Yr. Rainfall
Present

End of the 21st Century

Flash
Floods
3 hours

Short- rain
Floods1 dayLong- rain
Floods15 days
Konoshima and Nakakita, 2009

GCM Future 2075‐2099

20km mesh Future changes in 100-year annual max→
daily 20km averaged rainfall.
100km scale
100‐yearrain

Statistical test on changes
in max. value. (Future – Present)
20km

GCM Future ‐ GCM Present   

100km

Flash
Floods
3 hours

Short- rain
FloodsIn what spatial scale can we statistically say
OK ?

1 day

Long- rain
Floods15 days
Konoshima and Nakakita (2009), Kyoto University.

Konoshima and Nakakita (2009), Kyoto University.

20km mesh Future changes in 100-year annual max→
daily 20km averaged rainfall.
100km scale

Statistical test on changes
in max. value. (Future – Present)

100‐year
rain

20km

GCM Future ‐ GCM Present   

100km

Flash
Floods
3 hours

Short- rain
FloodsIn what spatial scale can we statistically say
OK ?

1 day

Long- rain
Floods15 days
Konoshima and Nakakita (2009), Kyoto University.

Konoshima and Nakakita (2009), Kyoto University.

Points in climate change assessment on
Japanese hazard

Points in climate change assessment on
Japanese hazard• There are various types of hazards thatbring disasters.
• Spacio-temporal information with highresolution is required for representing
reasonable extreme river discharge inJapan.

• There are various types of hazards thatbring disasters.
• Spacio-temporal information with highresolution is required for representing
reasonable extreme river discharge inJapan.

Features of Japanese River(1)Features of Japanese River(2)• Short length and steep slope.

• Large peak discharge, short duration

Height from sea level (m)Tone River

The Rhine R.

Shinano R.
Jyoganji R.
Shinano R.

Mississippi R.

Colorado R.

Tone River

The Rhine R.

The Seine river
Kitakami R.

Mekong R.

Chikugo R.

Tennessee R.

Duration (day)Distance from river mouth (km)Importance of hourly data in Japanese river basin⇒ Peak discharge is underestimated by near 50% when we use daily data
⇒ We need hourly rainfall for all Japanese river basins
Discharge at Hirakata
4500(Lumped, Hourly)(Lumped, Daily)

4000

1-km mesh10-minuteDischarge [m^3/s]

3000

5-km mesh30-minute

Precipitation(mm/h)

2500
2000
1500
1000
500
0
170

Very important for assess
initiation of land slide anddebris flow

NESTING

3500

By Masuda et al. (2006)

Kansai Dist.

175

180
185
Day of 1999

190

195

Projected typhoon by GCM20

It is the typhoon resolving output from GCM20 that hasrealized the impact assessment on Japanese river regime
41

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

Outputfrom GCM
and RCM

Mountains

River

Habitable Area

Spacio-temporal scale

Coastal Area

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）

大河川での洪水、大規模水害、土砂災害
2009/08/08 in台湾

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

Surface hydrological modelInterpreta‐tion ofoutputTyphoonRange：1000km
Duration：１day to a few days

Projected by AGCM20

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

Sedimentation andtransportation of soil

Rainfall runoff

River channel flow

Storm surge

Soil runoff

Building damage by strong wind

Evaluation

Reservoir operation

Inundation including
underground shopping
mole

Soil production

Various
Models（with long‐term run）

台湾中央気象局、台湾国家災害防救科技中心

Localized heavy rainfall (Baiu season)
Range：100km
Duration：6 hours to half a day

ShowerRange：10 km
Duration：about half an hour

中・小河川での洪水、内水氾濫、土砂災害
2010/10/20 in奄美

小河川や下水道内での鉄砲水、都市内水氾濫
2008/07/28 at都賀川 2008/08/05 at雑司ヶ谷

Projected by RCM

Impossible?

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

南日本新聞 OFFICIAL SITE

都賀川モニタリング映像

共同通信

Rainfall output from GCM and RCM• GCM20 (Hourly rainfall, Globe)– Extreme rainfall and Ocean wave in the world– Major and all Japanese rivers basins• RCM5 and RCM2 (30 minutes, Around JapaneseArchipelago)– Inundation in major metropolitan areas– Land slide, debris flow
– Major Japanese river basins• RCM1 (10 minutes rainfall, Piecewise sections inJapanese Archipelago)– Inundation in major metropolitan arears– Land slide and debris flow
– Strong wind hazard

Stochastic typhoon model

Methods of Impact assessment
Output from GCM and/or RCM■Hydrological Regime, Ocean Wave
Direct and Continual Utilization ofTime-series of GCM/RCM outputs■Strom Surge, Land Slides, Inundation
Statistical Evaluation of Extreme forcing
Design rainfall, Design typhoon

Hazard models
Run-off ModelOcean Wave Model

Hazard models
Storm Surge Model
Land Slide Model, Inundation Model

Evaluation of changes in hazards

Evaluation of changes in hazards

Evaluation of Changes in Disaster Risks

Evaluation of Changes in Disaster Risks

Proposal of Adaptation Measures

Typhoon Numbers/yr: Present - Future
Yasuda et al (2009)

0.08

Ise (present)
pdf (present)Ise (future)pdf (future)

Ise Bay

0.08

0.04

0.06

0.04

0.06
0.05
0.04
0.03
0.02

0.02

20

40

60

80

0
0

100

20

40

Number

100

0.04

0.02

Taiwan (present)
pdf (present)
Taiwan (future)pdf (future)

Taiwan

0.04

0.06

920

0.04
0.03
0.02
0.01

940

960

0
880

980

0.03

0.02

900

920

940

960

980

Central Pressure [hPa]

Tokyo(present)
pdf(present)
Tokyo(future)pdf(future)

Tokyo Bay

0.07
0.06
0.05
0.04
0.03

>920hPa

0.02

0.01

>920hPa

0.05

0.08

Probability

Tokyo (present)
pdf (present)
Tokyo (future)pdf (future)

Tokyo Bay

900

0.05

Central Pressure [hPa]0.05

Probability

Probability

80

Number

0.1

0.08

60

0
880

Ise(present)
pdf(present)Ise(future)pdf(future)

Ise Bay

0.06>920hPa

0.02
0.01

0
0

0.07

Taiwan(present)
pdf(present)
Taiwan(future)pdf(future)

Taiwan0.03 >920hPa
0.04

Probability

0.06

0.08

Osaka(present)
pdf(present)
Osaka(future)pdf(future)

Osaka Bay

0.07

Probability

Osaka (present)
pdf (present)
Osaka (future)pdf (future)

Osaka Bay

Probability

0.1

0.08

Probability

Probability of center pressure

Probability of typhoon attack for 100yrs
return period
Probability

0.1

0.02

0.01

0.01

20

40

60

80

0
0

100

Yasuda et al. (2009), Kyoto
University.
Number

100

200

300

400

NumberIncrease in Number of localized heavy rainfall
during Baiu season in 25 years
1 6

24
76

100

3 8
10

20
30

17 27
28

10
44

22

60 5

21
16

38

16

Current

920

940

960

0
880

980

900

920

940

960

980

Central Pressure [hPa]-Total rainfall versus maximum hourly rainfallTop 20 data of total rainfall andrisk ofmaximum hourly rainfall fromshallow landslide
Takeda City, Oita, Japan

5

5

900

Impact Assessment on Land slides

Lager Increase in western Japan

increase

0880

Yasuda et al. (2009),
Kyoto
University.
CentralPressure[hPa]

Future95％ significance
90％significanceNo significanceNakakita et al (2011)100
Rainfall intensity [mm/hr]

00

1976-1980
1981-1985
1986-1990
1991-1995
1996-2000
2001-2005
2006
2007
2008

80
60
40

surface soil layer

Japan(2011)
rockshallowlandslidedeep landslide
Schematic from PWRI Report No.4129 (2009)

20
0
0

200

400

600

800

1000

risk ofdeep landslide

Total rainfall [mm]Taiwan (2009)

Oku et al (2011)

Projected changes in total and maximum hourly rainfall in Japan

Maximum hourly precipitation

Future - Present

Color: 95% significancebased on 60-km model
ensemble experiments

Fut.－Pres.

Risk of shallow land slide%

Increase by 10〜20％ in whole area

Increase of strong
and large rainfall

95%

Increase of both theshallow and deep
landslide risk
Near Future - Present

increase

Sgn.

Risk of deep land slide%

Increase by 10〜20％ in mid and western Japanfrequency

95%

Sgn.

decrease

Oku et al (2011)

Total precipitation

Oku et al (2011)Introducing reservoir operation models into
distributed runoff model

Design value

System of distributed runoff model

Range for disaster Mitigation

River discharge
Storm surge
Design value(Return period)

Reservoir operation model

Example of combined computation

Range for disaster Prevention

Sayama et al (2008)

Possible changes in the number of floods requiring dam operationand emergency dam release (Yodo River)River discharge
Flood flow change(Q1: Annual Maximum discharge)
100yrs return period

80

Draught flow change(Q355 discharge)
10yrs return period

Number of floods withdam operation

70

Number of floods

60

Number of floods withemergency dam
release

50

40

30

20

10

0

1981-2000

2031-2050

2081-2100

Sayama et al. (2008), Kyoto University.

Influence of changing in snowfall and snow melt
(Mogami River: Japan seaside in Tohoku)
Present

Will dam’s network work in future?
Water Resources atTone-Ozeki (6058.8 km2)

Snowmelt

No Dams•Produce spring flooding•Supply irrigation water for paddy filed

Snowaccumulation

With Dams

FutureShift of season is vital not only for
agriculture but also for regional
cultureWater Demand•••

Tachikawa et al., 2008, Kyoto University

Living water: 37.43 m3/s
Industrial water: 2.08 m3/s
Agricultural water:
Apr~May 39.51 ~ 60.99 m3/s
May~Sep 111.62~186.71 m3/s

Kim et al., 2010, Kyoto University

There is high uncertainty inprojected design value

100-years return values of Storm surge(deviation from the average year value)
Current

End of Century
Seto Inland Sea

Suo-nada

AkiItsukinada
nada

Range for disaster mitigation(including large scale disaster)(unit: m)HarimaOsaka
nada
Bay

Hiuchinada

There is high uncertainty inprojected design value

Design value
by return value

Tokyo
Bay

Ise
Bay

SurugaBay

MikawaBay
Enshunada

Kumanonada

SagamiBay

Boso
Peninsula

Projected design value
It is almost certain that averageof design value would increase.

Range for disaster prevention
Izu
Islands

Mase et al (2011)

Uncertainty inherent to GCM projection
1. Uncertainty in cumulus parameterization
Projected
2. We
only usevalue
single 25-years time-series of output forst century
present, near future and endof the 21
RandomUncertainty––

Is this enough number of years as statistical sample? ＝＞ReModel’s uncertainty
sampling or ensemble projection will be needed.
Quite extreme event may not occur within
25-yearsuncertainty
time series
CO2 single
Scenario's＝＞Worst scenario experiment is required!

Accuracy of estimated annual max. discharge
Accuracy of 100 years return value (Jackknife method)
With 25-years single time series
Present

Change in Annual
maximum Discharge

Normalized
standard deviationof projection

Near future

Near Future/Present
End of century

3. Uncertainty in CO2 release scenarios–

We are using most likelihood A1B scenario

End of the Century/PresentTime
The larger the projected value is, the larger the standard deviation is.

Schematic ofreturn value’s uncertainty
AMS

High uncertainty:
extreme events,
flood, land slide

GEV
Obs.
Model A
Model BLow uncertainty:
agriculture,
water resources
Return value canbe used as designlevelWith 25-years single time series
Konoshima and Nakakita (2010)

Return value can
NOT be used as
Design level

Model A

Obs. Can RCM reduce

the uncertainty
and bias?
Model B

Outline• Climate change impact assessment– Impact of AGCM20 on extreme events climate
impact assessment in Japan– Typical climate change assessment on disasterenvironment in Japan – projection of change indesign value– Heading to adaptation :importance of taking
a worst case scenario into consideration• Real-time ensemble weather forecast withhigh resolution

There is high uncertainty inprojected design value

Super Typhoon
by Dynamic DS

Super Typhoon projected by
CReSS (Storm Resolving Storm
Simulator)• We may be almost sure that average of extreme design
value would increase.
• However, projected increase in the design value is merely
rough estimation,• because, for example, the worst case typhoon for a
specific river basin may not be realized (computed) in asingle projected time series.
• Therefore, it is very important to estimate river discharge
when a worst case typhoon would pass through, eventhough we cannot estimate return period.
Tsuboki et al. 2009, Nagoya University

GCM20-Projection for Aug., 2076

SF0508 projected by GCM20 + CReSS
Total rainfall amount: 500～800mm

Animation by Konoshima and Nakakita (2010)

Tsuboki et al., 2009, Nagoya University

Virtual Shifting of typhoon’s initial position- for making a worst scenario NHM-5km

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

AGCM20

Dynamic downscaleby RCM

Worst case impact assessmenton• Land： extreme wind and rainfall• Ocean： storm surge and wave height

Ishikawa et al (2009)hourly precipitation

daily precipitation
Ｉｓｈｉｋａｗａ ｅｔ ａｌ．2009

Simulation of River Discharge using
Precipitation Output （Tone River Basin）
AGCM20Main 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(listed from the top)

mm/hTone RiverBasin“worst track”
Central Tokyo
Ｉｓｈｉｋａｗａ ｅｔ ａｌ．2009

River Discharge by the virtual shifting of typhoon
which was projected by GCM
River discharge（ton/sec)

Possibility of peak discharge, almost
double of current design discharge

Track of a typhoon
projected by GCM20

Current design
value

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

Oku et al (2009)

Heading to adaptationa Worst case scenario
Survivability Critical, Edge of Survivability
Range for disaster mitigation(including large scale disaster)

There is high uncertainty inprojected design value(crresponding to 200
return period)

Design value
by return valueTime

Tone-Ozeki
Kurihashi

Tokyo

Track of virtually shifted
typhoonProjected design value
It is almost certain that averageof design value would increase.

Range for disaster prevention

Oku et al (2009)

Summary (1)Summary (2)1. The AGCM and RCM with super-high spatio-temporal
resolutions (20 km-1 hour) made it possible to evaluate
extreme hazard (ex. Max. discharge).
2. However, this does not mean that we can evaluate thechanges in such a high spatial resolution.
3. We can get approximate projection on changes ofreturn values of extreme events.
4. However, there is a risk that the return period does nothave enough accuracy because there is no guaranteethat quite extreme events could be properly projected
within the limited number of ensembles. (Single timeseries output from the AGCM20 and RCM)
5. In this sense, it may be difficult to project correct
design hazard for water management and flood control
so on.

5. On the other hand, the risk management deal withphenomena beyond design hazards. In this sense, it isvery important to take into account the result from aworst case scenario as one of the forcing hazard fordisaster risk management under climate change.
6. Taking into consideration above items, I think, it isvery important for climate change adaptation todiscriminate more between planning with anuncertain design level and risk management with aworst case scenario.
7. Of cause, making the number of ensembles increase isessential for the Kakushin follow-up program.

Launching of Sousei Program• Kyousei(共生)Program:2002-2006– 20kmRCM• Kakushin(革新)Program:2007-2011– 20kmGCM, 5,2,1kmRCM– Natural Disaster (Inc. water resources)

Sousei (創生) Program (2012-2016)
We have been harvesting mutual
Situation of output fromand respect among
Ultimate Goal understanding
Japanese civil engineers,
Climate models
meteorologists and climatologists
Sosei Program (post Kakushin) is thestage to take impact assessment as
issue with the first priority under themutual understanding among
Japanese researchers.

Groups in SOUSEI Program

under the Kakushin Program, by
hot discussions. (2007-2011).

Sosei• Sousei(創生)Program:2012-2016– Impact assessment and producing
adaptation methodologies (First priority)– for Natural Disaster, Water resources,
Ecosystem and Eco service (Kyoto
University will lead the nation wide
assessment team)

Post Sosei

Kakushin(2007-2011)
Topics:Natural hazard, Water Resources
and Ecosystem and Eco-service

For adaptation decision making
Deterministic, Probabilistic and Beyond

Key issues in SOUSEI(Program for Risk Information on
Climate Change)• Generating PDF of extreme values with higher
accuracy• A: Prediction and diagnosis of imminent global
climate change (Univ. of Tokyo)• B: Climate change projection contributing tostabilization target setting (JAMSTEC)• C: Development of Basic Technology for Risk
Information on Climate Change (MRI)• D: Precise impact assessments on climatechange (Kyoto Univ.)• Proposing adaptation philosophy consistent
with mitigation philosophy

Precise impact assessments on climate change(PI: E. Nakakita, KU (Kyoto University))• i. Climate change impacts on natural hazards (KU)
i‐a Risk assessment of meteorological disasters under climate change (T. Takemi, KU)
i‐b Risk assessment of water‐related disasters under climate change (Y. Tachikawa, KU)
i‐c Risk assessment of coastal disasters under climate change (N.Mori, KU)
i‐d Measuring socio‐economic impacts of climate change and effectiveness of adaptation strategies(H. Tatano, KU)– i‐e Development of risk assessment and adaptation strategies for water‐related disaster in Asia (S.
Tanaka, ICHARM, PWRI)

–
–
–
–

– Generating of PDF using a lot of 60km ensemble– Converting extreme values in 60km‐scale intovalues in regional‐scale using RCM5 and RCM2
daynamically dounscaled from GSM20.

– Developing decision making methodology underhigh uncertainty of risk
– Developing decision making methodology underno information on probability of a worst case

Sousei (創生) Program (2012-2016)
Proposing adaptation philosophy
consistent with mitigation
philosophy

Generating PDF of extremewith higher accuracy
Ultimate Goal values
Generating of PDF using a lot

Developing decision making
methodology under highuncertainty of risk
Developing decision making
methodology under noinformation on probability of aworst case

Sousei (2012-2016)• ii. Climate change impacts on water resources (K. Tanaka, KU)– ii‐a Assessment of socio‐economic impacts on water resources and their uncertainties underchanging climate (K. Tanaka, KU)– ii‐b Assessment of climate change impacts on the social‐ecological systems of water resources andhydrological cycles (T. Oki, UT)• iii. Climate change impacts on ecosystem and biodiversity (T. Nakashizuka, TU)– iii‐a Assessment of climatic impacts on ecosystem and biodiversity (T. Nakashizuka, TU)– iii‐b Economic evaluation of ecosystem service (S. Managi, TU)– iii‐c Eco‐climate system in Northeastern Eurasia and Southeast Asian
tropics: impacts of global climate change (T. Kumagai, NU)– iii‐d Assessment of multiple effects of climate change on coastal marine ecosystem (Y. Yamanaka,
HU)

Post Souseiof 60km ensemble
Converting extreme values in
60km-scale into values inregional-scale using RCM5
and RCM2 dynamicallydownscaled from GSM20.

Kakushin(2007-2011)
Topics:Natural hazard, Water Resources
and Ecosystem and Eco-service

For adaptation decision making
Deterministic, Probabilistic and Beyond

Outline

ConclusionsA) It should be emphasized that building up
a new philosophy of adaptation for the
climate change is very important.
B) It is also important to really re-recognizethat improvement of understanding andreal-time-forecasting accuracy of extreme
weather should be one of the majorcountermeasures as adaptation.
85

Heavy rainfall by typhoon(T1112)• Climate change impact assessment– Impact of AGCM20 on extreme events climate
impact assessment in Japan– Typical climate change assessment on disasterenvironment in Japan – projection of change indesign value– Heading to adaptation :importance of taking aworst case scenario into consideration• Real-time ensemble weather forecastwith high resolution

Heavy rainfall by typhoon(T1112)（出典元：気象庁）

What is ensemble forecast
12000

Ensemble Forecast
Each Ensemble Member
Discharge of Contol Run

10000

discharge (m3/sec)• Ensemble forecast– Numerical weather prediction with highresolution (20km)– Numerical weather prediction with super-highresolution (2km)

8000

6000

4000

2000

0
09/01 09:00

Real-time weather forecast

09/02 09:00

09/03 09:00

09/04 09:00

time (hour)

A number of trials (samples) of forecast, by which we can estimate statics offorecast such as mean, variable (uncertainty) and so on.

• Latest weather radar with high spaciotemporal resolution

Weather forecast with 20km NWP
Radar observation

Ensemble weather forecast for a week

Forecast by GSM

Observation

Forecast

Hourly rainfall
09:00
Sep. 1

By J-Power (2012)

Time

12:00
Sep.2

12:00
Sep.3

12:00 12:00
Sep.4 Sep.5

12:00
Sep.6

12:00
Sep.7

12:00 12:00
Sep.8 Sep.9

By Oishi (2012)

Real-time weather forecast
2km ensemble rainfall forecast
Observation Radar

m01

m02

m03

m04

m05

p01

p02

p02

p03

p04

p05

Ensemble
Mean• Ensemble forecast– Numerical Weather Prediction with highresolution (20km)– Numerical Weather Prediction with super-highresolution (2km)• Latest weather radar with high spaciotemporal resolution

09/03/09:30 JST~ 09/04/09:00 JST
94

Real-time weather forecast•

Hydrological application of the ensemble rainfall forecast– The temporal distribution of Hydrograph generated by the ensemble are similar to the radar• Ensemble forecast

and dam inflow data, although it is underestimated at peak value.

– for a week with Numerical Weather Prediction– For 31 hours with Numerical Weather
Prediction

F
Futatsuno
Dam catchmentt
12000

Ensemble Forecast
Ensemble Mean
Each Ensemble Member
Discharge of Contol Run
Discharge of Radar Rainfall
Dam Inflow

rainfall intensity (mm/hour)

10000

8000
discharge (m3/sec)

0
20
40
60

6000

4000

• Latest weather radar with high spaciotemporal resolution

2000

0
09/01 09:00

By Yu (2012)09/02 09:00

09/03 09:00

09/04 09:00
09/05 09:00
time (hour)

09/06 09:00

09/07 09:00

95

C and X band operational network by MLIT

Polarimetric Doppler Radar

C‐band Radar
Obs.: every 5 min
Distrib.: 5‐10 min
X‐band Radar
Obs.: every 1 min
Distrib.: 1‐2 min
26 C-band Conventional
1 C-band full Polarimetricsince April 2009
•

•

•
•

This kind of weather radar can observe 3D structure of the rainfall and the windin the rainfall system.
This can observe various polarimetric parameters. (e.g. differential reflectivity
ZDR, correlation coefficient ρHV, differential phase shift KDP)
We can estimate size, shape and type of hydrometeors.
These information will bring us not only a higher QPE accuracy but also ahigher QPF accuracy through data assimilation by micro-physical processmodel.

New operational network by X-band MP radars
60 km range
30 km
range

Kyoto

Kobe
Osaka

By the River Bureau in MLIT

11 X-band Doppler Polarimetricsince April 201017 X-band Doppler Polarimetricsince April 2011

1,000 km

2 C-band Doppler full Polarimetricwithin 1-2 years

Copy: MLIT (Ministry of Land, Infrastructure, Transport and Tourism)

QPE and QPF by the new network•Higher sensitivity by :X band radar•Higher spatial resolution by :X band radar (250~500 m)
Dense network•Free from attenuation by :Polarimetric function (KDP)
Dense network together with Cband•Higher QPE accuracy by :Polarimetric function (ZDR, KDP)
•Shorter scan interval with low
elevation :1 minutes•Shorter transmission time :2 minute•3D image :Volume scan

New operational networks by X band polarimetric Doppler radars have been be
in operation in various urban areas in Japan.

Automatic 3D detection and tracking system of baby cell aloft
has been developed by Kyoto University.

Surveillance of natural dam breaking

Copyright 2011 by Nakakita

Outline• Climate change impact assessment– Impact of AGCM20 on extreme events climate
impact assessment in Japan– Typical climate change assessment on disasterenvironment in Japan – projection of change indesign value– Heading to adaptation :importance of taking aworst case scenario into consideration• Real-time ensemble weather forecast withhigh resolution101Lecture3: Fundamentals of Freshwater Ecology

Yasuhiro TAKEMON (Water Resources Research Center, DPRI, Kyoto University)
River ecosystems provide us various “ecosystem services” including water supply, food provision, water
purification through nutrient cycling, transportation of sediment and materials, flood regulation by water
retention in the floodplain, and supporting recreation, culture and environmental education. In spite of thesevalues, the ecosystems have been deteriorated under severe human impacts all over the world. In order to sustain
those ecosystem services for the next generation, we should change ways of basin management to those enablingmuch wiser use of the services based on scientific knowledge of freshwater ecosystems.
In this lecture, several basic concepts in ecology, biodiversity, eco-hydraulics and eco-hydrology will
be presented at first, and then, structure of freshwater ecosystems and its function will be explained from the
aspects of habitat structure for various riparian organisms and material cycling in the ecosystem. Based on theknowledge of freshwater ecology, climate change impacts on river ecosystem will be discussed particularly in
relation to “threshold temperature” and “cumulative temperature” as two key words in consideration withthermal impacts on organisms. The results will be applicable to environmental assessment of global warming
impacts and thermal pollutions by nuclear power stations, sewage treatment facilities and other plants draining
warm water.
Among human impacts on freshwater ecosystems, water pollution is still the most anxious matter ofconcern in some developing countries, whereas deterioration of hydro-geomorphologic dynamisms by dam
construction and channel improvement leads to more serious changes in freshwater and coastal ecosystems allover the world. Biomass and biodiversity of freshwater community will be formed under a given condition ofgeomorphologic settings, and thus, physical habitat structure created by geomorphologic processes such as
sediment erosion, transportation and deposition is an important basic factor for freshwater ecosystems. Therefore,a science for analyzing habitat structure and its maintenance mechanisms, named “Habitatology”, is required fordevelopment of future basin management. In this lecture, roles of disturbance in freshwater ecosystems and aconceptual scheme for the riverbed management will be shown with some examples investigated in the Tenryu
River and the Yodo River systems. At last, we will discuss on future perspectives of integrated basin
management rooted in freshwater ecology.
Note: Some of the concepts and knowledge introduced in this lecture will be applied practically to theecological field surveys under the title of “Measurement of Riverbed Habitat Conditions for Aquatic Animals” at
the Uji River and the Kizu River on 11th December.

The 23rd IHP Training Course in Kyoto University

Fundamentals of Freshwater Ecology

Contents
1. Definition of Ecology and Biodiversity

Fundamentals ofFreshwater Ecology

2. Structure of Freshwater Ecosystems
3. Necessity of Freshwater Ecology

Yasuhiro TAKEMON
Water Resources Research Institute,
Disaster Prevention Research Institute,
Kyoto University

4. Field and Methods of Freshwater Ecology
5. Climate Change Impacts on River Ecosystem
6. Importance of Habitat Ecology
7. Roles of Disturbance in Freshwater Ecosystems
8. Application to riverbed management

Lecture 3

on 4th Dec 2013

Lecture 3 on 4th Dec 2013

What is Ecology?
The branch of science that is concerned with the relationships
between organisms and their environments (American
Heritage Medical Dictionary)
Haeckel, E. (1869) : Okologie, from Gk. oikos "house,dwelling place, habitation” + -logia "study of."
Elton, C.（1927）: Economics and/or Sociology based oninteractions among organisms
Biological interaction
Organism
reactionactionenvironment

Organism

Organism

reactionactionWhat is ecosystem structure andfunction?
Structure
Flow

Migration

Material flow
Energy flow
Cycle
Material cycling

FlowenergyRetention
Volumetime

Function

Facilitation
Retardation

Biological manipulation on
the flow and cycle

Cycle

environment

Fundamentals of Freshwater Ecology

Contents
1. Definition of Ecology and Biodiversity
3. Necessity of Freshwater Ecology
4. Field and Methods of Freshwater Ecology
5. Climate Change Impacts on River Ecosystem
6. Importance of Habitat Ecology
7. Roles of Disturbance in Freshwater Ecosystems
8. Application to riverbed management
Lecture 3 on 4th Dec 2013

Subbasin
1

2

1

1

2

Mountain
Lake
1

1
2

1

1

1

2. Structure of Freshwater Ecosystems

1

1

Headwater
segment2

1
2

Mountainsegment

3
3

Reservoir
2

Grand
water
lake

Watershed221

Freshwater organisms have
evolved adapting to thehierarchical habitat structure
Basinof a river system

Valleysegement
1

Tributary3

SpringGrand
waterchannel4

Spring Alluvial fun
segment

Branchriver

Alluvial fun

SpringFlood plane

1
1
2
2

Flood planesegment
Main
river

4

Oxbow
lake
Natural levee

Alluvial
plane
Delta segement
Rivermouth
Sandy flat4

Spring-fed river

Branch
river

Delta

Back marsh
Sandy
beach

Hierarchical structure of a river system

1

2

1

1

1
2

1

2

2

Reservoir
2
1

Grand
water
lake

1

3

Watershed

2
3

more

Channel stability

less

less

Sediment supply

more

Valleysegement

Tributary

Dam
Spring 4

Grand
water
channelBranchriver
Spring

1

Main channel
Pool

Secondary channel
Pool

Riffle head
Riffle tail1
2
2

Alluvial fun
Flood plane

Secondary channel
RiffleBar headbackwater

1

Alluvial fun
segment

3

Flood planesegment
Main
river

4

Oxbow
Spring-fed riverlake
Natural levee

Alluvial
plane
Delta segement
Rivermouth
Sandy flat4

Branchriver

Grain size

MountainLake
1

2

Mountainsegment

Slope

1
2

stability

1

RiffleMain channel

Classification of reachscale habitat structure
less

Headwater
segment

Dams, barrages, levees, andriver channel works result inchanges in geomorphology andBasin habitat structure.

more

Subbasin

more

1

1

Delta

Side poolin abandoned
channelSide poolin active
channelTerrace poolin abandoned
channelWoodlandBar tailbackwater

Grassland

Back marsh
Sandy
beach

BarelandRiffle tailClassification of habitat structure in consideration with
subsurface waters in the river channel

Organisms living in the riffle beds

less

Human impacts on habitat structure of river systems

less

more

Terrace pool

Active channel Terrace

Modified from Church (1992)

Takemon (2007)

Requiring smooth surface of stones and rocks

Blepharocerids
Simuliids (Simulium)

Heptageniids (Epeorus)
Copyright: Takemon & Choi (original figure)

Heptageniids (Rhithrogena)

Psephenids

Organisms living in the sediment

Examples of bird habitats

Free burrowers
moving freely in sandy or
muddy substrates,
sometimes building Ushaped burrows

Tube builders
moving smoothly on
the surface of stones
buried in the sandy or
muddy substrates

Ephemra strigata

Kentishplover.

Reed
Warbler

Meadow
Bunting108Relative height (m)Gliding burrowers
moving smoothly on
the surface of stones
buried in the sandy or
muddy substrates

Longbilledplover

LittleringedploverWagtails
EgretsNest

Feeding6
42

Ecotonein 2010Ecotonein 1965

Bareland

1965年
grassland
Woodlanfd
2010年

Shallows

0
Chroloperlidae
gen.sp.

Chironominae gen. sp.

-2

Naididae gen.spp.
0.5

erosional

Habitat structure

depositional

1.0

Frequency in the unit area
環境省 (編)(2006改定) 日本の絶滅のおそれのある昆虫類-レッドデータブック-5 昆虫類. 246pp. 編集/環境省自然環境局野生生物課, 発行/財団法人自然環境研究センター 発行.
京都府(2002)京都府レッドデータブック2002 上 野生動物編:935pp. 京都府文化環境部自然環境保全課.
竹門康弘, 土居秀幸, 谷田一三（2003）木津川砂州における河川水流入部の水質環境と間𨻶動物の分布様式. 木津川の総合研究：263-270. 河川学術研究会木津川研究グループ.
竹門康弘・若園美沙子・松原始・江崎保男（2009）2.2.3.鳥類の糞から得られた餌動物の特徴.木津川の総合研究II：121-134.

Terrace pool in the Kizu River

Fishes living in the pools and backwaters
Particularly Bitterlings
Micropterus salmoides
Chaenogobius petschiliensis

Acheilognathus rhombeus

Lepomis macrochirus

Nipponocypris temminckii

Japanese medaka Oryzias latipes

Odontobutis obscura

Other aquatic inhabitants
Siphlonurus sp.

A succession pattern of terrace pools
Bare land

Open
Inorganic

Calopteryx atrata

Habitat Suitability
Deep

Palaemon paucidens
Species A

Succession

Semisulcospira reiniana

Species B

Shallow
Species C

Orthetrum albistylum
speciosum

shadow

Vegetation
expansion

Paracercion calamorum

GerridaetOrganic

dry

TriaenodesInterspecific relations among pool dwellers
Unio douglasiae douglasiae

Patterns in relations of channel water levelto ground water level
Canyon wall type

Floodplain in valley
type

Corbicula leana

Rhinogobius flumineus

Canyon wall with terrace type

Symbiosis among the three
Bitterlings
Glochidium larva

Unionids

Gobby
fish

Modified from Malard et al. (2002)
Unio douglasiae douglasiae

Floodplain in delta type

Fundamentals of Freshwater Ecology

Contents
1. Definition of Ecology and Biodiversity
2. Structure of Freshwater Ecosystems
3. Necessity of Freshwater Ecology

Freshwater ecology is essential forunderstanding and utilization of

4. Field and Methods of Freshwater Ecology“River Ecosystem Services ”

5. Climate Change Impacts on River Ecosystem
6. Importance of Habitat Ecology
7. Roles of Disturbance in Freshwater Ecosystems
8. Application to riverbed management
Lecture 3 on 4th Dec 2013

Scheme of ecosystem services in relation
to ecosystem components
Key Concept: Balance of material cycling and its retention
within and among the ecosystem components

River Ecosystem Services
Classification

ExamplesFunction

SupplyWater resources

Drink, Irrigation, Industrial

EnergyHydro-power stations

Materials

Sediment, Nutrient, Food

Resources

Provision
Habitat

Biodiversity

Field

Agriculture, Fishery

LandscapeRecreation, Tourism

ResourcesandEnvironment

CultureFormation

Regulation
Flood control
Erosion-deposition
Material cycling

Modified from Costanza

Drainage and mitigation

Disaster
ReductionandEnvironment

Riverbed level
regulation
Water quality regulation

Modified from Costanza et al.,1997

Fundamentals of Freshwater Ecology

Ecosystem Management
Management of ecosystems authorized by government
acts or assignments aiming at maintaining ecosystem
structure and function by means of adaptive
management based on monitoring and its feedback.

Contents
1. Definition of Ecology and Biodiversity
2. Structure of Freshwater Ecosystems
3. Necessity of Freshwater Ecology
4. Field and Methods of Freshwater Ecology

Economicalcapability

5. Climate Change Impacts on River Ecosystem

Ecosystem
management

Socialacceptability

6. Importance of Habitat Ecology
Ecologicalhealth

7. Roles of Disturbance in Freshwater Ecosystems
8. Application to riverbed management

After Kamizaki（2000）”Ecosystem Management”

Lecture 3 on 4th Dec 2013

Fundamentals of Freshwater Ecology

Contents
Ecohydrology and Ecohydraulics

1. Definition of Ecology and Biodiversity
2. Structure of Freshwater Ecosystems

Ecohydrology: an interdisciplinary field studying theinteractions between water and ecosystems

3. Necessity of Freshwater Ecology

Ecohydraulics: an interdisciplinary field studying the hydraulicprocesses in ecosystems
Ecogeomorphology: an interdisciplinary field studying thegeomorhologic processes in ecosystems

4. Field and Methods of Freshwater Ecology
5. Climate Change Impacts on River Ecosystem
6. Importance of Habitat Ecology
7. Roles of Disturbance in Freshwater Ecosystems
8. Application to riverbed management
Lecture 3 on 4th Dec 2013

Climate change impacts on river ecosystem

Causes for raising water temperature

Climate change impacts on river ecosystem

Method for detecting in-stream fountain site1) Sewage plant drainage
Japan: 328,000 Gcal/yr/haN

Katsura River
KYOTO GARDEN PALACE

2) Power plant drainage
Japan: 989 Kcal/Kwh

End
Kamo
River

3) Subway waste heat
Tokyo: 4,300 Gcal/yr•station
Sapporo: 524Gcal/yr•station

Sewage plantdrainage
Power plantdrainage
Subway waste
heat
Garbage
Incineration Plant

4) Garbage Incineration Plant
Japan: 2,000kca/kg

After NEDO (1990)

Start

Kamo
River

TakanoRiver

Aerial images of thermal infrared video were taken from a helicopter inthe early morining (6:00-8:00) on 12 February 2007 at a hight of 210m
with a resolution of 0.72m.

In-stream fountains at Nishigamo beside the
Kamigamo Shrine

In-stream fountains at Demachi Yanagi
beside the Shimogamo Shrine in the
Takano River
Takano River Kamo River
Demachi Yanagi
Station

Kamo
River

Shimogamo
ShrineKamigamo Shrine

fountain
TakanoRiverfountain
Instream fountain in the
Takano River, a tributary of
the Kamo River

Aerial images of thermal infrared video were taken from ahelicopter in the early morining (6:00-8:00) on 12 February
2007 at a hight of 210m with a resolution of 0.72m.

Seasonal changes in water temperature of themain channel of the Takano River at
Shimogamo site

Seasonal changes in water temperature at thein-stream fountains of Shimogamo site

Ranged within 16-18℃

Ranged between 2.533.8℃

Benthic animals found at in-stream fountains
Endemic species

Dominant species

Nishigamo（St.1）

Paratya compressa compressa

Tubificidae gen. spp.

Kamigamo（St.2）

Nihonogomphus viridis

Tokunagayusurika akamushi

Shimogamo（St.5）

Sternomoera ? sp.
Pseudodiamesea sp.

Asellus hilgendorfii

Kojin-Bridge（St.7）

Dixa nipponica

Impact of artificial thermal drainage in Kyoto
Katsura River
Kamo River

Siphlonurus yoshoensis

Katsura River

Pseudodiamesea sp.

Sternomoera ? sp.

Sewwage plant of Kyoto City

Kyoto City

Species richnessof Chironomidae

Kamo
River

Asellus hilgendorfii

Dixa nipponica

6ªC

Abundance of
Chironomidae>15ªC

Nishi-Takase River

Newly found groundwater
species lacking eyes

Stony bed
Vegetation
StreamFountainStreamFountain

Fountain sites are characterized by a set of cold stenothermal species endemic
to the fountains and abundant tolerant species against eutrophication.

Kamo
River

Kamo River

Takano River

Aerial images of thermal infrared video were taken from a helicopter inthe early morining (6:00-8:00) on 12 February 2007 at a hight of 210m
with a resolution of 0.72m.

Water temperature raise of sewage plant drainage

Sewage plant drainage contributes the raise ofriver water temperature
From 1965 to 2004
Arakawa Basin

Arakawa Basin

Air temperature
Sewage drainage

4 ºC

Raise of WT

River water

8 ºC
5.5 ºCMonth

Kinouchi et al.(2005)

Longitudinal changes in benthos community
characteristics along the Kamo River
Species richness

Toba Sewage Plant

Abundance

FountainUpper Komo

Homoiothermal vs Poikilothermic

Toba Sewage Plant
Vegetation
Stony bed

St.1 St.2 St.3 St.4 St.5

Kinouchi et al.(2005)

Since most of organisms in river
ecosystem are poikilothermic, impact
of temperature change is serious!

FountainSt.6 St.7 St.8 St.9

Takano R.

Lower Kamo

St.1 St.2 St.3 St.4 St.5
賀茂川
Upper
Komo

St.6 St.7 St.8 St.9

高野川R.
Takano鴨川 KamoLower・Both pecies richness and abundance showed a peak in the middle
reaches of the Kamo River around the conjunction with the Takano River.

Picture from Kyoto-Shinbun
20091205

Picture from Asahi-Shinbun
20091205

A “monkey ball” for warming up eachother observed in winter in Japan, thenorthern boarder of wild monkey.

Fish has a narrower thermal range forsurvival than mammal. A seceneof“Kaibori” , draining pond for fishing.

Impacts of thermal regime change on biologicalpopulations

Thermal regime affects poikilothermicorganisms through degree days

Thermal regime change

Thermal regime change

Effective degree-days

Maximum and minimum
temperature

Effective degree-days
Phenology
Bloom date of cherry blossom can be predicted by degree days

Life cycle

Phenology

Production and reproduction

Changes in life history traits

Migration trigger

Lethal threshold

Population survival

Changes in ecological distribution

Arrows indicate causal directions of impact

Maximum and minimum
temperatureRelations of changes in thermal regimes tostream ecosystem

Life cycle of the mayfly Ephemra strigata
ImagosEgg
mass

Thermal regime change
Maximum and minimum
temperature

Effective degree-days

Gravelbar

RiparianwoodLife cycle

RifflePhenology

Production and reproduction

Changes in life history traits
Arrows indicate causal directions of impact

Study site

Lake Biwa
Kurama River

Katsura River

Reproduction isheld at the riffle
and eggs are laidat the gravel
shore in May.

Shiga

Pool

Subimagos

高水敷

Subimagos fly
up to theriparian wood
canopy andstay for 3-4
days until
transform toimagosLast instar
nymphsemerge fromthe water
surface inMayNymphsNymphs live in the sandy substrate
for one year in Honshu.

Difference of seasonal timing of the last instar
recruitment and emergence between 1982 and1984 seemed to be related to the thermal regimes
Yaer of warm winter

Kyoto
Kamo River
Uji

Nymphs inhabit thepool and live on fineparticulate organic
matter suspended in
the river flow

Emergence of subimagos werecounted everyday using
emergence traps to estimate thelast instar recruitment in the field

Uji River

Kizu RiverJapan

Yodo RiverN

Yaer of cold winter

Osaka
Nara

The mayfly Ephemera strigata has a distinctively
synchronous emergence period in May
Nypmphs werecollected weekly toestimate the last instar
recruitment in the field

Male

FemaleEmergence ofsubimagos werecounted everyday
using emergencetraps in the field

The seasonal timing of the thelast instar recruitment andemergence was significantly
different between years

After Takemon (1990)

Conceptual model for calculation of thermal
constant in the last instar nymphs of the mayfly
Water temperature

Osaka Bay

Effective degree-hours

Developing zero

Recruitment of thelast instar nymphs

Thermal constantEmergence ofsubimagos

Thermal constant: effective degree-days /hours required for completion of development
Developing zero: the lower threshold temperature for development

Graphical method for determination of TC and DZof the last instar nymphs

Simulation of the emergence curves basedon estimated DZ and TC.

TC: Thermal constantEffective degree
hours/days methods
can predict not only the
emergence timing(phenology) but also
the emergence
syncronicity

DZ: Developmental zero
TC and DZ could be
estimated by assuming thenymphs of SDE and SDL
have the same TC .
Male TC = 3624 °C hr(151°C day)Female TC = 3706°C hr(154°C day)Male DZ= 8.8 °C
Female DZ = 9.0 °C

Normal distribution curves
with the same standard
deviations of the fieldsamples were used.

This method is widely
applicable for poikilothermicorganisms living in streams
and rivers.

After Takemon (1990)

After Takemon (1990)

Simulation of the male emergence curves
based on a set of different values of DZ

Relations of changes in thermal regimes tostream ecosystem

assumed DZ value

Emergencetiming andsynchronicity
can be controlled
by the values of
DZ

Thermal regime change
Effective degree-days

Maximum and minimum
temperature

Migration trigger

Lethal threshold

Population survival
The last instar recruitment
Field emergence curve

Changes in ecological distribution

DZ = 8.6

Arrows indicate causal directions of impact
After Takemon (1990)

Ordinary method for determination of DZ by rearing theanimals under different constant temperature conditions

Fundamentals of Freshwater Ecology

Contents
1. Definition of Ecology and Biodiversity

E. strigata

DZ: the lower
thresholdtemperaturefordevelopment

DZ

LT

DZ

LT

LT: the higher
thresholdtemperaturefordevelopment= Lethalthresholdtemperature

the lower threshold temperature for development

DZ

LT

DZ

E. japonica

LT

2. Structure of Freshwater Ecosystems
3. Necessity of Freshwater Ecology
4. Field and Methods of Freshwater Ecology
5. Climate Change Impacts on River Ecosystem
6. Importance of Habitat Ecology
7. Roles of Disturbance in Freshwater Ecosystems
8. Application to riverbed management
Lecture 3 on 4th Dec 2013

Habitat evaluation perspectives

Habitats of aquatic animals are determined bygeomorphology
Main channel

Secondary channel

本流路瀬

二次流路瀬

Goby-fish spawns at the riffle head Riffle
for reproduction
本流路淵

Riffle
二次流路淵
Pool

Pool

淵尻瀬頭

Bar structure and/or rifflepool structure
as an indicator of river
ecosystem state

Riffle headpool

Freshwater goby

rifflepool

Depth (cm) Fry

Females of Ephemera strigata makeswarms above a riffle before oviposition（ 淵かけ上がり ）Riffle tail旧河道たま り低水路たま りSide poolin bandandchannelSide poolin mainchannel

砂州尻ワン ド（ 背水型ワン ド ）Bar tailbackwater

Spawning bed
Spawning redd

Kizu River in Japan

瀬尻淵頭
砂州頭ワン ドBar headbackwater

瀬尻淵頭（ 淵かけ上がり ）Ishida et al （2006）

木本植生
woodland
草本植生
grassland
裸地
BarelandRiffle tailMaterials
Eggs laid into the water

They land on the gravel shore to lay eggs.

An example of the concentrated distribution of oviposition
sites of Ephemera strigata (Eaton)

71 females laid eggs here
Side channel
44 nymphal
skins after
emergence

Methods

Habitat survey•Geomorphology measurement (contour of cm resolution)•Permeability test of hyporheic zone (current speed)•Measurement of substrate diameter•Water quality (WT, DO, EC, pH, NO3, etc)
•Measurement of canopy coverage (using fisheye lens)

Biological survey
5m

Main channel

After Tanaka et al (2003)

Distribution pattern ofoviposition sites

Field experiment of egg
hatchability and survival ratio

Correlation analyses
Finding out factors governing fitness of the oviposition sites

Packer method was
adopted for measuring
permeability
Darcy’s low for flow: v = kiResults: spatial variations in hyporheic current
velocity at Site B
Hydraulic gradient (i)Hydraulic conductivity (k)Packer method

V: mean current speed, i: hydraulic gradientk: Hydralulic conductivity
Q: Flow volume
l : Length of sediment column
h: Height of water column underequilibrium condition
Piezometer method

pool

pool

rifflePorewater current speed (V)t1: Initial time of water injection
Stand pipe was set at the depth of 15 cm
t1: Completion time of penetrationfrom the bottom in channel and fromgroundwater surface on the bar.
Inner diameter (r) =4.6cm、length (l)=5cm

pool

Experimental design for rearing eggs andyoung instar larvae in the field at site B

pool

riffleDistribution of the oviposition
sites corresponding well to thesites of high hydraulicconductivity and gradient, andtherefore, high current speedof hyporheic water.

Distribution of DO concentration and watertemperature of hyporheic water at site B.

pool

pool

riffleBurying caseCollected nymphs

Collected eggs

pool

Cases with 1,000-2,000 eggs were buried on 2 May andcollected on 24 May, 7th June, 22nd June, and 9th July 2003.
Total number of eggs
LivingDead

Dead

Egg mortality = Number of dead eggs / Total number of eggs
Hatchability = Number hatches / Total number of eggs
Survival ratio = Hatchability *Number of living nymphs / Total number of hatches

Results: distribution of egg hatchability

DO concentration (mg/l)

Water temperature (℃)２）Water temperature was high inner area of the bar.

Results: distribution of survival ratio

pool

pool

pool
rifflepool

24 May24 Maypool

9 Julyrifflepool

7 Junepool

pool

rifflepool

pool

riffle7 Junepool

22 Junepool

riffle１）DO concentration was high around the shore area ofupper riffle corresponding to down welling zone of thechannel water.

HatchedLivingpool

pool

riffleBurying sites

rifflepool1) Although egg hatchability was higher along the riffle
shores, eggs could hatch even in lad areas of the bar.

pool

pool
riffle22 Junerifflepool
rifflepool

9 JulyNymphs can survive only near the riffle shoreRelations of survival ratio to DO concentration andwater temperature in hyporheic zone

Environmental conditions required forincreasing fitness of mayflies
Flow regimes

unstablestableSediment supply
less
5.7

9.5

more
woodlandbarelandflat
Upwelling

Downwelling

9.6

3.4

Threshold DO
concentration forsurvival

20.4

17.3

Threshold DO : 3.4mg/L
Threshold WT : 20.4℃

Thresholdtemperature forsurvival

Conclusion
Implications for the oviposition sitesof Ephemera strigata
(1)

Slope
woodlandUpwelling in May
Downwelling
ＤＯ >4.5
mg/L in July，>3.4mg/L
HC(k) >0.005〜0.05cm/sec
Current speed (V) >0.3〜4.0cm/hr
WT <20.4℃ in Julybarelandsteep
Downwelling

Upwelling

Upwelling

Downwelling

Requirements of Ephermera strigata

Discussion: Aquatic insects using hyporheic
zone need fresh sandy bar with highpermeability
Vector of present condition in JAPANBare land

Habitat suitability
for E.strigata(2)
Grass land
RiffleThey lay eggs along the riffle shore of gravelbar at the bar head in order to increasesurvival ratio of eggs and young instar
Poool
nymphs because of higher DO concentration (3)derived from higher current speed in the
hyporheic zone.

Wood land

Vector of target image for management

Fundamentals of Freshwater Ecology

Better habitat conditions are reincarnated by
disturbanceContents
1. Definition of Ecology and Biodiversity

bar island

2. Structure of Freshwater Ecosystems
3. Necessity of Freshwater Ecology

Downwelling

Upwelling

More species live in higher
density in the downwellingzone and the surface layer ofthe phreatic zone.
(Takemon et al. 2003)

4. Field and Methods of Freshwater Ecology
5. Climate Change Impacts on River Ecosystem
6. Importance of Habitat Ecology

Downwelling

Upwelling

7. Roles of Disturbance in Freshwater Ecosystems
8. Application to riverbed management

Species diversity and abundance

Lecture 3 on 4th Dec 2013

Why disturbance is necessary?

Habitat conditions required for Ayu
fish reproduction

Intermediate-Disturbance Hypothesis
Theoretical Relationship Between Diversity and"Disturbance"

recruitment/colonization(r)
Eggs of Ayu fish

Why they select riffle head for spawning?
Riffle suitable for the spawning red

Frequency of DisturbanceIntensity of DisturbanceInterspecific relations are used for the reason after Connel(1978) but habitat creation and conditioning are more importantfunction of disturbance at least in ecosystems.

1mm

Tenryu River

Suitable habitat age for spawning redd of
Ayu fish

Method for measurement of riverbed softness

Suitability of riffle habitat for spawning redd of
Ayu fish will decrease linearly after creation

Falling weight
Scale

Penetration coneSoft
riverbed

Just afterlarge spates

Longevity

Habitat age

Highpermeability

Interstitialflow
erosionAfterexperience ofsmall spates
After long
period of low
water level

Interstitial
hard
Lowflow
permeability riverbed

hard
High
riverbed permeability

Suitablity

Time

Biotic Diversity

competition(K)

Spawning redd location

Ayu fishdeposition

Soft
Low
riverbed permeability

Interstitialflow

Cone penetration test

76

Hyporthesis on habitat changes in riffles
large
spates

A Geo-morphological Monitoring Method for Analyzing
Spatiotemporal Patterns of Riverine Habitat

Creation of soft
riverbed by new
deposition

deposition erosion

deposition erosion

small
spatesDecrease in riverbed
softness by erosion

erosion deposition

erosion deposition

Lowdischarge

Decrease inpermeability byfiltering
deposition
deposition
erosionerosiondeposition
deposition

Kyoto University Disaster Prevention Research Institute
IDEA Consultants. Inc.

8 July 2013

Makoto Hyodo

A Seminar at Trento University78Hydrograph of hourly water stages and flow discharges
A research location whereiszoomed inFunagiradam

Hourly water stage and flow discharge at Kashima in the Tenryu river

Hiryu bridge 17.0k

Down

Middle（写真提供：浜松河川国道事務所）

Up

11 Sep.

2,000③

13 Oct.

17 Nov.

2,500
10 Dec.

3,000
3,500
4,000

時刻水位：鹿島橋地点を水文水質データベースより収集
時刻流量：鹿島橋地点のH-Q式(H23年度)より算出（資料提供：浜松河川国道事務所）

2012/12/27

2012/12/17

2012/12/07

2012/11/27

2012/11/17

2012/11/07

2012/10/28

2012/10/18

2012/10/08

2012/09/28

2012/09/18

2012/09/08

2012/08/29

2012/08/09

2012/07/30

2012/07/20

2012/07/10

2012/06/30

2012/06/20

2012/06/10

5,000
2012/05/31

4,500

30

An aerial photo taken in 2013.12
New Tenryu
river bridge

13 Aug.

Observation
failed

1,500

②

31
2012/05/01

5m①

maximum flow
discharge

32

16.0k
m

Kasasagi
bridgeRiverbedTenryu
river
BrSoftnessidgemeasuredflood of3
Habitat surveys conducted 500m /s

Nearly36
average
35 maximum
flow
34
discharge
33 （4,000m
Average3/s）

2012/05/21

Below
riverbed

37

1,000

→ Camera monitoring started A Small

Camera test
conducted

2012/05/11

Tower

500

38

2012/08/19

Hourly Water Stage (T.P.m)

16.5k
m

Hamakita
bridge

0

39

m

Flow

Hourly flow discharge (m3/s)

40

Approx. 60m
from the riverbed

Approx. 30cm

Hourly water stage (T.P.m)

Observed maximum water stage: T.P.+38.151m(2003/8/9 15:00)
Mean annual flood discharge: 4,000m3/s(from 2005 to 2010)

Hourly Flow Discharge (m3/s)

Approaches①～③：現地調査日

79

hanging patterns of riverine habitat due to a small size floo80

Spatiotemporal patterns of riverbed softness upstream

3

A small size flood event of 500m /s upstream
Up-BW-1
Up-SP-2
A channel used
Up-SP-1
when a small
Up-Riffle-2
flood occurs①③ Larger numbers show riverbed is soft and vice versa②

2012/10/15

Up‐BW‐11
7.3-5.3
6.5-7.3

2012/10/23 16:00

2012/10/22 10:00

Flood waterflowing
through an
old channel

Inundatedby backwater

6.4-8.7

11.4
10.6

A flood peak

Up‐SP‐1

9.0

Sept.
7.2

Now.

4.9

BW and SP
weremerged

Oct.

6.7

6.7

4.5
3.7

Up‐Riffle‐1

dn mid up

5cm⇒・Riffles are more coarse than back waters and side pools.
・Riffles get coarse after a small flood event, especially at a riffle head.

Riffles expanded by local
2012/10/23 10:00

Up‐Riffle‐2

2012/10/26 10:00 geo-morphological change

82

81

hanging patterns of riverine habitat due to a small size floo
A small size flood event of 500m3/s downstream
Dn-SP-1
Dn-Riffle-1

Dn-BW-21

Dn-SP-2

2012/10/22 10:00

patiotemporal patterns of riverbed softness downstrea①

A mix of back water and achannel used when a small
flood occurs②③ Larger numbers show riverbed is soft and vice versa

Dn‐SP‐1

Flood water flowing
when a small flood
A flood peakoccurs2012/10/23 16:00

Flood waterflowing
through an
old channel

4.4-7.0
7.6
7.0
6.4
5.8

4.3

4.2

6.8
5.4
4.3

2012/10/26 10:00

83

Sept.

Nov.

5cm
Dn‐BW‐21

2012/10/23 10:00

10.3

Oct.

Dn‐Riffle‐
1

Up Mid Dn
Back
water

17.4

Dn‐SP‐2⇒・ Riffles are more coarse than back waters and side pools.
・Riffles get coarse after a small flood event, especially at a riffle head.
84

Fundamentals of Freshwater Ecology

Riverbed degradation by damsContents

Yasugawa Dam (54 years old)

1. Definition of Ecology and Biodiversity
2. Structure of Freshwater Ecosystems
3. Necessity of Freshwater Ecology
4. Field and Methods of Freshwater Ecology

Down stream

5. Climate Change Impacts on River Ecosystem

Upper stream

6. Importance of Habitat Ecology
7. Roles of Disturbance in Freshwater Ecosystems
8. Application to riverbed management

Armoring of riverbed
occurred by sediment
trapping in reservoir

Lecture 3 on 4th Dec 2013

Reservoir dam impacts on river ecosystems

Changes in benthos community in tail-waters

Landscape at 2km below the Ohtaki Dam in Japan after construction

Just after construction2002

9 years later

2011

Species richness

Individual density

Biomass

40000
60

60

Riverbed degradation occurred

3 years later2005

10 years later

30000

40

20000

20

10000

40

20

2012
0

0
1
2
Control
Down
Dam
site
stream site

0

0
1
2
Control
Down
Dam
site
stream site

0

0
1
2
Control
Down
Dam
site
stream site

Species richness decreased and abundance of particularspecies increased in the tail waters of reservoir dams.

Riverbed degradation

Target Image for countermeasure:a natural river without dams upstream

Impact of sediment
trapping by dams andsand dredging

Riverbeddegradation
Riverbed landscape of the Tonda River in Japan

Dam impacts as a barrier for
migratory animals

Target image for restoration ofmaterial flow among terrestrial-riverocean ecosystems
Irrigation dam in
Kamogawa in Kyotos City

Upper river

Sandy flat
Sediment
dynamism

Japanese eel, Anguilla japonica

Animalmigration

Estuary

Seaweed vegetation
Japanese mitten crab, Eriocheir japonica

Ayu or Sweetfish, Plecoglossus altivelis

Target species: marine shrimps using
estuary for reproduction

Target species: Some flounders depending
on inner estuary as nursery for fry“Shiba-ebi” migrates between deep sea and riverthrough their lifecycle.
They migrate between deep sea and river throughtheir lifecycle.

Metapenaeus ensis

Kareius bicoloratus
Stone flounder

Pleuronectes yokohamae
大阪水試研報(13):73〜77,2001より

95

Scheme of the riverbed management based on Habitatology

Work image of riverbed management
Dam dawnstream
Target area
Reservoir
Sediment deposition QS2QS3 QS4
QS1
Replenishment
md
Replenishment
mr

Upper

Sediment load

Practical subjects
Measure of work
(Control object)

CausalrelationsFish habitat

Habitatsuitability

Management

Bed change

2) Riffles

Riverbed softnerss

HabitatsuitabilityDO
concentration

QS4

Bed change

Porosity

Sediment movement Qs

QS41) Pools

Relative height

Relative height

Management

Bed change

The riverbed management is a scheme of river management
aiming at maintaining a target riverbed geomorphology.

QS4

Qs

Habitat Suitability
Model

Geomorphology Model

Causalrelations

Habitatsuitability

Ecological function

Sediment load model

Qs

QS5

CausesRiverbed
softness

Development of
ecological evaluation
methodology fordesirable
geomorphology

Sediment
management

Pool density

Riverbed geomorphology

Identification of
appropriate
sediment load
and flow regimes

Bed changeManagement
objective

Sediment load control
Flow regime control

Relative height

Management
objectResearch subjects

Porosity

DO concentration

Spawning
redds3)Riverbed
Intersticial
waterSediment Replenishment in Nunome Dam

Needs: Present feature of Japanese rivers

a total of 2,585 m3 sediment was supplied with flushing operation

Flooding area (ha)Reduction of ecotones in the Yodo River

History of sediment replenishment (date and volume)(b) Under replenishment(2009/10/8) discharge: 81㎥/(a) Before (2009/10/2)
sediment volume: 500㎥(c) After (2009/10/14)

Matching of requirements
We need to estimate
upper limit of the riverbed
height for disasterprevention

Non flooded area
Annual max-8 days
8-22 days water level22-70 days
70 days-annual mean
Annual mean-minimum
Water surface area at theminimum water level

We need to estimate lower
limit of the riverbed height
for environmental
requirements

A case study in the
Mizoro-ga-ike Ponda semi-natural pond of 9 ha in areaand 1 km in circumference, locatedin the north of Kyoto City
Designated as a national treasurte

Lake
Biwa

Mizoro-ga-ike

Japan
Kyoto

Kyoto CityTokyo
rooms for sediment dynamism

Mizoro-ga-ike is composed of a floating mat of peat vegetation,
coastal submerged vegetation, and an open water.

Structure of the floating mat bearing a lot of RDB species

Coastal submerged vegetation
Sphagnumcuspidatum
hummock

Floating mat ofpeat vegetation

Nannophya pygmaea

Sphagnum
palustre
Hydrovatus yagii

hummock
hollowOpen water

100m

Visit http://mizoro.org/index.html for information

Fresh Peat

1.5 m

Water

< 0.2 mPeat and sediments> 10 m

Vertical structure

WaterspiderCeriagrion
melanurum

Ilyocoris cimicoides
exclamationis

Alien fish intruders in Mizoro-ga-ike

Changes in fish species composition in Mizoro-ike
Largemouth bass

100

Blugill sunfish

Micropterus salmoides
salmoides
Lepomis macrochirus

Blugill sunfish
Compsitions (%)

80

Largemouth bass

Rhinogobius sp.
Cyprinus carpio

60

Oryzias latipes

Blugill sunfish

Rhodeus ocellatus
ocellatus

40

Carassius sp.
Pseudorasbora parva

20

0
1979
(n=66)Fish community structure in 1970s in Mozoro-ga-ike

1998(n=79)

1999(n=136)2000(n=263)

2001(n=314)2002(n=119)Alien fish intrusion in late 1970s in Mozoro-ga-ike
Fallen insect eaters

Fallen insect eaters

Fish and anuran eaters

Phytoplankton and algaeeatersZooplankton eaters

Phytoplankton and algaeeatersZooplankton eaters

Fish and anuran eaters

カダヤシ

カムルチー

カムルチー

メダカ
メダカ

Benthos eaters

カワバタモロコ

タモロコ

オイカワ

カワムツ

タモロコ
ナマズ

シロヒレタビラ

タイリクバラタナゴ

オイカワ

ブルーギル

カワムツ

シロヒレタビラ

モツゴ

モツゴ

ニッポンバラタナゴ

Benthos eaters

カワバタモロコ

ドンコ

ギンブナ

ニッポンバラタナゴ

トウヨシノボリ

タイリクバラタナゴ

オオクチバスギンブナ

トウヨシノボリ

ドンコ

ナマズ
ドジョウ

ホトケドジョウ

コイ

コイ

Before invasion of alien fishes smal cyprinid species are abundant. They took a rolefor preventing water eutrophication by consuming phytoplankton.

Fish community structure in 1990s after alien fish invasion
Fallen insect eaters
Phytoplankton and algaeeatersZooplankton eaters

Fish and anuran eaters

カダヤシ
カムルチー

ドジョウ

ホトケドジョウ

Before invasion of alien fishes smal cyprinid species are abundant. They took a rolefor preventing water eutrophication by consuming phytoplankton.

Citizens participatingin the net fishing
Setting of fishing net

Benthos eaters
ブルーギル

Eri-ami fishing net
オオクチバスギンブナ
トウヨシノボリコイ

ドジョウ

Before invasion of alien fishes smal cyprinid species are abundant. They took a rolefor preventing water eutrophication by consuming phytoplankton.

Visit http://mizoro.org/index.html
for information

Collecting fishes on a boat

Action of adaptive management

Traps used for alien fish control in Mizoro-ga-ike

Results of population control for Bluegill sunfish
Population estimates

Number of individuals

Population estimates
Population estimates
Population estimates
Number of 1+ fish removal
Number of 0+ fish removal

Bait is no need for trapping
the bluegill sunfish

Conclusion and Discussion

Present state and future prediction of population

Strategy for ecosystem management

2003

Bluegill sunfish

River ecosystem

Wetland ecosystem
1.Community structure

1.Community structureInterspecific interactions
overwhelm physical disterbancePhysical disturbance reset theinterspecific interactions
Adaptive management forcontrol efforts

10,000

Apt to change into one way
direction to eutrophication

Being in a dynamic equilibrium
state

産

2005

9,000

2.Material Cycling

2.Material Cycling

モデル1

卵期前に除去した成熟魚も繁殖したと仮定

8,000

産
モデル2

7,000

卵期前に除去した成熟魚は繁殖していないと仮定

Strategy

個体数

6,000

個体推定値
5,000

Priority in management

4,000
3,000

Manipulation of physical process such as
flow regimes and sediment dynamism

2,000
1,000
0
98

99

00

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

年

Conclusion and Discussion

Strategy for ecosystem management
River ecosystem
Unstable habitat

Achievement

Manipulation of physical process such as
flow regimes and sediment dynamism

0

Wetland ecosystem
Stable habitat
Manipulation of biological process suchas primary production and predator-preyinteractions

community ／ community+habitat

Investment ratio of manipulation

1

Manipulation of biological process suchas primary production and predator-preyinteractions

Lecture 4: Sustainable Water Resources Management in Marginal Area :Case Study in Indonesia
Ignasius D.A. SUTAPA (Asia Pacific Center for Ecohydrology - UNESCO Category II Center)
Water is recognized as the most essential of all natural resources and, obviously, there is noenvironmental diversity and even social and economic development can be sustained without water.
However, soon water became a limited resource as the demand is increasing following the increasing ofworld’s population and the expanding use of water in other sectors. Gleick et al (2002) report that in thelast century, the world population grew from 1.6 billion in 1900, to 6.0 billion in 2000 and according to theUN (2204) projections this figures is expected to reach 8.9 billion by the year 2050 yet freshwater supply
that can immediately satisfy human and ecosystem needs is very limited and it is unevenly and irregularly
distributed.
Indonesia's attention to the issue of environmental damage is increasingly intensifying. One causeof the worsening environmental conditions is due to natural resource management that cannot be renewed
or can be renewed has exceeded its carrying capacity. Environmental sustainability and ecosystem levels
are not maintained anymore, therefore the parties involved, in this case, the private and state enterprises,
governments and communities need to develop good cooperation and synergy for sustainable
environmental management.
Water resources in marginal areas are water resources in areas with the following characteristics:
peatland areas, brackish areas, areas of pollution and post-disaster areas. One of the major problems inIndonesia today is to manage water in peatland areas. Indonesia has vast peatlands spreading across several
islands: Sumatra, Kalimantan and Papua. Water resources in the peatland areas have the following
characteristics:- Has a low pH levels (2-4) that is highly acidic- Have high levels of organic- High levels of iron and manganese- Yellow or dark brown
These charactheristics can be explained by the process of peat water formation as shown in Figure 1.

Figure 1 Mechanisms of peat water formation (UNDP, 2006)

Part I

ROLE OF APCE AS LOCAL WISDOM CATALYSTFOR SUSTAINABLE
WATER RESOURCES MANAGEMENTASIA PACIFIC CENTRE FOR ECOHYDROLOGY
APCE – UNESCO CATEGORY II CENTRE“Managing Water Systems throughEcohydrology and Cultural Values”

Dr. Ignasius D.A. Sutapa
Executive Secretary
Asia Pacific Center for Ecohydrology(APCE) – UNESCO Category II Center
Research Centre for Limnology Campus
Cibinong Sciences Centre – Jl. Raya Bogor Km 46
Cibinong – Bogor – West Java - INDONESIA
Tel.: 021-8757071 Email : ignasdas@yahoo.co.id

BACK GROUND• The Asia Pacific Centre for Ecohydrology (APCE) is a
category II center of the United Nations Educational,
Scientific and Cultural Organization (UNESCO).
• It focuses on ecological approaches to water
resources management, to provide sustainable water
for the people by harnessing science and technology,
education and culture.
• APCE is committed to contributing towards
overcoming current and important issues of national,
regional and global interest, such as poverty, climatechange adaptation and disaster risk reduction.

Organization Structure

Executive Director

Executive Secretary

Research Division

Training & Workshop

Information System

Public Awareness

APCE DIRECTIVE• VISION• Executive Director :– Prof. Dr. Hery Harjono• Executive Secretary :– Ass. Prof. Dr. Ignasius D.A. Sutapa• Head of Reasarch Division :– Prof. Dr. Hidayat Pawitan• Head of Training and Workshop Division :– M. Fakhrudin• Head of Information System :– Prof. Dr. Robert Delinom• Head of Public Awareness :– Dr. Munasrio To be an Internationally Reputed Asia Pacific Center in
Urban and Rural Ecohydrology by 2021

• MISSIONo Develop understanding and practices of ecohydrology
through research, training and knowledge exchanges,
information systems and public awareness.

• VALUESo Wisdomo Integrityo Harmony

STRATEGIC GOAL
1. To promote local resources base ecohydrologicalresearch
2. To strengthen local capacity to adopt
ecohydrological concept and approach
3. To provide easy access to local resources based
ecohydrological information and knowledge
4. To enhance public awareness of local resources
based ecohydrological practices

RECENT ACTIVITIESIFAS TRAINING COURSE• A technical course was organized based on theframe‐ work of the Flood Forecasting and Warning
System which was conducted in 10 countries(Australia, Cambodia, China, Indonesia, Lao People’s
Democratic Republic, Malaysia, the Philippines, theRepublic of Korea, Thailand and Vietnam).
• This Integrated Flood Analysis System (IFAS) course
was realized in collaboration with the International
Centre for Water Hazard and Risk Management, the
UNESCO Jakarta Office and LIPI.

Demo Site for Community‐Based
Water Management• The objective of the demo site for ecohydrology development is toact as a field station for the implementation of ecohydrology
concepts in the field.
• The demo site ecohydrology campaign is expected to be significant
in socializing the sustainable management of water resources inaccordance with the concept of ecohydrology. It will also serve as anatural laboratory for the future development of ecohydrology,
especially as a tropical Indonesian concern.
• Ecohydrology demo site development in Indonesia will be directed
to a location demo site representing the concept of sustainable
water resources management in several different groups, namely
an ecohydrology demo site for the community‐based managementof water resources.

IFAS course activities

Flooding events in Indonesia

Problem of Water resources in Islamic boarding
school• Indonesia : 26.000 Pondok Pesantren (islamicboarding school)• Number of people in Ar‐Risalah : 850 p• Average needs of clean water :– 850 x 100 l/p/day = 85.000 l/day = 85 m3/day• Problem of wastewater– 85 m3/day discharged directly to the environment– High degree of pollution• Urgent to be solved

Raw Water Condition

Water for domestic use (bathing, washing, cooking)

Importance of water in preparation of daily
prayer for the BS Students

Constructed Wetland
Reduce pollution
Recycle wastewater

Capacity building of community leaders
Way of paradigm shift
Increase in community awareness and participation

Best practice enlightment
Way of Heartware Concept Implementation

Cultural Landscape andSubak System in Bali

Discussing with Islamic
Leaders community

Artificial constructed wetland• Subak is the name of the water management(irigation) system for paddy fields on Bali island,
Indonesia, developed more than 1,000 year ago.
• Over that time, this traditional ecologicallysustainable irrigation system has constantly adjusted
to changing situations.
• The result is an intricate system which is stronglyinterlinked with Bali’s natural, social, cultural andreligious environment.

IPAG60: Alternative Technology
for Clean Water in Peatland Areas
Subak system : Pura,
paddy field and watero The majority of areas in Riau Province and Central Kalimantan Province
have land with peat surface water, which characteristically has:o low pH levels (2‐4), making it highly acidico high levels of organic mattero high levels of iron and manganeseo yellow or dark brown colour.

o Peat water treatment technology that has been established in previous
studies (2009‐2011), by Ignasius D.A. Sutapa and team enables peat areas
to have peat water treatment facilities for the drinking water supply.
o A lot of territory in some areas in Indonesia – especially Sumatra andKalimantan – have clean water source issues.
o Implementation of this technology in the wider area is necessary tosupport the increase in water services in the region.

Rice Field in Jatiluwih
world heritage site

Important Points

IPAG 60 : from peatwater
to clean water

Clean water produced by
local people with IPAG60• Main problems of water resources managementin Indonesia : pollution, conflict of interest, lackof community participation• With around 26.000 of islamics boarding schools
in Indonesia, important gate to introduce SWRM
concept• Strengthening tha capacity buliding of Ar‐Risalah
Islamic Boarding School in wastewater
management increase water quality released tothe environment

THANK YOU...

Part II

The Bruntland Report popularized the term sustainability
for human and environmental development when it was
published in 1987. In the report, sustainable activities
were defined as ones where the needs of the present
generation are met without compromising the needs offuture generations
 What the Bruntland definition implies is an equitable
distribution of the resource not only spatially between
users in a given location, but temporally between users
over time. The idea is to allocate the resource in such a
way as for all, including the environment, to have anadequate share without making any one group worse off,
both now and in the future.


Dr. Ignasius D.A. Sutapa

Executive Secretary

Asia Pacific Center for Ecohydrology(APCE) – UNESCO Category II Center To

achieve sustainability, there must be arethinking of what we consider a basic need. It is
common in our society to say that we need agiven resource, but how much of it do we reallyneed to use?
 How do we decide what the basic needs of ourecosystem and the organism living within it are?
 Defining what constitutes a basic need is perhaps
the greatest challenge to adopting sustainable
practices in our daily lives, as interpretations ofneed vary widely from region to region, village tovillage and even from person to person.

Sustainable Water Management (SWM), : simply to manage
our water resources while taking into account the needs ofpresent and future users
 The International Hydrological Programme, a UNESCO
initiative, noted:

There has been a shift in recent years from the traditional‘top-down’ approach to a more open management system
where all levels have a say in the allocation and use of theresource. If properly done, this system ensures that theneeds and concerns of those most affected by the use of
the resource are addressed, without loosing sight of thewider issues touching the society as a whole.
 Understanding the needs of the stakeholders, as well as
the possibilities and limitations of the resource, is needed
to manage it effectively. This requires sharing bothindigenous and modern scientific knowledge, as well as
establishing a dialogue between individuals and large
institutions. With the right information, appropriate
strategies can be formulated to deal with the realities ofresource management, such as distribution, access, rights,
etc.






◦ "It is recognised that water problems cannot be solved by quicktechnical solutions, solutions to water problems require theconsideration of cultural, educational, communication andscientific aspects. Given the increasing political recognition of theimportance of water, it is in the area of sustainable freshwater
management that a major contribution to avoid/solve waterrelated problems, including future conflicts, can be found.“SWM attempts to deal with water in a holistic fashion,
taking into account the various sectors affecting water use,
including political, economic, social, technological andenvironmental considerations.

Since the Mar del Plata Water Conference hosted by the UN in
1977, SWM has been high on the international agenda. Later
conferences and workshops have addressed the issue and haveattempted to refine the concept as more and more research has
been done in the area. The current understanding of SWM isbased primarily upon the principles devised in Dublin during theInternational Conference on Water and the Environment (ICWE) in
1992, namely:◦ Freshwater is a finite and valuable resource that is essential to sustain life,
the environment and development.
◦ The development and management of our water resources should bebased on a participatory approach, involving users, planners and policy
makers at all levels.
◦ Women play a central role in the provision, management and safeguardingof water resources.
◦ Water has an economic value and should therefore be seen as an economic
good.



These principles reflect the importance of water in our daily lives
and the need for proper communication, gender equity, andeconomic and policy incentives to manage the resource properly.

 In addition to acting as repositories for uniqueand important biodiversity, peatlands inSoutheast Asia is of global importance becauseof its ability to store an estimated 120 billion
tonnes of carbon or approximately 5% of theworld’s terrestrial carbon.
 Peatlands also play a critical role in the socioeconomic well-being of the country, particularly
for their ecological and hydrological value, theirtimber and non-timber forest products, watersupply, flood control and many other social,environmental and economic benefits.

Peat swamp forests are habitats or are part of the home range
for rare and endangered mammals such as Malayan Tiger(Panthera tigris malayensis), Tapir (Tapirus indicus), Sumatran
Rhino (Dicerorhinus sumatrensis) and Orang Utan (Pongo
pygmaeus).
 Peat swamp forests also support a diverse bird community.
Prentice and Aikanathan (1989) recorded 173 species of bird in
the North Selangor Peat Swamp Forest of which 145 werebreeding residents. Birds present include endangered species
such as hornbills and the Short Toed Coucal.
•Peatland rivers, also known as ‘black-water rivers’ are important aquatic
habitats for fish. These rivers often have a higher degree of localised
endemism for fish species compared to other rivers, and they are also an
important source of aquarium fish. Ng et al (1992) recorded more than 100
fish species in the North Selangor Peat Swamp Forest. Approximately 50% of
these are restricted to black-water rivers.











Increasing pressures for land development (e.g. agriculture,
infrastructure) have affected peatlands in Indonesia over the past 20
years.
A number of these threats directly stem from or are associated to land
conversion, especially for agricultural practices, that have been managed
in an unsustainable manner.
These threaten the integrity of peat ecosystems and have resulted insignificant loss of ecological support services eg. flood mitigation,
prevention of saline water intrusion, sediment and toxic removal,
groundwater recharge, micro-climate regulation etc.
Many agricultural and plantation projects for oil palm, pulpwood, riceand various other crops on peatlands have failed due to unsuitableconditions and the application of inappropriate methods.
The land conversions have direct negative physical impacts on peatland
ecosystems and its associated biodiversity. These impacts also haveassociated effects on remaining peatlands due to drainage, such as peat
subsidence, fire and loss of vital ecological services



Water Regulation



Carbon Sequestration and Storage◦ Peatlands in their natural state are water-logged due to ahigh water table and act as a large water reservoir,consequently playing an important role in water regulation.
Important functions in this aspect are flood mitigation andwater supply, which contributes to the environmental
security of human populations and ecosystems in its
surrounding areas.
◦ Peatlands in the Southeast Asia play a role of global
importance in storing an estimated 120 billion tonnes ofcarbon or approximately 5% of the global terrestrial carbon.
Malaysia has the second largest extent of peatlands inSoutheast Asia after Indonesia, most of which are still intact
thus contributing to sequestering carbon from theatmosphere and acting as a store for large amounts ofcarbon Peat swamp forests have been a source of timber andnon-timber forest products. They are rich in high quality
timber species such as Ramin (Gonystylus bancanus),
Durian Paya (Durio carinatus) and a number of Shorea
species.
 There are at least 120 timber species of commercial
value and if harvested in a sustainable manner willcontinue to provide these resources for a very long
period of time.
 Other non-timber plant products include rattan, asamkelubi, palm trees, Pandanus, scented wood trees
species, medicinal plants, resin-producing trees andornamental plants, for eg. wild ferns which are utilised
and traded by local communities living around peatland
areas◦• Peat soils are generally marginal to poor for
agriculture, particularly those exceeding 2m in depth.
• Poor or unsustainable practices and the abandonmentof agricultural projects leave the degraded peatlands
vulnerable and susceptible to more negative impacts
and threats, leading to further peatland degradation.

• Peatland fires in the country and in the SE Asian region
have had one common phenomenon in the past 20 years.
They are often associated with periodic drought
occurrences and closely-linked with forest clearance anddrainage activities by the forestry and agricultural sectors.
• The El Niño cycles also play a significant role in peatland
fire incidents. Detrimental impacts associated withpeatland fire incidences are the negative effects on thesocio-economy of local communities who are dependenton peatland resources, environmental pollution and thesignificant decrease or loss of important floral and faunal
populations.
 There is currently inadequate information onsustainable peatland management due to apoor understanding of peatland ecosystems.
 It
is also difficult to access existing
information from the respective government
agencies, departments and ministries which
relate to peatlands and their resources Currently,

there is still a lack of specificpolicies and guidelines related to sustainable
peatland management in Indonesia.
 Existing
policies and guidelines do notprovideproperpeatlandmanagement
guideline, which further contributes to theunsustainable use and degradation ofpeatlands and their resources.


One of the prominent characteristics of peatlands is its highwater table. This naturally-occurring high water table is an
important factor in their formation and for sustaining theirstability.
 Over-drainage of peatlands can have detrimental effects to theecosystem. The threats of over-drainage stem from forestry andagricultural practices in peatlands.
 Agricultural and forestry practices generally attribute to poor
water management practices in peatlands, which significantly
lower the water table leading to the drying and breaking-downof peat soils (i.e. peat subsidence).
 This in turn will affect the floral and faunal biodiversity. In severecases of over-drainage, subsidence of up to 5m have beenrecorded over a period 20 years and such negative impacts could
also be further enhanced during the dry season or droughts.
Part III

LIPI

 Background Objectives

 Distribution of Peatlands in Indonesia
 Characteristics of peat water How to treat peat water?

Dr. Ignasius D.A. Sutapa

 Conclusion

Asia Pacific Center for Ecohydrology(APCE) – UNESCO Category II Center

LIPI

BIOSPHERE RESERVE

National Committee of MAB-UNESCO Indonesian Programme

GIAM SIAK KECIL-BUKIT BATU
BIOSPHERE RESERVE, RIAU
APPROVED AS NEW BIOSPHERE RESERVE AT
THE 21ST ICC/MAB UNESCO MEETING IN
JEJU – REPUBLIK KOREA
ON THE 26TH OF MAY 2009

Emerging challanges and potentioal role of BR in addressing Climate Change, Provision of
Ecosystem Services and Urbanization as a principal driver for ecosystem-wide pressures(MAP)

How to Manage ?

Ecologicalcharacteristics• Peat swamp forest• High biodiversity value• Unique small lakes• Hydrological system

Buffer Zone
± 222,426 HaCore Area± 178,722 Ha
Transition Area
± 304,123 Ha

Values in the Future of CB GSK-BB
Improve the image of Indonesia in Riau and globallevel Encourage the establishment of the Regional Center
for Peat Swamp Forest and build south south
cooperation (South-South Cooperation)
 An example of the application of the ecosystemlandscape management by integrating conservationand development of knowledge-based economy
 Tourism industry with a capital of cultural andbiological richness
 Develop economic activity through the use of natural
resources science-based swamp
 GSK-BB CB making safe of all the activities thatdamage the sustainability of the biosphere reserve.


1. A model of ecosystem management inIndonesian peatland
2. Being a peat swamp forest research center ・Regional Research Center for Peat-Forest Land
Conservation management partnership modelwith private sector
3. A model of the development of ecologicalservices (ecotourism and trade Carbon).

Scope of Technology Aplication

4.

INSTITUTIONAL STRENGTHENING MANAGEMENTAGENCY
PLANT IMPROVEMENT OF MANAGEMENT AND
ACTIONRESEARCH & DEVELOPMENTEMPOWERMENT THROUGH

5.

FUND RISING

1.
2.
3.

 ECONOMIC DEVELOPMENT BASED LOCAL RESOURCES
 THE
ROLE
OF
SOCIALDEVELOPMENTECONOMICCOMMUNITY INCREASING ROLE OF CIVIL SOCIETY trough NETWORKINGAND COORDINATION

1. Peat Water Treatment Plant into clean water;
2. Horticulture Plant Pot To Increase Capacity of Farmers Rural
Household Economy In Transition Buffer Zone and Area ofBiosphere Reserves;
3. Innovation Through Cultivation Vegetable Gardening Normal
and Inverted Utilizing Local Resources in the Transition Area
and Buffer Zone Biosphere Reserve
4. Integration of beef cattle farming business and Electricity
Production (Bio-Electric) For lighting and bio-gas substitutefor firewood / kerosene / LPG for cooking in the village at
the Transition Area and Buffer Zone Biosphere Reserve, and
5. Freshwater Fish Farming In Peat Inland Buffer Zone in theTransition Area and Biosphere Reserve.

GOALS
1. Provide alternative water treatment technologies
for peat water;
2. S pot cultivation as a water saving alternativemodel of economic resources in the form of
new and institutionalize SME village level;
3. Utilizing
local
raw
materials(microbial
functional and peat) for vegetable production
withnormalandinvertedgardeningtechnology.;
4. Integrating the cultivation of beef cattle andElectricity Production (Bio-Electric) For lighting
and bio-gas substitute for firewood / kerosene/ LPG for cooking;
5. Freshwater Fish Farming In Peatwater

Increase well-being and public healthat BR GSK-BB :Pro poor,
Pro job,
Pro growth,
Pro green Humandependence on water in line withpopulation increases.
 As presented in the WHO forum by the former UN
Secretary General Kofi Annan: "That we arealmostimpossible
toeradicate
AIDS,
tuberculosis, malaria or other infectious diseases
that are endemic in developing countries, so that
we could win the battle for safe drinking water,
sanitation and fundamental health care ".

 Thestatement shows the meaning of theimportance of water for survival and life on earth.

 Provide

alternative
water
treatment
technologies that can be used in peat landarea in GSK-BB BR
 Conduct
field observations in order todetermine the mounting location of IPAG
 Know the readiness and willingness of local
communities
toadopt
appropriatetechnologies that will be implemented

IslandProvince

Sumatera

Land Area (Ha)Available toagriculture (Ha)6.244.101

2.253.733

Riau

4.043.600

Jambi

716.839333.936

1.483.662

1.144.851

South Sumatera
Kalimantan

5.072.249

774.946

1.530.256

Middle Kalimantan

3.010.640

672.723

West Kalimantan

1.729.980

694.714South Kalimantan

331.629

162.819Papua

7.001.239

2.273.160

TOTAL

18.317.589

6.057.149

Note: If the peat lands in the Province of Nanggroe Aceh Darussalam, North Sumatra,
West Sumatra, Bengkulu and East Kalimantan are considered, then the total area of peat
lands in Indonesia is around 21 million ha Low

pH levels (2-4)
that is highly acidic
 Highlevelsoforganic matters High levels of ironand manganese Yellowor brown(dark) color

 Low

pH water can cause tooth decay andgastrointestinal illness.
 High content of organic matters causes odor andprovides optimal condition for microbial growth.
 If chlorine is used as disinfectant, Three Halo
Methane (THM) compounds such as carcinogenicorgano-chlorine can be resulted.
 Chemicalbound of Iron and manganese
increases metals compounds in water that cancause lethal effect to human if continuously
consumed.

 Drinking

water for the survival : 5 liters /person / day Water for food preparation : 10 liters / person/ day Water for sanitation :20 liters / person / day Water for hygiene : 15 liters / person / day Volume :◦ 50 liters / person / day (minimum requirement)◦ 100 liters / person / day◦ 150 liters / person / day During this time, the public use of peat water

directly to meet the needs of day-to-day(bathing, washing, cooking and evendrinking)
 Communities have some difficulties to getclean water and clean water availability is very
urgent 2 villages : Tanjung Leban and Tasik Betung.
 IPAG installation plan focused on the village

of Tanjung Leban for reasons :◦ access◦ Strategic location◦ supporting factors are sufficient

IPAG : Instalasi Pengolahan Air Gambut(Peat Water Treatment Plant)No

Parameter

Method1

pH

pH meterIn situ2

Turbidity

Turbidimeter

Laboratory

3

TDS

4

Salinity, Temp

5

Conductivity

6

TOM

Conductivity /TDS meter
Water Quality Checker(WQC)
Conductivity /TDS meter
Titrimetry

Laboratory

In situ
In Situ
In Situ

7

Fe - Mn

Spectrophotometry

Laboratory

8

Nitrite, Nitrate,
Ammonium, TN,
TP,Sulfat, Colour
E. Coli, Coliform

Spectrophotometry /Spectrophotometer DR
2000
PlatecountLaboratory

9

In Situ

Site 4 (Tanjung Leban river)Site 2 (Air Raja River)Site 1 (Parit Desa)

Location

Site 3 (Lintas Dusun River)Site 8 (SM deep well)

Site 6 (Zein Well, mineral soil)

Site 5 (Jauhari well)

Site 7 (Amirrudin well)

Prototype of IPAG30 tested in Middle Kalimantan(Patent No. : P00201000586)

 Neutralize the pH Absorb the color Precipitate particles

Children are enjoying clean water

Peat Water

Clean Water

No

Installation of IPAG

Standard

Peat
water

TCU

15

462 - 503

2-

Odourless

Odour

Odourless

Tasteless

Acid

Tasteless98.9 - 103

92

Parameter

Unit

1

Color

2

Odour

3

Taste

-

4

Conductivity

mS/cm

5

Turbidity

NTU/FAU

6

DO

mg/l

7

Temperature°C

8

Salinity%

5

Air temp.

Clean
water

4 - 11

1

3.9 – 8.2

4.6

29 – 30.2

24.3

0

0

No

Parameter

Unit

Standard

Peat
water

Clean
water

1

pH

-

6.5 – 8.5

3.52

6.7

2

Ammonia (NH3-N)

mg/l

1.5

0.511

0.310

1

E. Coli

Col/100 ml

3

Nitrat (NO3-N)

mg/l

50

1.109

0.064

2

Coliform

Col/100 ml

4

Nitrit (NO2-N)

mg/l

3

0.042

0.002

5

Total N

mg/l

0.323

0.167

6

Total P

mg/l

0.109

0.030

7

Sulphate (SO4)

mg/l

250

38.80

71.53

8

TOM

mg/l

-

438.2

19.86

9

TDS

mg/l

500

735

353

 Peopleare starting to understand theimportance of clean water for health Societyreceived with open arms IPAG
installation plan in the village TanjungLeban Communities willing tooperate properly and maintain IPAG in the
future IPAG mounting location has been agreed with
the Air Raja river (Site 2)

Water quality in the village of Tanjung Leban in theform of dug wells, boreholes and peat water fromrivers and lakes : most did not qualify as a source ofclean water or drinking water.
 Most of the main parameters (physical, chemical andbiological) did not meet the allowable thresholdbased on the Health Minister of Republic Indonesia
of. 492 in 2010
 Efforts are needed to improve the quality of the peat
surface water into clean water to provide the everyday
needs of local people
 Jar test trials conducted in two villages to treat water
peat has managed to find the optimal combination ofingredients needed, so that the water produced
meets health standards
NoParameter

Unit

Peat
water

Clean
water

0

70

0

0

630

0

Standard

Part IV

Dr. Ignasius D.A. Sutapa
Ignasius
D.A. Sutapa
Asia PacificDr.
Centrefor Ecohydrology
Research Centre
for Limnology - LIPI
Asia Pacific Centre for Ecohydrology
Jl. RayaBogorKm
46 - CSC,
Research
Centre for
Limnology- LIPI
Jl. Raya Bogor
Km 46 - CSC,
Cibinong–
Cibinong– Bogor– WestJava
Bogor – West Java
Email : ignasdas@yahoo.co.id
Email : ignasdas@yahoo.co.id

11/22/2013

1

Map of Indonesia
 Provisionof clean water is a major humanneed for survival and becomes the deciding
factor of human health and welfare.
 The availability of clean water reduced day by
day not in accordance with the humanpopulation growth.

 PDAM is the one of local company (belong

to local government) having task to deliverclean water.
 Level of coverage in clean water◦ 41,88 % of people in big cities◦ 13,94 % of people in villages◦ 27,05 % national average Difficulties
to deliver clean water inmarginal area :Air bersih
menjadi terbatas◦ Peatland areas◦ Coastal and small island areas◦ Flooding areas◦ Water polluted ares

6
11/22/2013

5

11/22/2013

 Peat water has characteristics including :◦ brownish red color,◦ containing high organic matter,◦ pH 2-5 and◦ sour taste◦ low hardness (Herlambang and Said 2005)

IslandProvince

Land Area(Ha)

Worthy of
Agriculture (Ha)6.244.101

2.253.733

SumateraRiau

4.043.600

774.946

Jambi

716.839333.936South Sumatera

1.483.662

1.144.851

5.072.249

1.530.256

Centre Kalimantan

3.010.640

672.723

West Kalimantan

1.729.980

694.714South Kalimantan

331.629

162.819Papua

7.001.239

2.273.160

TOTAL

18.317.589

6.057.149

 The total area of peatlands in Indonesia reaches± 21 million ha, of which includes the island of
Sumatra, Kalimantan, and Papua
 Based on the data from Litbang BB SDLP (2008),
distribution of the largest peatland is located on
the island of Sumatra in Riau Province andKalimantan, Central Kalimantan province

Kalimantan

Peatwater for daily needs
 The results of preliminary showed that Sala River

water, in Katingan Central Kalimantan and Air
Raja river water in Bengkalis Riau Province. In
terms of quality, belongs to the class C with
polluted status based on method STORET score(Minister of Health Regulation No. 115/2003).
 Peat water has very limited use such as for local
fisheries.
 The purpose of this study was to test IPAG60 in
improving the quality of peat water into cleanwater, in two locations : Tanjung Leban village in
Riau Province and Bana Hiyang village in Central
Kalimantan Province.
10 Peat water treatment into clean water :IPAG60 Two locations: Katingan, Central Kalimantan

Province and Bengkalis, Riau Province.

 IPAG characteristics :◦ continuous flow
◦ production capacity of 3600 liter/hour◦ type IPAG60◦ serie no IPAG 60-16DA5-001/V/2011

11/22/2013

Training courseof IPAG60

13

Training of IPAG60

11/22/2013

14

11/22/2013

Physical quality analysis
 These parameters are taken to see the characteristics of the peat water including:color detected by a colorimeter at 455 nm wave length, taste, conductivity
measured by conductivity TDS meter, turbidity using turbidymeter, temperatureand salinity measured by Water Quality Checker (WQC).
Chemical quality analysis


Water quality measurements based on non-metallic and metallic parameters
content. Analysis of non metallic content with titration method include: pH,sulphate, total organic mater (TOM), ammonia, nitrate, nitrite, hardness, cyanide,
fluoride, total N, phosphate, total P, and phenol. Analysis of the metal content
includes: Hg, As, Fe, Cd, Zn, Cu, Pb, Mn, Ca, Mg, total Cr by the method ofspectrophotometry with DR 2000 Spectrophotometer.
Biological quality analysis
 Pollutant indicator bacteria observed were total E. coli and Coliform. Colony countmethods (OLIVER 1999) using peat water samples from selected rivers. 0.45 nm
porous cellulose membrane placed on a sterile filter device using tweezers.

15

 Water

11/22/2013

quality analysis and assessment weredone by the of STORET scoring method(Minister of Health Regulation No. 115/2003).
 This method aims to determine the status ofwater quality that is commonly used.
 The principle of this method is to determine
water quality status by comparing water
quality data with water quality standards inaccordance with its designation (MOE Decree
No. 115 of 2003).

Number of
Parameter<10>10Classification

Parameter
ValuesPhysicalChemical

Biologycal

Maximum

-1

-2

-3

Minimum

-1

-2

-3

Average

-3

-6

-9

Maximum

-2

-4

-6

Minimum-2-4

Average
Status-6-12
Water quality

Class AGoodClass B

Fair

Meet thestandard

-6
-18
Score
0

Ligthly polluted-1 s/d -10

Class C

Bad

Polluted-11 s/d -30

Class D

Very Bad

Highly polluted

≥ -31

No
1
2
3
4

Unit

Standard*)

Peat water

Score

TCU
NTU/FAU

15
No odourNo taste
5

374
Odour
Acid
25

-1
-1
-1
-3
-6

Parameter

ColourOdour
Taste
Turbidity
Total skor

Clean
water
By IPAG4
No odourNo taste
0

Scor
e
0
0
0
0
0

Parameter

Unit

Standard *)1

Colour

TCU

15

2
3
4

Odour
Taste
Turbidity
Total skor

NTU/FAUNo odourNo taste
5

Peat
water

Scor
e

462 503
Odour
Acid
7.5

-1

Standard
*)

Peat
water

Score

1

Total (Fe)Manganese(Mn)pH
Ammonia(NH3-N)
Nitrate (NO3N)
Nitrite (NO2-N)
Total N
Phosphate(PO4-P)
Total P
Sulphate (SO4)TOM
Hardness
Total score

mg/l

0,3

0,414

-2

Clean
water
By IPAG
0.021

mg/l

0,1

0,061

0

0.038

0

-

6.5 – 8.5

2.82

-2

7.0

0

mg/l

1.5

ttd

0

0.027

0

mg/l

50

0.177

0

0.308

0

mg/l
mg/l

3
-

0.036
0.566

0
0

0.006
0.859

0
0

mg/l

0.2

0.429

-2

0.014

0

mg/l
mg/l
mg/l
mg/l

0.2
400
500

0.939832.21
619.42
Ttd

-2
0
0
Ttd
-8

0.017
3.894
0.617
ttd

0
0
0
0
0

Peat water

Score

78

-3

Clean water
By IPAG
0

109

-3

0

3

-1
-1
-1
-4

Clean
water
By IPAG
2

5
6
7

Score

No odourNo taste
1

0

8
9
10
11
12

0
0
0
0

0
0
-2

Clean water
By IPAG< 0.009
0.039
6.7

0.511

0

0.310

0

1.109
0.042
0.323
0.109
38.80
438.16
735

0
0
0
0
0
0
-2
-4

0.064
0.002
0.167
0.030
71.53
19.86
353

0
0
0
0
0
0
0
0

Parameter

Unit

Standard*)

1
2
3

Total (Fe)Manganese (Mn)pH
Ammonia (NH3N)
Nitrate (NO3-N)
Nitrite (NO2-N)
Total N
Total P
Sulphate
TOM
Hardness
Total score

mg/l
mg/l
-

0.3
0.1
6.5-8.5

Peat
water
0.174
0.071
3.52

mg/l

1.5

mg/l
mg/l
mg/l
mg/l
mg/l
mg/l

50
3
0.2
250
500

5
6
7
8
9
10
11

Unit

4

No

4

Parameter2

Physical analysis for Air Raja River
No

No

Score

Score
0
0
0

No Paramete Standard*r)0
E. Coli1(col/100ml)

Coliform

20(Col/100ml)

Total

0

Score
0
0

-6

No

Parameter

Standard*)

Peat water

1

E. Coli

070

Coliform

0

630

2

Score

(col/100ml)(col/100ml)

Total

0

Score

Clean
water
By IPAG

Score

-3

0

0

-3

0

0

-6

0

Peat Water : before and after treatment
Peat Water

Clean Water
No
1
2
3

Total

Parameter
PhysicalChemical
Biology

Before
-5
-8
-6
-19

Status of water quality

Polluted

No
1
2
3

Before
-4
-4
-6
-16

Total

Parameter
PhysicalChemical
Biology

Status of water quality

Polluted

After
0
0
0
0
Meet thestandard
After
0
0
0
0
Meet thestandard IPAG60 was tested in two locations: Sala Riverin Central Kalimantan Province and Air Raja
River in Riau Province.
 Based on water quality analysis using theclassification score, it can be concluded that
IPAG60 has been able to increase the qualityof peat water coming from two different
rivers, from class C (polluted status) to class
A (good status).
 It will provide an alternative technology toprovide clean water access in peat land areasLecture 5: Biodiversity and Ecosystem Services in the Context of
Global Change
Osamu SAITO (Institute for Sustainability and Peace, United Nations University)
This session is intended to provide graduate students with a multidisciplinary perspective andunderstanding of biodiversity, ecosystems and agriculture, and their interactions, and integration of humanneeds and ecosystem functioning in land management systems. The session will overview global
consequences of ecosystem changes and drivers of these changes, to introduce sustainable approach to
ecosystems management at different levels.
The session will introduce biodiversity in the context of global change with overview of several
global initiatives on assessment of biodiversity and ecosystem services. The session will then disusescondition and trend of food and biofuel production and explore the potential of ecosystem service andagrodiversity for sustainable food and biofuel production. Finally, the session will examine the linkage ofland use to global environment challenges, especially land degradation, climate change and loss of
biodiversity in production landscapes and also look at sustainable land management approaches tointegrate full range of ecosystem services at various levels. The session will give particular attention to
local knowledge and innovations in dealing with global environmental changes.
1. Global change and challenges of biodiversity-

Assessment Landscape-

Global Environmental Outlook (GEO) & Global Biodiversity Outlook (GBO)

2. Convention on Biological Diversity (CBD)-International Target and Strategy-

National Target, Strategy, Policy Tools

3. Millennium Ecosystem Assessment (MA)-MA Conceptual Framework-

Global and sub-global assessments and scenario analysis

4. Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES)-

Focus and functions of IPBES-

Traditional knowledge and ecosystem management

5. Agrodiversity and resilience for sustainable bioproduction-

Agrodiversity and Resilience-

Management approaches

6. Summary and conclusionReferences
Books:
Millennium Ecosystem Assessment, 2005. Ecosystem and Human Wellbeing: Synthesis. Island Press,137pp.
< http://www.maweb.org/documents/document.356.aspx.pdf>.
Brian Walker, David Salt and Walter Reid, 2006. Resilience Thinking: Sustaining Ecosystems and People
in a Changing World, Island Press, 192pp.
Anantha Kumar Duraiappah, Koji Nakamura, Kazuhiko Takeuchi, Masataka Watanabe, Maiko Nishi (ed)(2012) Satoyama-Satoumi Ecosystems and Human Well-Being: Socio-Ecological Production Landscapesof Japan, United Nations University Press, 480pp.
Fiktret Berkes, Johan Colding & Carl Folke (ed) (2003) Navigating Social-Ecological Systems: Building
Resilience for Complexity and Change, Cambridge University Press, 393pp.
Journal articles:
Charles Perrings, Anantha Duraiappah, Anne Larigauderie, Harold Mooney, “The Biodiversity andEcosystem Services Science-Policy Interface”, SCIENCE, 10 February 2011
Anne Larigauderie and Harold A Mooney “The Intergovernmental science-policy Platform on Biodiversity
and Ecosystem Services: moving a step closer to an IPCC-like mechanism for biodiversity”, Current
Opinion in Environmental Sustainability 2010, 2:9–14
K. Takeuchi, 2010. Rebuilding the relationship between people and nature: the Satoyama Initiative. EcolRes (2010) 25: 891–897
Thomas, R. J. 2008. Addressing land degradation and climate change in dryland agroecosystems throughsustainable land management. Journal of Environmental Monitoring, Volume 10, Number 5, May 2008
United Nations documents and documents of other international organizations:
Secretariat of the Convention on Biological Diversity (2010) Global Biodiversity Outlook 3. Montréal, 94
pages. <http://www.cbd.int/doc/publications/gbo/gbo3-final-en.pdf>
Japan Satoyama Satoumi Assessment, 2010. Satoyama-Satoumi Ecosystems and Human Well-being: Socioecological Production Landscapes of Japan – Summary for Decision Makers. United Nations University,
Tokyo, Japan, 36pp.

Outline
1. Global change and challenges of biodiversity
 Assessment Landscape Global Environmental Outlook (GEO) & Global Biodiversity Outlook (GBO)

2. Convention on Biological Diversity (CBD)

Biodiversity and Ecosystem
Services in the Context of Global
Change International Target and Strategy
 National Target, Strategy, Policy Tools

3. Millennium Ecosystem Assessment (MA) MA Conceptual Framework
 Global and sub-global assessments and scenario analysis

4. Intergovernmental Platform for Biodiversity and Ecosystem
Services (IPBES)

9 December 2013

 Focus and functions of IPBES
 Traditional knowledge and ecosystem management

Osamu Saito, Ph.D

5. Agrodiversity and resilience for sustainable bioproduction

Academic Programme Officer
Institute for Sustainability and Peace
United Nations University
E-mail: saito@unu.edu



Agrodiversity and Resilience
Management approaches

6. Summary and conclusion
2

1

Global Assessment Landscape
2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

GIWA
MA
WWDR

WWDR2

WWDR3

FRA2005

FRA2010LADA

1. Global change and challenges of
biodiversity

IPCC3

IPCC4

IPCC5

GBO2

GBO3
CAWMA

GEO2

GEO3

GEO4

GEO5
IAASTD
AoA(GMA)GIWA – Global International Water Assessment; MA – Millennium Ecosystem Assessment; WWDR –World Water Development Report; FRA – Forest Resources Assessment; LADA – Land Degradation
Assessment; IPCC – Intergovernmental Panel on Climate Change; GBO – Global Biodiversity Outlook;
CAWMA – Comprehensive Assessment of Water Management in Agriculture; GEO – Global
Environmental Outlook; IAASTD – International Assessment of Agricultural Science and Technology
for Development; AoA (GMA) – building the foundations for Regular Process for the Global Reportingand Assessment of the state of the marine environment, including socio-economic aspects.
3(Source) A.K. Duraiappah (2009) Ecosystem Services: The Global Assessment Landscape4

The GEO approach to integrated environmental assessment

Global Environment Outlook (GEO) GEO is a consultative, participatory

 Five Key Questions for GEO’s integrated approach:
1. what is happening to the environment and why?
2. what are the consequences for the environment and humanity?
3. what is being done and how effective is it?
4. where are we heading? and5. what actions could be taken for a more sustainable future?

process that builds capacity for
conducting integrated environmental
assessments for reporting on the state,trends and outlooks of the environment.
 GEO is also a series of products that
informs environmental decision-making
and aims to facilitate the interaction
between science and policy.
 The rigorous assessment process aims to
make GEO products scientificallycredible and policy relevant - providing
information to support environmental
management and policy development.

 The “integrated approach” to answering the questions above is anumbrella term for:
 linking the analysis of environmental state and trends with the policy

analysis;
 incorporating global and sub-global perspectives;
 incorporating historical and future perspectives; covering a broad spectrum of issues and policies; and integrating the consideration of environmental change and human well-being.

http://www.unep.org/geo/pdfs/geo5/
GEO5_report_full_en.pdf

56

An analytical framework for the economy-environment interaction

DPSIR Model

Driving forces

The Driver-Pressure-State-Impact-Response (DPSIR) framework/approach has been broadly
applied in environmental assessments and analysis.
Drivers are factors that result in pressures that in turn cause changes in the system. Bothnatural and anthropogenic forcing factors are considered; an example of the former isclimate variability while the latter include factors such as human population size,
associated urban development, demand for food, energy, etc.
Pressures include factors such as pollution, habitat loss and degradation. For example,
coastal development results in increased coastal armoring and the loss of associatedintertidal habitat.
State variables are indicators of the condition of the ecosystem (including physical,
chemical, and biotic factors).
Impacts comprise measures of the effect of change in these state variables such as loss of
biodiversity, declines in productivity and yield, etc. Impacts are measured with respect tomanagement objectives and the risks associated with exceeding or returning to below
these targets and limits.
Responses are the actions (regulatory and otherwise) that are taken in response to predicted
impacts. Forcing factors under human control trigger management responses when target
values are not met as indicated by risk assessments.

Anthropic activities and processes
that cause pressures: production(agriculture, industry, part oftransports…), consumption,
recreation outside the economic
system…
modify,substitute,removeResponses
Actions of the anthropic system to solve
environmental problems: pollution prevention
and reduction activities, economic“environmental damage” prevention andreduction, sustainable use of resources...°stimulate,ask for7

Pressures
generateeliminate,
reduce,prevent

Direct stresses from the anthropic systemon the natural environment: release ofpolluting substances (emissions to air, towater, waste...), radiation emissions,intake of natural resources, use of soil,
other changes of the natural environment
influence,
modifyrestorate,
influencecompensate,
mitigate

State
Conditions and tendencies in thenatural environment: air, water andsoil quality, global temperatures
evolution pattern...

Impacts

provoke,cause

Effects on the anthropic system due to changes inthe state of the natural environment: negative
consequences on human health, economic loss in
production activities, floods...§(Source) Cesare Costantino, Federico Falcitelli, Aldo Femia, Angelica Tudini (2003)

8

The Conceptual Framework of GEO

A generic DPSIR framework for water

9(Source) Peter Kristensen (2004)The DPSIR Framework

10

Multilateral Environmental Agreements (MEAs) The MEAs include global biodiversity-related treaties

and the “Rio Conventions”:<Global biodiversity-related treaties>

2. Convention on Biological Diversity(CBD) Convention on International Trade in Endangered Species of Wild Fauna andFlora (CITES),
 Conservation on Migratory Species (CMS),
 International Treaty on Plant Genetic Resources for Food and Agriculture(ITPGRFA), Ramsar Convention on Wetlands (Ramsar), and World Heritage Convention (WHC),<Rio Conventions> Convention on Biological Diversity (CBD/UNCBD)
 UN Framework Convention on Climate Change (UNFCCC)
 UN Convention to Combat Desertification (UNCCD)
11

12

What is biological diversity?

CBD entered into force on 29 December 1993. It has 3 main
objectives:
1.The conservation of biological diversity
2.The sustainable use of the components of biologicaldiversity
3.The fair and equitable sharing of the benefits arisingout of the utilization of genetic resources"Biological diversity" means the variability among livingorganisms from all sources including, inter alia, terrestrial,
marine and other aquatic ecosystems and the ecologicalcomplexes of which they are part; this includes diversity
within species, between species and of ecosystems. (fromArticle 2. Use of Terms)"Biological resources" includes genetic resources,
organisms or parts thereof, populations, or any other biotic
component of ecosystems with actual or potential use orvalue for humanity.

13

Definition of key terms

14

Global Biodiversity Outlook (CBO) "Ecosystem" means a dynamic complex of plant, animal Global Biodiversity Outlook is theand micro-organism communities and their non-living
environment interacting as a functional unit.

 Drawing on a range of information

flagship publication of the CBD.
sources, including National Reports,
biodiversity indicators information,scientific literature, and a study
assessing biodiversity scenarios forthe future, the third edition of Global
Biodiversity Outlook (GBO-3)
summarizes the latest data on status
and trends of biodiversity and draws
conclusions for the future strategy of
the Convention.

 "Habitat" means the place or type of site where anorganism or population naturally occurs.
 "Sustainable use" means the use of components ofbiological diversity in a way and at a rate that does notlead to the long-term decline of biological diversity,
thereby maintaining its potential to meet the needs andaspirations of present and future generations.
15

Threat status of species in comprehensively assessed taxonomic groups
Number of species

Red List Index
A Red List Index value of 1.0 indicatesthat all species in a group would beconsidered as being of Least Concern,that is not expected to become extinct in
the near future. At the other extreme, avalue of 0 indicates that all species in agroup have gone extinct.

Birds
Number of species
Amphibians

Birds

Amphibians

16

Mammals
Mammals

The proportion of warm-water coral,
bird, mammal and amphibian species
expected to survive into the near future
without additional conservation actions
has declined over time. The Red List
Index (RLI) for all these species groups
is decreasing. Coral species are moving
most rapidly towards greater extinction
risk, while amphibians are, on average,
the group most threatened.

(Source)GBO-3

17(Source)GBO-3

18

Conservation status of medicinal plant species in different geographic regions

Strategic Plan for Biodiversity 2011-2020
 In decision X/2, the tenth meeting of the

The greatest risk ofextinction occurs in thoseregions, Africa, SouthAmerica and the Pacific,
where medicinal plants aremost widely used.

(Source)GBO-3

Conference of the Parties (COP 10) held from 18 to
29 October 2010, in Nagoya, Aichi Prefecture,
Japan, adopted a revised and updated Strategic Planfor Biodiversity, including the Aichi Biodiversity
Targets, for the 2011-2020 period.
 This new plan will be the overarching framework
on biodiversity, not only for the biodiversity-related
conventions, but for the entire United Nations
system.
 COP10 agreed to translate this overarchinginternational framework into national biodiversity
strategies and action plans within two years.

19

2011-2020 Strategy: Strategic Goals

20

2011-2020 strategy: Selected Targets
 At least halve and where feasible bring close to zero the rate of loss

 Address the underlying causes of biodiversity lossof natural habitats including forests

 Reduce the pressures on biodiversity

 Protect 17 per cent of terrestrial and inland water areas and 10 per Safeguard biodiversity at all levels

cent of marine and coastal areas

 Enhance the benefits provided by biodiversity

 Restore at least 15 percent of degraded areas Provide for capacity-building Make special efforts to reduce the pressures faced by coral reefs
 Substantially increase in the level of financial resources in supportof implementation of the Convention

21

22

2011-2020 Strategy:
Strategic Goals

2011-2020 strategy: Implementation
 The “Aichi Target” will be the overarching framework on

biodiversity not only for the biodiversity-related
conventions, but for the entire United Nations system.
 Parties agreed to translate this overarching internationalframework into national biodiversity strategy and actionplans within two years
 Actions in support will also take place at sub-national andlocal levels

The Aichi Biodiversity Targets“Living harmony with nature”

Address the underlying
causes of biodiversity
loss

Strategic Goal A: Address the underlying causes of
biodiversity loss by mainstreaming biodiversity
across government and society

Reduce the pressures on
biodiversity

Strategic Goal B: Reduce the direct pressures on
biodiversity and promote sustainable use

Safeguard biodiversity at
all levels

Strategic Goal C: To improve the status ofbiodiversity by safeguarding ecosystems, species andgenetic diversity

Enhance the benefits
provided by biodiversity

Strategic Goal D: Enhance the benefits to all frombiodiversity and ecosystem services

Provide for capacitybuilding

Strategic Goal E: Enhance implementation throughparticipatory planning, knowledge management andcapacity building(Source) http://www.cbd.int/doc/strategic-plan/2011-2020/Aichi-Targets-en.pdf

23

24

The Millennium Ecosystem Assessment (MA) is: Designed to meet a portion of the assessment needs of Convention on Biological Diversity (CBD),

3. Millennium Ecosystem Assessment(MA) Convention to Combat Desertification (CCD), Ramsar Wetlands Convention,
 other partners including the private sector and civil society

 Focused on the consequences of changes in ecosystems forhuman well-being
 Undertaken at multiple scales (local to global)
 Designed to both provide information and build capacity toprovide information
 Expected to be repeated at 5-10 year intervals if it successfully

meets needs
First MA Product(2003)

MA Synthesis Report(2005)

MA Full Reports (2005)

26

25

Focus: Ecosystem Services
The benefits people obtain from ecosystems

The MA focuses on: Ecosystem services
 The consequences of changes inecosystems for human well being The consequences of changes inecosystems for other life on earth

27

Focus: Consequences of Ecosystem Change for
Human Well-being

28

The MA is an Integrated Assessment
IPCC looks at impacts of one driver (climate) on different systems; MA will
integrate the effects of multiple drivers on all ecosystems
DriverResponse

Human
ImpactEnergyFood
Biodiversity
Sector
SupplyHealthEconomics

IPCC
29

Climate Land Cover Biodiversity Nutrient
Etc.
Change Change
Loss
Loading

Climate Change

Water

SocialEcosystems

HealthEconomics

SocialMillennium Assessment
30

MA Framework

Scenarios are not predictions

 Basic material for a good life Health Good Social Relations
Human Security
Well-being
 Freedom of choice
and action

Indirect Drivers of Change Demographic
 Economic (globalization, trade,market and policy framework) Sociopolitical (governance andIndirect framework)
institutional
 Science
and Technology
Drivers Cultural and Religious

Complexity

Human Well-being andPoverty Reduction• Methods to help decision makers
systematically think about the future andhighdraw lessons for today’s decisions include
predictions, projections, explorations, andscenario analysis.
• Scenario planning (or scenario analysis)
has emerged as the most appropriate toolfor complex systems.
• The goal of scenario planning is toconsider a variety of possible futuresreflecting important uncertainties, rather
lowthan to focus on an accurate prediction oflowhigh
Uncertainty
a single outcome.
• Scenarios are stories about the future, told Tools for Addressing Future Uncertainty andComplexity
as a set of “plausible alternative futures”
about what might happen under particular (Source) WRI (2008) Ecosystem Services: A Guide for
Decision Makers,
assumptions (MA 2003).

Direct Drivers of Change

Ecosystem
Services Changes in land use
 Species
introduction or removal
Direct Technology adaptation and use
Drivers
 External
inputs (e.g., irrigation)
 Resource consumption
 Climate change Natural physical and biologicaldrivers (e.g., volcanoes)
31

32

Millennium Ecosystem Assessment's (MA) Scenarios

Scenario making process(e.g. UK’s Rural Futures）

 Not predictions – scenarios are plausible
futures Both quantitative models and qualitative
analysis used in scenario development

MA(2005) Millennium Ecosystem Assessment Findings
33

MA Scenarios

Population changes under different scenarios

Globalization
Global Orchestration:
A world with a “sociallyconscious” globalization thatemphasizes economic growth,social reform, and equity, butdecision makers take a reactive
approach toward environmental
problems.

34

TechnoGarden:This society explores thepossibilities of “green”
technologies to manage allcategories of ecosystem services
to support human systems in amore globalized world

Reactive

Proactive

Order from Strength:
Decision makers also only deal
with environmental degradation
when it starts to seriously affecthumans (reactive); at the sametime they focus on national
security issues and economic
improvements only for their own
countries.

Adapting Mosaic:
More regionalized world with afocus on experimentation, local
learning, and adaptations toecosystem change and theintroduction of more flexible local
governance structures forenvironmental and social
management.

Regionalization/Localization

35（Source）Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-being Synthesis

36Changes in Direct Drivers

Improvements in services can be achieved by 2050

（Source）Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-being Synthesis

Crop Land

Forest Area

（Source）Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-being Synthesis

37

38

The Japan Satoyama
Satoumi Assessment(JSSA)

Multi-scale assessment
 Includes information from 33 sub-global assessments

 Sub-global assessment of the Millennium

Ecosystem Assessment (MA) The main objective of the JSSA is to provide

scientifically credible and policy-relevant
information on the significance of ecosystemservices provided by satoyama and satoumi
and their contributions to economic andhuman development
 Studied from 2006 to 2010, and theassessment report was just published in 2010
for CBD/COP10 at Nagoya(Source)
Japan Satoyama Satoumi Assessment, 2010. Satoyama-Satoumi
Ecosystems and Human Well-being: Socio-ecological Production
Landscapes of Japan – Summary for Decision Makers.
United Nations University, Tokyo, Japan.
http://www.ias.unu.edu/resource_centre/SDM-EN_24Feb2011.pdf

39

Local and Indigenous Knowledge (LINK) in Socio‐Ecological
Production Landscapes (Satoyama and Satoumi)

40

Satoyama and Satoumi Landscapes providing
a “Bundle of Ecosystem Services”
Provisioning :Japan Satoyama Satoumi Assessment (2010) defines satoyama andsatoumi landscapes as dynamic mosaics of managed socioecological systems producing a bundle of ecosystem services forhuman well-being: Socio-Ecological Production Landscapes(SEPLs) Rice; Rice bran (used as fertilizer orfor the production of Japanese pickles);rice straw (for roofing materials, ropes
and straw sandals); seven spring herbs

Regulating : Flood control; Water retention, etc.

Cultural: Festival and events (including warshipof the “god of the water”, “god of ricepaddies”); Various cooperative
systems

Biodiversity:
Links between abundant rice paddy environments andlocal people involved in rice production (JSSA, 2012)

Satoyama

Satoumi(JSSA, 2010)41

 Habitat for biological organisms

benefited from year-round warm andshallow water, and fertile soil (Japanesecrested ibis, oriental stork, Crucian carp,
dojo loach, river snails, etc.)
42

110

Depopulation and Underuse

105

Tokyo
東京

National
全国
群馬

85
80

Gunma

75

Wakayama

70

和歌山
秋田

65

Akita

(104ha)

1990

1995

2030

Abandoned by
non-farmers

2000

2005

（Source）国立社会保障・人口問題研究所編（200７)日本の都道府県別将来推計人口 平成19年5月推計，厚生統計協会

43

Positioning and characteristics of four scenarios in JSSA

2025

Year

Abandonedby farmers

1985(Source)Japan Satoyama Satoumi Assessment, 2010. Satoyama-Satoumi Ecosystems and Human Well-being: Socioecological Production Landscapes of Japan – Summary for Decision Makers. United Nations University, Tokyo, Japan.

2020

60
2015

“indirect” drivers.

首都圏

90

2010

about by decreases in rural population,a shift from biomass energy to fossil
fuel and importation of cheap timber Under-use or even abandonment oftraditional satoyama and satoumi
landscape, which has led to negative
trends in health and overall well-beingin Japan Major consequences: Expansion of wild
animals, deterioration of quality ofdomestic forest resources, degradationof regulating services (carbon, flood
control, etc.,) and losing cultural
servicesTokyo MA952005

 Under-use of satoyama forest broughtsatoyama and satoumi in the last fifty years
 The human impact upon ecosystem services includes “direct” andPopulation index (2005=100)100

 The timeframe of the assessment is changes that have occurred in

2035

Conceptual framework of JSSA

44

Findings and data: The JSSA examines

four prospective
scenarios for satoyama
and satoumi in the year
2050.
 The vertical axis
examines governanceand economic
development in termsof localised versus
globalised approaches.
 Horizontal axis looks
at ecosystem service
management as natureoriented versus
technology oriented.

 Summary for Decision Makes(SDM) in English andJapanese Policy Brief in English
 6 cluster reports in Japanese National Report in English(2012 through UNU Press)
 National Report in Japanese(2012 through Asakura
Publishing)45(Source) JSSA(2010)

SATOYAMA
Initiative

46

Vision: Societies
in harmony with nature
長期目標：自然共生社会

Three-Fold Approach:

1. Consolidate wisdom on securing diverse
ecosystem services and values
2. Integrate traditional ecological knowledge
and modern science
3. Explore new forms of co-management systems
Resource use
within the carrying 3つの行動指針：
capacity and resilience 1. 多様な生態系のサービスと価値の確保のための知恵の結集
of the environment
環境容量・自然復元力の 2. 伝統的知識と近代科学の融合
3. 新たな共同管理のあり方の探求
範囲内での利用

Cyclic use ofnatural resources
自然資源の循環利用

Contributions
to socio-economies

4. Intergovernmental Platform for
Biodiversity and Ecosystem Services(IPBES)

社会経済への貢献

Multi-stakeholder
Recognition ofparticipation andthe value andcollaboration
importance of local
多様な主体のtraditions and cultures
参加と協働
地域の伝統・文化の価値と重要性の認識

47

Five Key Perspectives in the approach
5 つ の 視 点

47

48

IPBES: Current Status

IPBES and Scientific communities

Scientific Background: Many ecosystem services related assessments

Policy makers,
stakeholders Fragmented, multiple frameworks and methodologies
 Scientific credibility varies
 Regularize the Millennium Ecosystem Assessment (MA) which focused on ecosystemservices and human well-being
Institutional Background:
Established on 21 April 2012 in Panama, IPBES is an independent intergovernmental bodywhich aims to provide scientific support for policy-making in the area of biodiversity andecosystem services. The four key functions of IPBES are to provide：

1.
2.
3.
4.

3

Policy support

Regular and timely assessments
Knowledge generation
Support policy formulation and implementation
Capacity building

4

Capacitybuilding

1

Assessments

The Multidisciplinary Expert Panel (MEP) of IPBES is preparing a common conceptual
framework that facilitates and realizes these four functions by the next IPBES Plenary(IPBES-2) scheduled for December 2013 in Antalya, Turkey.

IPBES Plenary-1 in Bonn, Germany, 21-26 Feb. 2013

Requests for
information to the
Plenarygaps

2
Catalysing knowledge generation

49

IPBES Work Programme Objectives
The Plenary in its decision IPBES/1/2 requested a draft work programme for 2014-2018 with a sequenced andprioritized set of objectives, deliverables, actions and milestones for advancing the four functions of IPBES atrelevant scales. In order to deliver on the four functions of IPBES at relevant scales in a coherent andintegrated manner, five cross-cutting objectives are proposed, and the work programme deliverables are
clustered under these objectives. The five objectives are to:a) enhance the enabling environment for the knowledge-policy interface for biodiversity and ecosystemservices;b) strengthen the knowledge-policy interface on biodiversity and ecosystem services on regional and subregional levels;c) strengthen the knowledge-policy interface with regards to thematic and methodological issues;d) strengthen the knowledge-policy interface on the global dimensions of changes in biodiversity andecosystem services; ande) communicate and evaluate IPBES activities, deliverables and findings.

Scientificcommunities

50

Conceptual Framework used by Millennium Ecosystem
Assessment (MA)
Human Well-being andPoverty Reduction
 Basic material for a good life Health Good Social Relations
Human Security
Well-being
 Freedom of choice
and action

Indirect Drivers of Change Demographic
 Economic (globalization, trade,market and policy framework) Sociopolitical (governance andIndirect framework)
institutional
 Science
and Technology
Drivers Cultural and Religious

Direct Drivers of Change

Ecosystem
Services Changes in land use
 Species
introduction or removal
Direct Technology adaptation and use
Drivers
 External inputs (e.g., irrigation)
 Resource consumption
 Climate change Natural physical and biologicaldrivers (e.g., volcanoes)
52

51

Key Questions derived from proposed IPBES conceptual
framework

Objectives of IPBES Conceptual Framework (CF) The IPBES CF is a model of how social-ecological systems work, a concise

1. How have changes in HWB caused changes of the productive base frombiodiversity and ecosystem functioning? (Arrow1)
2. How have changes in biodiversity & ecosystem functioning and other forms ofwealth influenced the supply of the ecosystem goods &services and otherservices? (Arrow 2)

summary in the form of a narrative and graphics that depicts key social andecological components, and the relationships between these components,
providing common terminology and structure for the variables that are ofinterest in the systems under analysis and facilitating scaling up and down
between assessments conducted at different scales.
 IPBES recognizes and considers knowledge systems other than science,
including indigenous and local knowledge systems, which can becomplementary to science-based models and can reinforce the delivery of
IPBES functions.
 CF is a tool to achieve a common vision and a shared language across thedifferent disciplines, knowledge systems and stakeholders that are expected tobe active participants in the Platform.
 IPBES CF is intended to be a working agreement, a basic common
understanding on how social-ecological systems work, as a basis for
coordinated action towards the achievement of the four functions and theultimate goal of the Platform.

3. How have changes in the access and useof ecosystem goods &services and othergoods & services influenced HWB?
(Arrow 3)
4. What policy interventions in the form of
institutions and decisions might beneeded to mainstream management of
ecosystem into broader social, economic,
and environmental planning?
5. How to take into the impacts of decisions
by harnessing synergies and resolving
trade-offs among natural and other forms
of wealth.
IPBES/1/INF/9

53

54

Agrodiversity
 Agrodiversity broadens the concept of agricultural

biodiversity from meaning simply genetic resources
to meaning the landscape level biodiversity, and thediversity of local social organizations andtechnologies that support biodiversity and reduce
agricultural and ecological risks.
 Substantial knowledge gaps remain regarding
multiple functions and values of agrodiversity forlivelihoods (food security, income generation, andresilience to market fluctuation and environmental
change, etc.) and ecosystem health (conservation of
biodiversity, pest regulation, nutritional and water
cycles, carbon sequestration, and etc).
 Special attention should be placed on integrated
approaches to management of interactions ofagrodiversity components rather than one single
component per se, especially diversity management
at landscape level.

5. Agrodiversity and Resilience forsustainable bioproduction

Agrodiversity and Resilience
Management approaches

56

55

Adaptive Cycle and Resilience

Terminology used by the Resilience Alliance (according
to Holling & Gunderson, 2002)

The approach of the Stockholm Resilience center and the Resilience Alliance

Definition

Ecosystem
resilience

Ecological resilience can be measured in terms of the magnitude of
disturbance that can be absorbed before the ecosystem reconfigures itself. It
focuses on the requirement to maintain existence of function, accepts
variability and the concept of multiple states of equilibrium. This approach
recognises the inextricability of the dynamics linking society, naturalprocesses and man-made systems understanding that the longer a social
system is ‘locked in’ to an established power structure or set of behaviours
and beliefs, the greater its vulnerability to unexpected and unforeseen changes
inthe environment.

Engineering Rate and speed of return to pre-existing conditions after disturbance.
It focuses on a requirement for efficiency and predictability in man-maderesiliencesystems and processes and assumes the existence of a single, optimal state. It
treats ecological system feedbacks as separable and manageable externalities.
It strives to contain or remove risks, and tends to assume the existence of a
global, rational actor, rather than acknowledging the different motivations ofindividuals, or social complexity. This concept of resilience is currently mostpervasive within society, organisations and institutions.

The Adaptive cycle as it proceeds through the four phases: exploitation (organization into a
new political/social system), conservation (maintenance and proliferation of the new system),release (revolution) and reorganization (regime change/a new paradigm).
By understanding adaptive cycles you gain insight into how and why a system change;
develop a capacity to manage for a system’s resilience.

Adaptive Cycle(Gunderson and Holling, 2002)
more
The range ofaccumulatedresources such as
economic, socialand environmental
capital that areavailable andaccessible

再編
Reorganization

Conservation

Potential

Term

Exploitation

ReleaselittleweakConnectedness

strong

The internal controllability of a system

57

58

Diversity, Redundancy, Resilience

Specified vs. General Resilience (Walker, 2009) Diversity:
 “Specified” resilience deals with the resilience "of what, to what"(e.g., the resilience of crop production to a drought).
 “General” resilience does not consider any particular kind ofshock, or any particular aspect of the system that might be affected.
The distinction between these two aspects of resilience is importantbecause there is a danger in focusing too much on known or suspected
thresholds. If all the attention and resources of management arechanneled into managing for identified (specified) resilience andassociated thresholds, the management may inadvertently be reducing
resilience in other ways – resilience to completely novel ‘surprises’.
There is therefore a need to consider both general and specified
resilience.
59“The combination of a diversity of ecological function at specific scales and thereplication of function across a diversity of scales produces resilient ecological function.”“Ecological organization at a specific scale is determined by interactions between species
and processes operating within that scale. Competitive interactions are strongest among
species that have similar functions and operate at similar scales. These interactions
encourage functional diversity within a scale, and the distribution of ecological function
across scales, enhancing cross-scale resilience.

 Redundancy:“The apparent redundancy of similar function replicated at different scales adds
resilience to an ecosystem: because disturbances are limited to specific scales, functions
that operate at other scales are able to persist.”“Overlap of ecological function enables an ecosystem to persist. Compensation masks
ecosystem degradation, because while a degraded system may function similarly to an
intact system, the loss of redundancy decreases the system’s ability to withstanddisturbance or further species removal.”(Source) Peterson, Garry; Allen, Craig R.; and Holling, C. S., "Ecological Resilience, Biodiversity, and Scale" (1998).
Nebraska Cooperative Fish & Wildlife Research Unit -- Staff Publications. Paper 4.
http://digitalcommons.unl.edu/ncfwrustaff/4

60How to assess resilience?

Building Resilience (Walker, 2011)

Step 2: System dynamics(resilience analysis)

Step 3: Intervention
options Resilience of what

 Specific resilience Preparedness,

 Resilience to what

 General resilience

Step 1: Define systems

 Step 1: Define systems
 Resilience of what
 Resilience to what

 Step 2: System dynamics (resilience analysis)
 Specific resilience General resilience Transformability

option & capacity
 Intervention

 Transformability

SystemShock/
DisturbanceSocioecologicalsystems, bioproduction
system Step 3: Intervention options
 Preparedness, option & capacity
 Intervention

Climate andecosystemchangesThreshold/limit

Adaptability

Preparedness

Intervention

Environmental
degradation andadaptation ofagricultural
field

Traditional
knowledge,
technology,
coping strategy,
alternate crop
species

Early warning
system,education,
biodiversity,
information
technology

Governance, tax,
penalty,regulation,
payment toecosystem
services (PES)

Based on Walker (2011)(Source) 2011/03/13 09:55 Brian Walker, "Resilience Propositions on Trial: Briefing for the Mock Court", Resilience 2011,
Arizona State University, http://coevolving.com/commons/20110313-0955-walker-resilience-propositions-on-trial

61

Increasing Extreme Weather Events Caused by Climate andEcosystem Changes and Enhancing Resilience
River
flow

Payment for ecosystem services (PES)
A Step-by-Step Approach to Developing PES Deals

flood

FLOODS The practice of offering

Enhancing
Resilience*drought
Global/present
Sub-global
Short-term
Globallevel

EWS

National/state level(Early Warning System)■Short-term precipitation/ flood
forecasting■hazard map making

Village/community level

Local

Short-term Risk
Management

DROUGHTS
futureLong-term
Monitoring andLong-term Forecasting●Ecosystem Change Forecasting■Global/Long-term Observation

Adaptive Management
System●Enhancing agricultural productivity and storage capacity, combined with the use ofindigenous species (drought resistant, early maturing variety etc.）
Agroforestry●Value adding to tree crops through the promotion of agroforestry■Wider scale irrigation/water management system building, using the existing
Resilience enhancement
infrastructure （dam, reservoir, well etc)
option：■Rainwater harvesting system●Agro-ecosystematic▲Risk management via EWS and adaptive management based on future scenario■Engineering▲Social▲Commercialization of ecosystem services and spontaneous natural resource
management (terroir management）(Source)CECAR-Africa, 2011

Traditional Homegarden Systems in Rural Asia

Common Features

Typical Layout

Unique Characteristics• Garden-Pond-Livestock bioproduction system• High biodiversity and material circulation
within VAC• Many combination variety of local businessmodels and ecosystem management systems• Combined system of agroforestry and livestock
production including inland aquaculture (e.g.
catfish)• Function as important food source to survive at
Asian currency crisis• Spice cultivation is a typical source of cash
income from homegardens• Certification scheme has been introduced tohomegardens and the their harvests (spices) toimprove livelihood of small farmers withprotecting biodiversity and natural environment.

Mohri et al (2013) Assessment of ecosystem services in homegarden systems in Indonesia, Sri Lanka, and Vietnam, Ecosystem Services,5: 124-136.

(Source)- Tacconi, L. (2012). Redefining payments for environmental services. Ecological Economics, 73(1): 29-36- UNEP (2008) Payments for Ecosystem Services: Getting Started: A Primer,
http://www.unep.org/pdf/PaymentsForEcosystemServices_en.pdf

64 Homegarden systems provide a broad bundle of ecosystem

VAC system

Bundle of various
in Vietnam
ecosystem services
from integrated
agriculture–aquaculture
system (joint
production) with higher
Pekarangan
biodiversity
in Indonesia
 Higher resilience:
Securing multiple
alternatives against
unforeseen shocks or
disturbances New business models: Kandyan homegarden
in Sri Lankatransform traditional
production system tomodern businessmodelsincentives to farmers or
landowners in exchange
for managing their land toprovide some sort ofecological service.
 "a transparent system forthe additional provision ofenvironmental services
through conditional
payments to voluntaryproviders.“(Tacconi,
2012)

Step 1: Identifying Ecosystem Service Prospects & Potential
BuyersDefining, measuring, and assessing the ecosystem services in aparticular area
Determining marketable value
Identifying potential buyers who benefit from the service
Considering whether to sell as individuals or as a group
Step 2: Assessing Institutional & Technical Capacity
Assessing legal, policy, and land ownership context
Examining existing rules for PES markets and deals
Surveying available PES support services and organizations
Step 3: Structuring Agreements
Designing management and business plans to provide theecosystem service that is the focus of the PES deal
Reducing transaction costs
Reviewing options for payment types
Establishing the equity and fairness criteria for evaluating
payment options
Selecting a contract type
Step 4: Implementing PES Agreements
Finalizing the PES management plan
Verifying PES service delivery and benefits
g the deal
Monitoringg and evaluating

Case Study: General Resilience of Homegarden in Asia

Based on the field survey of homegarden systems in Vietnam, Indonesia and Sri Lanka,
we found that traditional homegarden systems have relatively higher resilience against
various changes including demographic change, climate changes, and commercializationof bio-production system.



62

65

services that can provide more general resilience against
unforeseen events other than climate unpredictability, suchas sources of diverse and nutritious diets where food priceshocks render market sources unaffordable, or stored value
in multilayered timber canopies providing insurance against
unanticipated or infrequent family expenditure.
 Homegardens do not always require full-time maintenanceand are to some extent self-sufficient, but partial reductionof labor inputs for weed removal, drainage, harvesting,replanting, fencing, and composting can lead to secondary
forest succession, resulting in loss of services important towell-being.
 On the other hand, commercial simplification, especially fornutmeg in Kulugammuna and pepper in Godammuna,increases some provisioning services (primarily income) but
has negative implications for regulating, supporting, andcultural services.
66

Options to enhance resilience and cope with various
drivers
International level Generally, abandonment and commercialization■Certification system: premium
price to environmentallyfriendly value-added productsof homegardens can reduce the resilience of
local livelihoods by risking a number of
ecosystem service benefits, especially:

Regional to national• reduced income diversity from harvested produce;• lost traditional knowledge;• reduced species diversity, especially in cultivation ofannuals;• increased soil erosion, loss of fertile topsoil, anddownstream paddy siltation in heavy rains due toreduced drainage management;• increased wild animal incursion as abandoned plots
become attractive habitats; and• increased reliance on rising prices in import-heavy
markets for food security.

67■Improvement of irrigation systems andgovernance for better allocation of water■Payment to ES or local procurement
incentives
Community level ■Amending Seed Act to permit local
resilient seed banking and trade■Crop variety:incorporating higher yield,
locally sourced crop varieties
Household levelwith lower water■Incentives to multirequirements and higher
cropping andresilience to highmultiple ecosystemtemperatures
services■Animal control:■Introduction of
Locally appropriate wild animal
Drought and animal
controlresilient crops■Soil erosion control:■On-site rainfall
traditional lock-and-spillcollection
drains■Community-pooled labour

lock-and-spill drain

68

Summary
1. Global change and challenges of biodiversity
2. Convention on Biological Diversity (CBD)
3. Millennium Ecosystem Assessment (MA)
4. Intergovernmental Platform for Biodiversity and Ecosystem
Services (IPBES)
5. Agrodiversity and Resilience for sustainable bioproduction

6.Summary and conclusion

Conclusion
Credible scientific assessment of biodiversity and ecosystem services
is crucial and provides a base to develop and implement effective
policy options for their sustainable use and enhancement of resilience.

(Source) UNEP (2008) Payments for Ecosystem Services: Getting Started: A Primer,
http://www.unep.org/pdf/PaymentsForEcosystemServices_en.pdf

6970

Lecture 6: Integrated Sediment Management
Tetsuya SUMI (Water Resources Research Center, DPRI, Kyoto University)

Dam construction dramatically influences the river basin balance with respect to water sedimentinflow and outflow, which upsets downstream reaches. The objective of water and sediment incorporation
is to manipulate the river-reservoir system to achieve sediment balance while maximizing the beneficial
storage, and minimizing environmental impacts and socioeconomic costs. Such costs could be avoided if
reservoir sedimentation was minimized and dams were allowed to live forever.
In order to improve the above situations, the following measures are currently proposed andimplemented in Japan which is one of leading countries conducting intensive research and development on
reservoir sedimentation management.
Conservation of flow regime
River instream flow
Flexible dam operation
Flushing flow
Conservation of sediment flow
Sediment augmentation (replenishment)
Large scale sediment management measures (Sediment bypass, Sediment flushing)
Several methods for sediment management are available and have been implemented in practice.
These methods can be categorized as shown in Figure 1.
The necessity for the reservoir sediment management in Japan can be summarized as: (1) to preventthe siltation of intake facilities and aggradations of upstream river bed, accompanied by the sedimentation
process in reservoirs; (2) to maintain storage function of reservoirs, and realize sustainable water resourcesmanagement for the next generation; (3) to release sediment from dams as a perspective on comprehensive
sediment management in a sediment routing system.
The objectives of this lecture are: (a) to make a general review of impacts of reservoir
sedimentation both on reservoir sustainability and downstream river reach; (b) to select suitable options for
sediment management; (c) to study in detail several sediment management options such as sediment
bypassing, flushing and sediment augmentation/replenishment; (d) to consider environmental impacts of
sediment management; (e) to draw some general conclusions in terms of future strategies for sedimentmanagement.
It is common practice to remove accumulated coarse sediment by excavation and dredging, and to
make effective use of the removed sediment. Sediment augmentation/replenishment method is one of new
measures of sediment management. In this method, trapped coarse sediment is periodically excavated (ordredged depending on the site conditions) and then transported and placed temporarily on the channel
downstream of the dam, in a manner decided according to the sediment transport capacity of the channel
and the environmental conditions. The augmentation/replenishment process is efficient to restore the bed
load transport and the associated habited by coupling reintroduction with floodplain habitat restoration.

Sediment flushing is one of the most attractive methods from the costs and sediment supply point ofview. It is important to predict how anticipated phenomena will impact on the environment and to conduct
studies to develop feasible measures that minimize them. An analysis of sediment flushing operations todate, the effect of sediment flushing on the river’s sediment balance and physical environment is presented.
Sediment bypassing is also unique and effective sediment routing measure which is suitable to steep rivers
with high sediment yield such as in Japan and Swiss Alps. How to design and operate the sediment bypass
tunnel is now interesting topic on hydrology, hydraulics and stream ecology.

Sustainable use of reservoir
Sediment management approach
Upstreamof reservoir

Withinreservoir

At the dam

Downstream

Soilconservation

Dry
excavation

Density
current

Sediment
replenishment

Slope & bankprotection

Dredging/
HSRS

Dam raise

Dilution

Sediments
traps

Flushing

Bottomoutletmodification

Monitoring

Bypassing

Off‐channel
storage

SettlingbasinsRinse out

Figure 1: Inventory of measures for sediment management

Worldwide distribution of dams
INTEGRATED SEDIMENT MANAGEMENT

Tetsuya SUMI
WRRC, DPRI, Kyoto University
Bypassing, Miwa dam

Flushing, Dashidaira dam

Cascade Dams, Tenryu river

The Twenty-third UNESCO - IHP Training Course on
Ecohydrology for River Basin Management under
Climate Change, Dec.2-13, 2013.

Data from the GRanD database © Global Water System Project, 2011

Dam Impacts caused by reservoir sedimentation

Sediment yield map of the world Dam construction dramatically influences the river basinbalance for water / sediment inflow and outflow.

Sedimentation in reservoir
Dam Impacts• Discontinuity of

sediment transport
downstream• Modification of flow
regimes downstream• Reduction of storage capacity

Downstream geomorphology• Bed armouring and degradation

Downstream hydrology• Water table lowering, changes
in seasonal flow, flood
frequency and magnitude

Downstream ecosystem• Reduced ecosystem health(Biodiversity, Quality and quantityof food resources, Water quality)

Walling and Webb, 1983

Change of flow and sediment transport regimes

River channel is formed by natural fluvial process caused by
combination of ‘water flow and sediment transport’(Leopold 1957)

River continuum
concept
Vannote et al.
1980

Natural flow regime
Poff et al. 1997

Flood pulse concept
Junk et al. 1989

Fluvial river systems composedof 6 segments (Takemon 2010)

River channel as conveyor belt for
sediment (Kondolf 1994)

Reservoir sedimentation

Source: S. A Kantoush,T. Sumi and Y.Takemon
MAY 2011,
INTERNATIONAL WATER
POWER & DAM
CONSTRUCTION

Shift of stage-storage andstage-area curves due to sedimentation

Brune curve (1953)
Sediment trapping efficiency of reservoirs

Source: Reservoir Sedimentation handbook, Morris and Fan(1996)

Reservoir Yield change by reservoir sedimentation

Source: Reservoir
Sedimentation
handbook, Morrisand Fan(1996)

Source: Reservoir Sedimentation handbook, Morris and Fan(1996)

Global net reservoir storage by accounting
for the effects of storage loss due toreservoir sedimentation

Quenching the Thirst: Sustainable Water Supply and Climate Change, George
Annandale(2013)

General shape of a reservoir storage-yield curve, in which yield asymptoticallyapproaches the mean 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 sediment accumulation in the conservation pool may be expressed aseither a reduction in yield at a given level of reliability (point 1 to 2) or a reduction inreliability at a fixed yield (1 to 3).

Illustration of prolongation of reservoir lifetime

Sediment
impacts
assessment

Namingof priority
dam

Conventional design life approach andLife cycle management approach

Implementation
countermeasures

Actions

Sedimentation
process
assessment

Economicassessment

Screening of
Sediment
management

Monitoringof control
measures

Long‐term
Sustainabilityof reservoir

Criteria

Cost andbenefits
Feasibility of
sediment
strategy

SafetyMaximizing
sediment
releaseEnvironmental
and social
Biodiversity
Water
quality

Food
supplyEnvironmental
compatibility
Habitatdiversity

Source: T.Sumi and S.A. Kantoush
ICOLD Annual Meeting, Luzern 2011

Water supply reliability based on (a) the design life
approach and (b) the life cycle management approach

Quenching the Thirst:
Sustainable Water Supply
and Climate Change,
George Annandale(2013)

Egyptian case study
Sediment Yield
Reservoir Sedimentation
Reservoir Sustainability

Quenching the Thirst: Sustainable
Water Supply and Climate Change,
George Annandale(2013)

Nile Basin Rivers Environments
 Length is 6,825 km;
 Sources 3 main tributaries, White
Nile, Blue Nile and Atbara River; Area of almost 3 Mio km2
 Ten different geographical,
topographical and climate regions; Over 180 million people live; Runoff from NB around 2 % of thetotal runoff from African continent;
 Spatial pattern of rainfall inputs
across the NB is highly variable; Water resources are minimal in arid,semi arid areas of Egypt, Sudan;
evaporation losses are high.

Nile Basin Hydrology

Main
River
100%

Main
River
100%

Dams on the Nile River Basin

Merowe dam

Sediment Yield and Suspended Sediment Budget

Edfina Barrage
AssuitBarrage
Aswan High Dam
Nag Hammad Barrage
Essna
Barrage
Kajbar Dam(planned)

Jebel Auliya

KHASHM EL GIRBA DAM (U.S. VIEW)

ShereikDam(planned)

Merowe Dam
Tekeze Dam
SENNAR DAM (D.S. VIEW)

Tekeze Dam

Discharge andSuspended Sediment
Concentration SSC forsummer floods at Wadi
Halfa and Gaafra

Own falls
Rosseriers dam
Owen Fall

19

20

Current State of Reservoir Sedimentations in the Nile Basin

Aswan High Dam Reservoir Sedimentation

Aswan High Dam Reservoir Sedimentation
No sediment control
measure;We should clarify what are
the impacts of the
upstream dams on inflow
water and sediment;
Significant volumes ofsediment may betransported to the dam
when large flood events
follow a sustained low flow
periods;
A modification to the waterand sediment budget of
AHD reservoir will follow
the completion of the new
dam at Merowe.

Kashm El Girba, Sudan
1964
CAP=1.3BCM
MAR=12BCM, MAS=95MCM

Source: C. Guilbaud et al.

500

December

OctoberNovember

September

July

0
-500

AugustOctober

DecemberNovember

September

July

August

June

April

0

May

100

1000

June

200

Downstream of AHD (1968-1977) After construction

1500

April

300

Downstream of AHD (1929-1955) Before construction

2000

May

400

2500

March

Natural Nile Flow (1912-1962)
Downstream of AHD (1912-1962) (before)
Downstream of AHD (1968-1977) (After)

500

3000

February

600

January

Sediment concentration [PPM]

3500

700

March

Storage loss of Roseires Reservoir (1966-1995)
From 1966 to 1976 drop in the capacity was 550 Mm3

800

February

Mean Monthly Discharges [x10 6 m 3/day]Trap efficiency of Lake Nasser/Nubia is close to 100 %, only particles<2 μm istransported;
Deposition started at the tail zone of the reservoir and advanced northward along theriver bed
Dead storage capacity is sufficient for 500 yrs and the rocky-narrow channel of thesouthern part of Lake Nubia work as a place for a new delta formation on the sides ofthe lake;
We should clarify what are the impacts of the upstream dams on inflow water andsediment;

January

Roseires dam passing flood waterin July 1966, (Siyam, 2005)

Initial reservoir storage capacity of 3000 Mm3
In the early eighties of the last century it was
raised to 3300 Mm3 (level to 481 instead of
480)
Recently the dam will be raised to 10 m
7.4Mm3
Sediment measure is dredging the depositedsilt ahead of the power intakes and sluicing

Draw down sediment flushing

Source: C. Guilbaud et al.

Reservoir water level changes

Source: C. Guilbaud et al.

Sedimentation and storage loss

Japanese case study
Sediment Yield
Reservoir Sedimentation
Reservoir Sustainability

Source: C. Guilbaud et al.

High sedimentation rates in Japan High precipitation
Typhoon, Front Annual average 1,700 mm
Hourly up to 50- 100 mm, Daily up to 200 - 500 mm
 Geological condition
Two large faults
Median Tectonic Line, Itoigawa-Shizuoka Tectonic Line Geographical condition
2000-3000m high mountains, Steep valleys
 Earthquake Volcano activity

Catastrofic Flood Disasters
Tropical typhoon No.12, 2011

Sep.5
Sep.4

Sep.3
Sep.2
Sep.1
Aug.31
Aug.30Rainfall (mm)Aug.30-Sep.6

Upstream regions

Yasuoka dam(1936,11MCM)

Miwa dam(1959,30MCM)

Hiraoka dam(1951,43MCM)

Koshibu dam(1969,58MCM)
Sakuma dam(1956,327MCM)

Akiba dam(1958,35MCM)
Miwa damDebris flow

Land slide dam

Sakuma dam

Tenryu River, A=5,090km2

Source: MLIT

Reservoir sedimentation in Sakuma dam

Annual sedimentation records in Sakuma dam
Sedimentation（1,000ｍ3 ）Annual sedimentation(1,000ｍ3)

Sakuma Dam

Planned sedimentation capacity
Annual sedimentation
Actual sedimentation
Planned sedimentation

Average annual sedimentation

13,00012,00011,00010,000
9,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
-1,000

1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006

130,000
120,000
110,000
100,000
90,00080,00070,00060,00050,00040,00030,00020,00010,0000-10,000

J-Power (EPDC)
1956 Power generation
Gravity concrete Height=155.5 m

History of guidelines on reservoir
sedimentation design
 1958, Technical Criteria for River Works, Ministry of Construction Necessary volume for reservoir sedimentation is 100 years 1966, Reservoir sedimentation database guideline, MOC
 To check riverbed aggradation in order to prevent flooding risks
upstream of reservoirs caused by sedimentation

 1982, National reservoir sedimentation database, MOC
 All reservoirs over 1 MCM should report accumulated
sedimentation volume of 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.
Target dams :971 dams of 18.2 BCM capacity

High rates up to 4.3 million m3/yr hadYaers
been recorded for 1957-1969.
After that, it has been gradually decreased because of slope protection
works in the upstream catchments, construction of another new dams,slightly reduction of extreme flood events caused by large typhoons.

Target dams

Reservoir Sedimentationin JapanReservoir sedimentation database
Region215 関東479 中部
217 北海道
216 中部
307 中部
6 北海道
305 九州483 北陸488 北海道309 東北813 中部
312 北陸152 北海道
487 北海道
489 北陸314 北陸748 四国816 中部
154 中部
317 北陸815 東北11 北陸
494 中部
316 北陸498 中部
495 北陸817 中部
502 中部

Total
971 dams

Catchment

Dam

River

Dam Owner

雨畑
平岡
岩知志
泰阜
大井
清水沢
岩屋戸
小原
岩松
上郷
笹間川
神一
川端
芦別
祖山
出し平
小見野々
丸山
美和
奥裾花
夏瀬
神二
畑薙第一
鹿瀬
松川
成出
佐久間
秋葉

富士川
天竜川
沙流川
天竜川
木曽川
石狩川
耳川
庄川
十勝川
最上川
大井川
神通川
石狩川
石狩川
庄川
黒部川
那賀川
木曽川
天竜川
信濃川
雄物川
神通川
大井川
阿賀野川
天竜川
庄川
天竜川
天竜川

日本軽金属㈱中部電力㈱北海道電力㈱中部電力㈱関西電力㈱北海道企業局
九州電力（株）関西電力㈱北海道電力㈱
東北電力㈱
中部電力㈱
北陸電力㈱
国土交通省北海道
北海道電力㈱関西電力㈱関西電力㈱四国電力（株）
国土交通省中部地
国土交通省中部地
長野県土木部
東北電力㈱
北陸電力㈱
中部電力㈱
東北電力㈱
長野県土木部
関西電力㈱
電源開発㈱
電源開発㈱

（ｋｍ２）
99.7
3,650.0567.0
2,980.0
2,083.0
506.0
354.4
814.5
605.0
1,810.0
68.0
1,960.0780.0
2,172.5
929.4
461.2
266.8
2,409.0
311.1
65.0
556.0
2,060.0318.0
6,264.060
723.0
3,827.0
4,490.0

Year
42
55
48
71
83
67
64
63
64
45
46
53
43
53
76
21
38
53
48
26
53
53
44
78
31
54年
51
49

Total Capacity 2006 Sed. Volume Sed. Rate
（1000ｍ３）
13650
42425
5,040.010761
29400
5,576.0
8,309.011741
9,026.0
7,660.0
6,340.011346
6,479.0
6,250.033850
9,010.4
16750
79520
29952
5,400.09,350.011265
10740016525
7400.09,709.032684834703（1000ｍ３）
11659
35457
4,204.08146
22030
3650.05355.07549
5487.04636.03687.06559
3464.03176.016492
4352.8
8051
36669
13656
2410.04131.0
4382
41542
6192
2766.03626.2118628
11918

（％）
85.4%
83.6%
83%
75.7%
74.9%
65.46%
64.45%
64.3%
60.79%
60.52%
58%
57.8%
53.47%
50.82%
48.7%
48.31%
48.1%
46.1%
45.6%
44.63%
44%
38.9%
38.7%
37.5%
37.38%
37.35%
36.3%
34.3%

Reservoir Sedimentation in Major River Basins

35.0%
32%
30.0%

Sedimentation Rate(%）

No.

Hokkaido

25.0%
20%

20.0%

Chugoku
Kanto

Multipurpose
dams(MLIT)

14%

15.0%

Chubu
Kinki
Shikoku
Kyushu13%

12%

Others
9%

10.0%

9%

6%
5.0%

3%

5%

5%
3%

8%
6%

TohokuHokuriku

Hydropower
dams

7%

4%

8%
6%
4% 4%

6%
4% 4%
3%
2%
0%

0.0%
HokkaidoTohoku Kanto Hokuriku Chubu

11%
8%
5%

4%

6%

1% 1%

Kinki ChugokuShikoku Kyushu Okinawa Japan

Japan

Storage loss by sedimentation in multipurpose dams
(CAP: Reservoir Total Storage Capacity, MAS: Mean Annual Sediment Inflow)

40%
35%

Sedimentation Rate

30%
25%
20%
15%
10%
5%

TedoriShingu

Tone

Omono

Agano

Ishikari

Yodo

ChikugoMogamiTakahashi

Tokachi

YoshinoSagami

Kitakami

Sho

Shinano

Oyodo

Kiso

JintsuYahagiKurobeOhi

Fuji

Tenryu0%• InReservoir
Japan, 2730 dams (>15m high) with 23 BCM capacity.
• sedimentation
National reservoir sedimentation database
Sedimentationin Japan progress of all reservoirs over 1 MCM have beenreported annually to the central government from 1980s.

In 971 dams of 18.2 BCM volume,→
total sedimentation 7.7%
annual loss 0.24%/yr
Itoigawa-Shizuoka
Tectonic Line

Sediment
yield potential
map of JapanMedian Tectonic Line

単位は㎥/㎢/年
3/km2/yrm

Sedimentation progress
at Koshibu dam

Environmental changes in downstream rivers
 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

Tenryu River
Mouth

Sediment
suppy

Yasuoka dam (1936)
Hiraoka dam (1951)

1946

Sakuma dam (1956)
Akiba dam (1958)
Miwa dam (1959)

1961
Decrease

Koshibu dam (1969)

2001

Nakatajima Sand Dune and Sea Turtle (Sep.14, 2008)

Comprehensive Sediment Managementin Sediment Routing System
Balancing of sedimenttransport from thesource of the river to thecoast
Lack ofsediment supply

Coastal erosion

Overall sedimentdeposited in
Sakuma dam

Grain size in Tenryu river
天竜川流砂系（佐久間ダム、河道、海岸）
粒度分布

100

Percentage
in weight (%)
加積百分率（％）

Three grain size groupsof deposited sediment in
Sakuma dam andrelationship to coastal
erosion60

Sediment inriver channel

40

20

Sediment incoastal area

0
0.0

0.0

0.1

50%=0.8MCM/yr①①

1.55MCM/yr

Passing
through
河道には堆積せず通過
河道

Bed浮遊砂＋掃流砂
load+Suspended load

Washload+Suspended load

ウォッシュロード＋浮遊砂

Bottom set bed（底部堆積層）Fore set bed（前部堆積層）Top set bed（頂部堆積層）掃流砂
Bed load
浮遊砂
Suspendedload（ウォッシュ
Wash ロード）
load

10.0

40%=0.6MCM/yr
②
②

100.0

1000.05%=0.1MCM/yr③③

Sakuma佐久間ダム
dam

Sakuma dam
Dam
body
ダム堤体

1.0
粒径（㎜）
Grain size(mm)River channel

Almost sediment will
主材料である礫を埋める
pass through except
程度で大部分は河道には
some will deposit
among堆積せず通過
gravels

Deposit and form
river morphology

河道に堆積して河床を形成

H.W.L

River mouth
河口

Delta
堆砂の肩
（ウォッシュロード）Washload

L.W.L

Downstream
下流部
area

Sediment property

Middle
area
中流部

堆
砂
性
状

Delta
デルタ

Upstream
上流部area

土質区分
Size

粘土・シルト主体
Clay, silt

砂主体
Mainly
sand

礫・砂主体
Sand
and gravel

平均的な
Grain
size
粒度分布
content(%)（単位：%）

Gravel=0,
礫=0、砂=10、Sand=10,
粘土＝50、
Clay=50,
Silt=40
シルト＝40

Gravel=10,
礫=10、砂＝45、Sand=45,
粘土＝30、
Clay=30,
Silt=15
シルト＝15

Gravel=30,
礫=30、砂＝40、Sand=40,
粘土＝20、
Clay=20,
Silt=10
シルト＝10細粒分Fc
Fine
sediment

Fc=90%以上
Fc=over90%

Fc=45～50%程度
Fc=45-50%

Fc=30%以下
Fc=lower30%

自然含水比ｗ
Water content

w=100%以上
w=over100%

w=50～60%程度
w=50-60%

w=40%以下
w=lower40%
大Large小Small

密度・間隙比Density,
Porosity
強熱減量IgIgnition
loss
有機物・栄養塩
Nutrients

Ig=10%程度
Ig=over10%←→
Ig=8%程度
Ig=ca.8%

Large
大

←→

Ig=4%程度
Ig=ca.4%

Small
小海岸での沖合に流出
Coast
海岸 Discharge to sea

Depositand form
海岸では砂浜を形成
beach at the coast

Check dam
Storagereservoir

RiverbeddegradationRiver
environmentchangeSediment flow
monitoring
Bed load
Suspended load
Wash load

Cascade system in the Tenryu river

佐久間ダム堆積土砂
河道(S31)3～7k
河道(S31)9～13k
河道(S31)19～25k
海岸T.P.0m
海岸T.P.-5m
海岸T.P.-10m

80

Sedimentation

Deposit at river
mouth

海岸では河口部に堆積

Comprehensive
sediment
management in
the Tenryu River

How to select suitable sedimentmanagement options?
Reservoir size, Watershed size,
Water availability, Sediment Yield

Reservoir Size, Sediment Management Options
Dam Name

AREA
Catchment Area
(km2)

CAP
(BCM)

MAR
(BCM)

MAR/
AREA

MAS
(BCM)

MAS/
AREA

Reservoir
Gross
Capacity

Mean
AnnualRunoff(mm)

Mean
AnnualSediment
Inflow(mm)

High Aswan,
2,849,000 168
Egypt-Sudan,
Kashm El
100,000
1.3
Girba, Sudan
Sanmenxia,
688,400 16.2
China
Three
Gorges, 1,000,000 39.3
China
Kurobe,
184.5
0.199
Japan
Unazuki,
461.2 0.0247
Japan

55.5

0.12

12

0.095

43.1

1.231

424

0.18

0.525

0.000739

0.641

0.00062

CAP/ CAP/MAR MAS

10000

1000

100

Reservoir Sedimentation
Management Options

10

1
0.01

0.1

1

10

Reservoir sediment management measures in Japan
Sediment check dam
Diversion weir

Afforestation

Excavating

Sediment bypass tunnel

Trucking
Dredging
Reducing
Sediment Inflow

Density current
venting
Sediment scoring gate
Sediment supply(augmentation)

Sediment Routing
Sediment Removal

Check dam in the upstream river (Sabo Dam)
(Tenryu River, Japan)

Check Dam just upstream of reservoirs (Chosha Dam)
(Koshibu and Miwa dam, Japan)
Sediment Plant

Koshibu dam

Miwa dam

Koshibudam

Source: Reservoir Sedimentation
handbook, Morris and Fan(1996)

Sediment Routing in Reservoirs

Sediment Bypassing

Sediment Bypass Dam in the Japanand Switzerland
No

Name of Dam

Country

TunnelCompl
etion

TunnelShapeTunnelCross
Section(B×H(m))TunnelLength(m)

GeneralSlope(%)

DesignDischarge(m 3/s)DesignVelocity(m/s)

1

Nunobiki

Japan

1908

Hood

2.9×2.9

258

1.3

39

-

-

2

Asahi

Japan

1998

Hood

3.8×3.8

2,350

2.9

140

11.4

13 times/yr

Operation
Frequency

3

Miwa

Japan

2004

Horseshoe

2r = 7.8

4,300

1

300

10.8

2 times/yr

4

Matsukawa

Japan

Under
constr.

Hood

5.2×5.2

1,417

4

200

15

-

5

Koshibu

Japan

Under
constr.

Horseshoe

2r = 7.9

3.982

2

370

9

6

Egshi

Switzerland

1976

Circularr = 2.8

360

2.6

74

9

10days/yr

7

Palagnedra

Switzerland

1974

Horseshoe

2r = 6.2

1,800

2

110

9

2～5days/yr

8

3

-

Pfaffensprung

Switzerland

1922

Horseshoe

A= 21.0m2

280

220

10～15

～200days/yr

9RempenSwitzerland

1983

Horseshoe

3.5×3.3

450

4

80

～14

1～5 days/yr

10

Runcahez

Switzerland

1961

Horseshoe

3.8×4.5

572

1.4

110

9

4 days/yr

New Projects at Koshibu dam (Japan), Solis dam (Switzerland)

Miwa dam bypass system
NaganoTenryuriver
NagoyaSedimentation record in Miwa dam
Sedimentation+Excavation

Tokyo
Dam

Sedimentation

Flood control and sediment bypass scheme

Background and Objectives
 Sediment bypass tunnel is an effective measure to stop or reduce
sedimentation.
 Miwa dam sediment bypass tunnel is the first experience formultipurpose dams in Japan.
 Multiobjective operation is needed including flood control
 Divert mainly suspended fine sediment.

 It is important to study the following points to improve the bypass
performance:(1) How to trap coarse sediment in the upstream reservoir area(2) How to effectively divert the suspended sediment and high turbid
flow through the tunnel(3) How to minimize the downstream environmental impacts(2) How to trap coarse sediment in the upstream
reaches of reservoir by check dam
No.22

No.23

Bypass operation in 2007

No.25
No.26

Bypass discharge

No.24

Bypass outlet
No.27

Bypassefficiency

How to effectively
divert suspended
sediment and highturbid flow throughthe tunnel
Turbid flow monitoring• 2D surface velocity
measurement by LargeScale Particle Image
Velocimetry (LSPIV)• Suspended Sediment
Concentration (SSC)
measurement by
Turbidity(INFINITYTurbiditymeter (JFEAdvantech Company))

Diversion weir

Check damMode 1

How to utilize
upstream hydrological
data to optimize
bypass operation

Diversion Weir in Reservoir

Large-Scale Particle Image
Velocimetry (LSPIV)

Kurokawa
Mode 2

Miwa Dam
Bypass Rainfall and turbidity
are monitored by realPrediction oftime basis to predict Discharge andinflow discharge andSediment
concentration
sediment
concentrations

Mode 2

Takaiwa
Sugishima

Mibu River Effective bypass
operation by using
sediment concentration
predictions

Max. 0.5m/s

Three modes of Bypass operation

Inside of Bypass

Flow field to the Bypass iscreated after Mode 2

JFE-Alec
INFINITY-Turbi

How to minimize the downstream
environmental impacts

Max. 2m/s

Source: T.Sumi and S.A. Kantoush
IAHR, Brisbane 2011

Sediment bypass system at Asahi Dam

１

Bypass tunnel

Weir

Gradient ：1/34
Height ：3.8m
Width ：3.8m

Height ：13.5 m
Crest Length
：45 m

C
２
３
Length : 2,350m
Max. discharge：140m3/s

Outlet４

Mibu weir

Reduction of Sediment Volume in Reservoir

Rainfall30
40

Amount of accumulative rainfall
in 5:00 on Sept 4th : 1,507 mm

5060

W ater level

465

Punping up

Water Level

455
445
435

Generating

425

Discharge

Inflow
Discharge
Discharge with the bypass system

Spillway gate open

Inflow
Outflow
BypassingMaximum inflow（3th 10:13 pm）
Q=954.04m3/s
Maximum discharge（3th 10:30 pm）
Q=527.79m3/s

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

Spillway gate open

Actual sedimentation volume(×10 3m3)

10

Actual sedimentation volume

3500
3000

Accumulated sedimentation volume (Estimated value)
Accumulated sedimentation volume (Measured value)

500

2500

400

2000

300

1500
(216)

200
100
0

1000
(83) (70)

(83)

(78)

500
(53)
(46)
(40)
(28)
(14) (8)(15)(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

Accumulated sedimentation volume(×103m3)

600

20

(440)

Bypassed sediment

9/5 23:00

9/5 19:00

9/5 21:00

9/5 17:00

700
9/5 15:00

9/5 11:00

9/5 13:00

9/5 9:00

9/5 7:00

9/5 3:00

9/5 5:00

9/5 1:00

9/4 21:00

9/4 23:00

9/4 19:00

9/4 17:00

9/4 13:00

9/4 15:00

9/4 11:00

9/4 7:00

9/4 9:00

9/4 5:00

9/4 3:00

9/3 23:00

9/4 1:00

9/3 21:00

9/3 19:00

9/3 15:00

9/3 17:00

9/3 13:00

9/3 9:00

9/3 11:00

9/3 7:00

9/3 5:00

9/3 1:00

9/3 3:00

9/2 23:00

9/2 21:00

9/2 17:00

9/2 19:00

9/2 15:00

9/2 11:00

9/2 13:00

9/2 9:00

9/2 7:00

9/2 3:00

9/2 5:00

9/2 1:00

9/1 23:00

9/1 19:00

9/1 21:00

9/1 17:00

9/1 13:00

9/1 15:00

9/1 11:00

9/1 9:00

9/1 5:00

9/1 7:00

9/1 3:00

8/31 23:00

9/1 1:00

0

8/31 21:00

Amount of rainfall per hour

Operational Results of Asahi Dam
and Bypass System during the Flood

-100
-500
•Annualsediment volume and bypassed sediment in 2011 werethe largest volume since the start of the bypass operation.

•Without bypass system, about 74% of sediment volume would
have been accumulated in the reservoir. This sediment have
now positive effects on downstream river environment.

Longitudinal Profile of Riverbed
in Reservoir

Sediment bypass
Koshibu dam

500
480

Se Bridge
ＮＷＬ．４６２．００ｍ

Ｌｅｖｅｌ

460

Intake of the bypass
system440
ＬＷＬ．４３０．００ｍ

R=3.95m, A=49m2

Original riverbed in 1978

Outlet420

Riverbed in 2011
Riverbed in 2010

400

Riverbed in 1998

380
0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

Ｄｉｓｔａｎｃｅ ｆｒｏｍ Ａｓａｈｉ Ｄａｍ

・Since the bypass operation started in 1998, it is likely that the riverbed shapes inside
the reservoir have been relatively stable, though the riverbed of 2011 rises higher
than before in this flood.

L=3,982m, S=2%, Q=370m3/s

Sluicing

Sluicing
andDensity Current Venting
Density Current
Venting

Quenching the Thirst:
Sustainable Water Supply andClimate Change, George
Annandale(2013)

Sediment flushing dams in the World

Sediment Flushing

Name of Dam

Country

Dam
Dam
Heightcompleted(m)

InitialMean Annual １／(Mean Reservoi Average
Storage
Flushing Flushing
Sediment
Flushingr LifeAnnualCapacity
Duration Frequency
Inflow (MAS)(=CAP/ Discharge
Runoff)(CAP)(1/ yr)（hrs）(m3/s）(million m3)１） （=CAP/MAR) MAS)(million m3)

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
China
1966
65
13.3
Hengshan２）
Santo Domingo Venezuela
1974
47
3
Taiwan1938
30
7
Jen-shan-pei２）
Guanting
China
1953
43
2270
Guernsey
USA
1927
28.6
91
Heisonglin
China
1959
30
8.6
Ichari
India
1975
36.8
11.6
1961
35
56
Ouchi-Kurgan２） Former USSR
China
1960
45
9640
Sanmenxia２）
Iran
1962
82
1760
Sefid-Rud２）
Shuicaozi
China
1958
28
9.6
１）Average after dam completion， ２）Sluicing dams
Dashidaira

Japan

1985

Unazuki

Japan

2001

Gebidem

Switzerland

1968

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

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
0.0186

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

200
300
15
600
90
80
600
90
6
2
5
12.2
80
125
0.8
2.16
1000
2000
100
50

12
12
70
30
20
12
36
40
168
672
72
1272
120
120
72
24
2400
2900
2900
36

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.

Kurobe River Basin

Coordinated sediment flushingin the Kurobe river

Catchment area= 682 km2,
River length= 85 km①
m
1,600-

340

H=185m199MCM (1961)

Kurobe dam
1,448.000

Altitute

320

250

220
210

④ EL.235m⑤

Unazuki dam
245.000

-

-

-

-

-

-

Aimoto
10

20

30

40

50

60 km

The distance from the sea.

Unazuki Dam①
EL.232

②

24hr

③

③

①

② ③ ④
17hr

Dashidaira dam
343.000

Shimokurobe
0

24hr

12hr
44hr

44hr

400-

H=97m, 24.7 MCM (2001)

Unazuki② 230

Koyadaira dam
529.400

5hr

12hr

29hr
29hr
53hr
53hr

240

800-

200-

T≥12hrs

12hr

Sen-nin dam
851.000

600-

EL.327③300

H=76.7m,
9 MCM(1985)

1,000-

Dashidaira

330

310

1,400-

1,200-

②

15hr

⑤

④

⑤

68h
68hr

Coordinated water and sediment management isvery much important. Rainy season, June-July, andnatural flood timing is suitable for flushing.

Subjects of sedimentflushing

横断面模式図Cross
section Flushing efficiency

側岸侵食
Side
bank
erosionDegradation
河床低下=scoured sediment volume / water use

 Flushing effectDashidaira Dam= scoured sediment volume / total deposited
sediment volume before flushing Environmental impacts～ the influences of SS rising and DO dropping SS: Suspended solid concentration
duration time etc.
DO: Dissolved oxygen

Study on sediment discharge process during flushing
operations from quantity and quality point of view isvery important.

W f = KQf0.5 =6.4Qf0.5

S. A. Kantoush , T. Sumi, T.Suzuki and M.Murasaki：Impacts of sediment flushing onchannel evolution and morphological processes: Case study of the Kurobe River,Japan, River Flow 2010, Braunschweig, CD-ROM 2010.

Dashidaira dam: 9.0MCM
Sedimentation：4.0MCM
Flushing：12times, 5.6 MCM

99

96

01
98

97

Fe=0.01
04
03

02

100
1.0

10.0

100.0
6

3

Total Water Volume（W）（10 m ）3

3⑩ 60×103 m3

5000② 80×103 m3
4000⑨ 590×103 m3

3000
2000

⑦ 340×103 m3③ 20×103 m3

1000⑥ 460×103 m3⑪ 90×103 m3① 460×103 m3

'03

'02

'01

‘04

'00

'99

'98

'97

'96

'94

'95.11

'93

'95.7

'95.6

'92

'91

'90

'89

'88

0

KurobeSenninn
Dashidaira dam sediment flushed

100,0001000⑧ 700×103 m3（3450×10 m ）

6000

'87

Fe=0.02⑫ 280×103 m3

The biggest flood in 1995

'86

Fe=0.0595④ 1720×103 m3⑤ 800×103 m3

7000

'85

Fe=0.1

Cumulative sedimet volume（103 m3 ）Total Flushed Sediment Volume（S）（103m3）

10000
Dashidaira
VerboisGebidemBaira

Gross storage: 9×106m3

8000

3
3

Sedimentation volume
changes in Kurobe river

Sedimentation Amount （×10 m ）9000

Total water use and flushed sediment volume insediment flushing dams

KoyadairaDashidaira
Unazuki dam sedimentation volume

10,0001,000

100

Fe: Flushing efficiency =Total flushed sediment volume/ Total water volume

19251930193519401945195019551960196519701975198019851990199520002005
Year

Efficiency of Sediment Flushing

Flushing efficiency of Sediment flushing dams

Application of Sediment Flushing from the view point of wateruse

Shui ca ozi
Sef i d-R ud
J en-s ha n-pei
Sa nto D om i ng o

CAP

MAS

F erreraB a renburg
B a i ra

CAP ：Storage capacity
MAR ：Mean annual runoff

CAP
MAR
CAP 

Fe   
MAR 

MAS ：Mean annual sediment inflow
Fe ：Flushing efficiency  Vs / V

Vs ：Sediment discharge volume(m3)3
V ：Water use(m )V erboi s
Gebi dem

CAP / MAR ：Capacity-inflow ratio

D a s hi da i ra

0

0.05

0.1

0.15

 ：Ratio of water use for sedimentor Retention time (yr)0.2

flushing to the mean annual
runoff ＝V / MAR

CAP / MAS ：Reservoir life (yr)Flushing efficiency Fe

Fe: Flushing efficiency =Total flushed sediment volume/ Total water volume

Application of Sediment Flushing from the view pointof water use

Application of Sediment Flushing from the view
point of water use (β) and flushing efficiency (Fe)100000100,000

(a)

β=0.1

Flushing
feasible

1
0.001

0.1
CAP/MAR1

10000

Success

Fe=0.02100

Flushing
feasible

1
0.001

0.01
0.1
CAP/MAR1

Fe: Flushing efficiency =Total flushed sediment volume/ Total water volume
β: Ratio of water use for sediment flushing to the mean annual runoff (MAR)Xinanjiang
FengmanFailureShang yoy jiang
Bailianhe

Flushing

1000

Meshian

Dongjiang

Sediment Flushing dams

1,000

10

0.01

(b)

CAP／MAS

1,000

10

β=0.2
β=0.1
β=0.05

10,000CAP/MAS

CAP/MAS

10,000100

Fe=0.02, β=0.1

100,000

Fe=0.01
Fe=0.02
Fe=0.05

α＝2.45×10 3

Aswan

Zhaxi
Shuikou100

10

HooverHuantankou
Kurobe Dahuofan
Wuqiangxi
BoulderHuanglangtan
SakumaGangnan Elephant Butte
Fuchunjiang
Mangla Danjianko
Genissiat
Fengjiashan
Guernsey Three Gorges Tarbela
Liujiaxia
Guanting
Baira
XimalinVerboisAmalaza
Sefid-Rud
Lago loiza
Santo Domingo
Unazuki Gebidem
Fenhe
Heisonglin
Honglingji
Bikou
Rock Creek
XinqiaoDashidaira Suicaozi
Shiman Hengshan Bazjazui
Gongzui
Sanmenxia
Gmund
Xiaolangai
Zhiyu
Ouchi-Kurgan
WarsakQingtongxia
Zhenziliang
Ichari
Yanguoxia
First Falls
Wellbedacht

1
0.001

0.01

0.1

CAP／MAR110

Environmental monitoring
during sediment flushing

Environmental issues relating sediment management
 Minimize negative environmental impacts
 Water quality changes caused by discharging accumulated
sediment containing organic matters and those impacts on theaquatic ecosystem

Sea

Water quality(DO, SS etc.)
Mud quality
Aquatic species
Cross sections

N

-250
-150
-50

- Estimate the influences of SS quick rising and DO quick dropping- Establish minimizing measures （ex. Sediment Discharge Rule）
Water quality(DO, SS etc.)
Mud quality
Aquatic species
Cross sections

Unazuki dam

Dashidaira dam Reservoir

Water quality(DO, SS etc.)
Mud quality
Cross sections

0
5
10
15

Dashidaira

330

Inflow discharge[m3/s]
Outflow discharge[m3/s]
Water level[EL.m]

500
400

325
320

300

315

200

310
305

0

300

Unazuki700
D isc ha rge (m 3 /s )W ate rle ve l(E L. m)

D is cha rg e( m 3 /s )

335

600

100

250
Inflow discharge[m3/s]
Outflow discharge[m3/s]
Water level[EL.m]

600
500

245
240

400

235

300

230

200

225

100

220

0

215

30,00025,000S S( mg/ l)②
Draw down③
Flushing（Free flow）
Suspended sediment (SS)100,000mg/l

15Dashidairadam, 2000

Water temparetureSS(mg/l)

10Disolved Oxygen

10

NH4+

6mg/l
①Opening ofscoring gate

②Reservoir
empty

Time
③Side bank erosion
or collapse

DO(mg/l)Actual sediment flushing and sluicing
operations at the Dashidaira Dam

Average hourly rainfall in Dashidaira basin [mm]20700

Wa terlev el( EL .m)

A v er ag e ho ur ly
r ai nf al l( mm )Coordinated sediment flushing operation in 2006①
50,000SS(mg/l）

River

℃

10 km

DO(mg/l）50

NH 4+(mg/l）

Shimokurobe

SS(Dashidaira dam)[mg/l]
SS(Unazuki dam)[mg/l]
SS(Shimokurobe)[mg/l]

20,00015,00010,0005,000
002006/7/1

3

6

9

12

15

18

21

0
7/2

3

6

9

12

15

18

21

0
7/3

Balancing of Flushing efficiency and environmental impacts

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
362
215
60
22,0004,500
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
27,0006,500
2006 Sluicing 1378
203
12,0002,500
2006 Sluicing 2685
264
27,0005,200
2006 Sluicing 3
529
196
7,400
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‘Ayu (鮎)’, (Sweet fish,香魚,銀魚）
Typical fish in Japanese rivers

Flushing efficiently= Little waterHigh sedimentconcentration

Water use

Flushing naturally= Much water

Low sedimentconcentration

Reservoir draw down
Enough flushing discharge water
Rinsing discharge after flushing,etc.

Ca. 20cm

Riverbed elevation change from 1980
Based on 1980

Grain size distribution change from 1980

Dashidaira dam
completion 1985

Start of Dashidaira
dam flushing 1991

Unazuki dam
completion 2000

D60(mm)

-0.50
-1.00

100

-1.50

2007

2005

2003

2001

1999

1997

1995

1993

1991

Ave. 3.2-6.2k
Ave. 11.4-13.2k
Ave. 15.8-20.4k
1989

1987

1981

1979

-2.50

1985

Ave. 0-3.0k
Ave. 6.4-11.2k
Ave. 13.4-15.6k
Ave. of all section-2.00

River channel bed is gradually recovering in the river mouth area.

Shoreline change of the Kurobe river mouth area
1947

River mouth

1998

0－3k
7-11k
14-15k
Ave of all section

1000

4-6k
12-13k
16-20kGrain size is gradually recovering to 1970s by changing from200-400mm to 50-100mm

Consensus-building
procedure
Drawing up of sediment flushing planand environmental survey plan (execution organizations)

1975

2005

Opinionreflected

Advance explanation given to fishing and farming groups
and government organizations / Correct opinions
Opinionreflected

1985

2010

Kurobe River Sediment Flushing Evaluation Committee(composed of experts in environment, biology, and fishery etc. )Kurobe River Sediment Management Council(composed of relevant government agencies)

Shoreline is gradually recovering after 2000.

Sediment flushing carried out by Ministry of Land, Infrastructure
and Transport and The Kansai Electric Power Co. Inc.

Sediment augmentation project in Japan

River Restoration
andReservoir Sediment management

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

10

0.00

1983

Variation from 1980 (m)Unazuki dam
completion 2000

Start of Dashidaira
dam flushing 1991

1981

Dashidaira dam
completion 1985

0.50

1977

1.00

1979

Average river bed level

Downstream geomorphological monitoring

Sediment augmentation in Nunome dam
Table 1. History of the sediment
replenishment tests downstream of
Nunome dam
Year

2004
2005
2006
2007
2008

2009

Settingsediment
period28-9-2004
9-8-2005
NA
9-8-200727-6-2008
7-8-2008
12-11-2008
NA
2-10-2009

Flood period

29-9-2004
4, 5-10-2005
19, 21-7-2006
23, 29-8-2007
8-7-2008
5, 19 -9-2008
NA
2-8-2009
7,8-10-2009

Volume ofVolume ofVolume ofplacederodedremainedsediment (m3) sediment (m3) sediment (m3)
0
190
190
0
540
80
460
0
370
90
720
810
0
100
35
0*
100
100
0
500
0
500
0
500
0
500
500

* The remained 65 m3 sediment is removed. NA: No placed sediment or No flood occurred

Placed sediment
geometry

Flood stage
Normal stage

Source: T.Sumi and S.A. Kantoush
ICOLD Annual Meeting, Luzern 2011

Source: T.Sumi and S.A. Kantoush
ICOLD Annual Meeting, Luzern 2011

Sediment grain sizes
for sediment augmentation

Annual Sediment Augmentation
Volume（m3）100000
1:10

10000

1000

100
10,000100,000

1,000,000

Nibutani
MiharuFutaseKawamata
Aimata
1:100
Kawaji
HachisuYahagi
Nagashima
Akiba
1:1000
Nagayasuguchi
Urayama
Hitokura
Shimokubo
Agigawa
NunomeSagami
10,000,000

Percentage Passing（％）

Sediment augmentation volumes
vs. Annual reservoir sedimentation
100

Nibutani

90

Miharu80

Futase70

Hachisu

60

Managawa

50

Nagashima

40
30

Urayama20

Shimokubo

10

Akiba

0
0.001

Annual Reservoir Sedimentation Volume（m3）Agigawa
0.01

0.1

1

10

100

NunomeGrain Size（mm）

Source: T.Sumi and S.A. Kantoush
ICOLD Annual Meeting, Luzern 2011

Source: T.Sumi and S.A. Kantoush
ICOLD Annual Meeting, Luzern 2011

鳴鹿大堰：34 ㎞

⑬

②

⑪

①

③
⑫
⑧

④
⑦

市荒川発電所：24 ㎞

Purpose：Improvement of riverenvironment
・ Increase of downstream river flow discharge
・ Improve downstream river landscape・ Improve habitat for aquatic species

Flexible dam operation

Managawa Dam
River instream
flow

⑥
⑤

0 5 10真名川⑨⑩

Completion
1979
H=127.5m
V= 115 MCM
Flood control
Hydropower,
etc.

九 頭竜川

Managawa dam

Kuzuryu River Basin

下荒井堰：14 ㎞

富田発電所

麻生島：10 ㎞

君が代橋：9 ㎞

八千代橋：6 ㎞

土砂還元地点

佐開橋

五条方下流：3 ㎞

真名川発電所：3 ㎞

プラント前：2 ㎞

雲川ダム・笹生川ダム

真名川ダム：0 ㎞

Flushing flow

Test results from 2000 to 2002
show that habitat for sweet fishes
has been improved by the increaseof instream flow.

Aug. 2003（30m3/s）
Nov. 2004（50m3/ｓ＋Sediment）
Aug.2005（30m3/s）,Dec.（50m3/ｓ＋Sediment）
Nov.2006（50m3/ｓ＋Sediment）

River instream flow
0.28m3/s → 0.67m3/s

Nov. 8, 2007
（50m3/s + Sediment + River channel )20km

Start (10m3/s)

Flushing flow at Managawa Dam
60
H16表面流速
Velocity ①
Velocity②
H15表面流速
Discharge
H16八千代橋流量

2.5
2.0

50
40

1.5

30

1.0

20

0.5

10

5.5km below dam

0.0

流 量（m3/s)
Discharge(m3/s)

Surfacevelocity (m/s)
表面流速(m/s)

3.0

Max 50m3/s

Replenished Sediment

0
9

10

11

12

時

13

間

14

15

16

17

Hour (Nov.15, 2004)
11.73m. 7 3 m 上 昇up

1.8

プ ラ ン ト 前 （ ダ ム 下 流 1.7km）

1.7km below dam

五3.5km条 方 下 （ ダ ム 下 流 3.6km）
八5.5km
千 代 橋 （ ダ ム 下 流 6.5km）

1.4
1.2

11.10m. 1 0 m 上 昇up

1.0

00.91m. 9 1 m 上 昇up

0.800.75m. 7 5 m 上 昇up

Side bank erosion

君9.3km
が 代 橋 （ ダ ム 下 流 9.3km）

End (15m3/s)

0.6
0.4
0.2
0.0
8

9

10

11

12

13

14

15

16

17

18

19

時刻
Hour (Nov.15,
2004)

Water level and velocity change during flushing flow

１９５５

Grain size
distribution (%)
通過質量百分率（％）

位 変 change
動 量 （ ｍ(m)Water水level

1.6

100
90
80
70
60
50
40
30
20
10
0
0.01

試料A:採取地

Replenished
試料B:採取地
sediment
試料C:設置土砂0.1

1
粒径（ｍｍ）

10

Diameter (mm)Sediment Augmentation in other countries
1979
Managawa Dam

２０００

2000-2012
River Restoration

２０１１
Keswick Dam (Downstream of
Shasta dam) Sacrament River
California, USA

Sediment augmentation methods
In-channel Bed Stockpile

High-flow Stockpile

Point bar Stockpile

High-flow direct injection

Rhine River, Germany

Dredging
andHSRS (Hydro Suction
Sediment Removal System)

100Dredging and HSRS (Hydro Suction
Sediment Removal System)

Sediment Transportation
by Special Ejector Pump
System(SEPS)
Schematic Side
View of Special
Ejector
System Image of the SEPS

System Image of the SEPS
Syphon dredging

Discharge

Quenching the Thirst:
Sustainable Water Supply andClimate Change, George
Dredge Pipeline
Annandale(2013)

Dam

Appropriate selection ofreservoir sedimentmanagement strategy

Appropriate selection of reservoir sedimentmanagement strategy (Japanese Case Study)
CAP: Original totalCapacity-inflow
storage capacity
volume,MAR:ratio andReservoirLife Mean
annual runoff, MAS: Mean annual sediment inflow

Reservoir Life=CAP/MAS

100000No measures (from inner factors)

10000

Check damFlushing

Miwa

SakumaScoring gate

Koshibu

Scoring pipe

Matsukawa

Bypassing

1000⑱

Unazuki
⑥

④

100
⑫ ⑤ ③

Dashidaira
10
0.001

Excavating

⑦
⑭
⑨

①
⑬

②
⑧

⑲

⑮
⑪
⑰

⑳

⑩

Dredging⑯ Sediment check dam,

Sediment replenishment

Sediment bypass, Sluicing
Sediment flushing, Sediment scoring gate

0.01
0.1
1
Capacity-inflow ratio=CAP/MAR

10

Reservoir sedimentation management approaches(Basson, 2009 and Sumi, 2003)

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
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
SP, E, D, PF
12 山口 Yamaguchi SG, 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, CD
17 松川 Matsukawa B, E, CD
18 旭
Asahi
B
19 出し平 Dashidaira F
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

Quenching the Thirst:
Sustainable Water Supply
and Climate Change,
George Annandale(2013)

Lecture 7: Interaction Between River and Coastal Ecosystem
Yasushi SUZUKI (Japan Weather Association)
1.

Climate change information database
GCM outputs such as CMIP3 are available via network access to PCMDI web site. Meteorologicalresearchers are familiar with the usage of the GCM data, but the most of researchers other thanmeteorology such as agriculture, civil engineering, etc., are not familiar with the GCM. There are somedifficulties to use GCM; 1) to download the enormous quantity of data, 2) to understand the netCDF data
format, 3) to understand the GCM methodology, parameters and grids.
In order to provide a quick access way to GCM, Climate Change Information Database for
Hydrological Analysis is developed. The purpose of the database is to bridge the users and meteorological
specialists. The resolution of the data is unified, and climate change amount or factors for eachmeteorological element are provided from the database (http://hes.dpri.kyoto-u.ac.jp/database/). All data inthe database are interpolated on the same 80km mesh. Available data are the present-future projections of
27 GCMs, 2 validation dataset, 16 meteorological elements (precipitation, temperature, etc.), 3 emission
scenarios (A1B, A2, B1), and validation results (correlation, RMSE).
2.

Development of coupled river and ocean model
It is necessary to evaluate the long term change of ecosystems from rivers to ocean, in order to keep

the sustainable water environment near the river mouse and coast. In order to research the mechanism ofrivers and ocean, integrated water environment coupled river and ocean model is developed.
The model consists of the distributed Hydrological River Basin Environment Assessment Model(Hydro-BEAM), RIAM ocean circulation model (RIAMOM), and the advective diffusion model for Ise
Bay. River outflow, water temperature and sediment transport are nested at the river mouse, and JCOPE2
re-analysis data are used as an open sea boundary condition. For the case study, model result of sedimenttransport on 27/Apr/2003 is compared with the satellite image by the Aqua MODIS sensor. The resultscorrespond well with the satellite data.
3.

Climate change impact on the habitat of sweetfish
Future water temperature change of Kiso river and Ise Bay are calculated by coupled river-ocean

model under the CMIP3 A1B scenario. Water temperature increase of Kiso river is about 3 degrees, and sea
surface temperature change of Ise Bay is about 2 degrees. Coastal areas are more strongly influenced.
By the analysis of observed data, river run-up time of sweetfish has negative correlation with watertemperature difference (sea–river). Calculated future water temperature difference is smaller than that ofpresent, which results in the early start of river run-up time of sweetfish.

IHP Training Course Lecture 7

Contents

 Introduction of Hydrologic Environment Systems
funded by JWA

Interaction between riverand coastal ecosystem Development of climate change information
database Development of a coupled river and ocean model

Yasushi Suzuki

 Impact of future changes in water temperature on
the habitat of sweetfish

Japan Weather Association (JWA)
Previous affiliation: Hydrologic Environment
Systems, DPRIIHP Training Course Lecture7, 10/Dec/2013

 Conclusions

1

Hydrologic Environment Systems (JWA)

IHP Training Course Lecture7, 10/Dec/2013

2

Hydrologic Environment Systems (JWA)

To clarify mechanisms of environment issues anddisasters associated with the hydrologic cycle.
To consider how society should adapt to climate change
and social changes.
Hydrologic
Environment
Systems (JWA)

Japan
Weather
Associationneeds

2009 Oct.-2013 Sep. outcomes

needs

Governmentoutcomes

Public

DPRI, Kyoto Univ. funds

 Developing downscaling methods of climate model
outputs, and climate change information database Developing applied use of hydro-meteorologicalinformation
 Analyzing climate change impacts and social
dynamics on the water environment
Outcomes:
Climate change information database, Advanced
Hydro-BEAM, Coupled river and ocean model, etc.

IHP Training Course Lecture7, 10/Dec/2013

3

4

Climate Change Information Database

Hydrologic Environment Systems (JWA)

ClimateIHP Training Course Lecture7, 10/Dec/2013

http://hes.dpri.kyoto-u.ac.jp/database/Global warming
Extreme event

Disasters
Flood, Drought, Soil Erosion

Reservoir operation

Map images and numerical
data are available

Integrated Hydrological
Environment Managementfrom Mountain to Coast

Flood control
Water resources management

Socioeconomy
Water demand
Quantity & Quality

EcologyMonthly and annual mean data
Ecosystems and Environment
IHP Training Course Lecture7, 10/Dec/2013

Coastal Environment
5

IHP Training Course Lecture7, 10/Dec/2013

6

Climate Change Information Database

Climate Change Information Database

Objectives:
To provide quick access way to persons, who are notfamiliar with GCMs.

 All data in the database are interpolated on the same
80km grid 27 GCM results of CMIP3 and AGCM20 (present andfuture projections) 3 emission scenarios (A1B, A2, B1)
 16 meteorological elements (precipitation, temperature,snowfall, wind, humidity, heat flux, etc.)
 2 validation dataset (AMeDAS observation and JRA-25
reanalysis)
 Validation results (correlation, RMSE).

Users:
River administration, Dam operation, Agriculture, Privatecompanies, Citizens, etc.
Merits:To avoid difficulties of enormous data download, handlingof netCDF format, difference in model resolution, etc.

IHP Training Course Lecture7, 10/Dec/2013

7

IHP Training Course Lecture7, 10/Dec/2013

Difference of Model Resolution and Land Sea Mask

8

Development of Climate Change Information Database

Pickup the essence of enormous GCM outputs
Arrangement of GCM outputs by unit “primary mesh”

Green area is defined as that fraction of land exceeds 50 %.

1. Average 4 grid data near from center of “primary mesh”
2. Validation GCM outputs by unit “primary mesh”
with observation value or Reanalysis data
3. Evaluate Climate Change ( ratio or difference ) from GCM outputs
model gridcenter of“primary mesh”

Validation of
Reproducibility
Observed or
Reanalysis

GCM:
GCM:
Present Climate Future Climate
Climate Change Value

Fine

Resolution

CoarseComparison and model ensemble are enabledwith Database arranging by unit “primary mesh”

JRA‐25 and 7 models from CMIP3
IHP Training Course Lecture7, 10/Dec/2013

9

IHP Training Course Lecture7, 10/Dec/2013

10

CMIP3 Reproducibility over Japanese Land

Variability of GCM and SRES scenario
300

100

300

降水量 mmPrecipitationmm

対流性降水量Convectivemm
Precipitationmm

降雪水量 mm
Snowfall
mm
80

200

200

100

100

0
30 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

030 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

60
40
20

地上気温
℃
Air Temperature

C

20

20

地表面温度 ℃
SurfaceTemperature C

0
6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

風速 m/sWind
Speed m/s

5
4

IPCC Fourth Assessment Report: Climate Change 2007
10

10

0

0

3

↑ Large Variability in GCM

2
1
‐10
‐10
150 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 150 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

LatentHeat Flux
潜熱フラックスW/m2 W/m2← Large Variability in SRES

2
顕熱フラックス
Sensible
HeatW/m
Flux
W/m2

100
100
50
50
0

Uncertainty is Unavoidable
0‐50
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

IHP Training Course Lecture7, 10/Dec/2013
IPCC Fourth Assessment Report: Climate Change 2007

11

20C3m: 1971‐2000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

IHP Training Course Lecture7, 10/Dec/2013

0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

INGV‐SXGMIROC3_2h
CCSM3UKMO‐HadGEM1
降水量 mmCSIRO‐Mk3_0
CSIRO‐Mk3_5
ECHAM5_MPI‐OM
GFDL‐CM2_0
GFDL‐CM2_1
BCCR‐BCM2_0
CGCM3_1_T63
CNRM‐CM3
MIROC3_2m
MRI‐CGCM2_3_2
PCM
FGOALS‐g1_0
UKMO‐HadCM3
IPSL‐CM4
GISS‐AOMCGCM3_1_T47
ECHO‐G
GISS‐EH
GISS‐ERINM‐CM3_0
Jan JRA‐25
Feb Mar Apr May Jun Jul Aug
Sep Oct Nov Dec
Observed
気象官署

Michihiro et al. (2011)12

CMIP3 Reproducibility over Japanese Land

Multi‐GCM Ensemble

50

100

0
0
0
500Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1030 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Sea
LevelhPaPressure hPa
海面気圧

300
1015

200

200

100

100

5

0

0

IHP Training Course Lecture7, 10/Dec/2013

100

Sensible Heat Flux W/m2

50

100

25

50

0

0

‐25

上向き長波放射量W/m22↑ Longwave
Flux W/m

22
下向き長波放射量W/m↓ Longwave
Flux W/m
600

600

1030

400

400

1020

200

200

1010

0

0

1000

0

Screening by using RMSE and correlation

200
150

Ensemble Mean: Robust prediction of Climate Change
Variability: Uncertainty of GCM

100
50
0
Jan Feb Mar Apr May Jun

13

Jul

Aug Sep Oct Nov Dec

IHP Training Course Lecture7, 10/Dec/2013

AMEDAS(1971‐2000)

JRA‐25(1981‐2000)

JRA‐25(1981‐2000)

observed

JRA‐25 Reanalysis
underestimates

Both JRA‐25 Reanalysis
and MRI‐AGCM3.1S
underestimate in somelocal areas

Super High Resolution
MRI‐AGCM3.1S(20km)
is good
MRI‐AGCM3.1S(1979‐2003)

CMIP3 ave.
(1981‐2000)

CMIP3 is not good withsingle GCM, but could bebetter with multi GCM
ensemble

Any GCM are notcomparable to observedvalueComparison of Reproducibility: annual precipitation

Comparison of Reproducibility: monthly precipitation in September

IHP Training Course Lecture7, 10/Dec/2013

15

IHP Training Course Lecture7, 10/Dec/2013

Advantage of Multi GCM Ensemble

Climate Change of Future Climate

Comparison with AMEDAS (observed; 1971‐2000)
Screening GCM on condition that correlation coefficient is higher than 0.6

4.0
3.0
2.0

Kochi

CMIP3 ave. shows similar results compared to AGCM3.1S.

Michihiro et al. (2011)

Michihiro et al. (2011)17

IHP Training Course Lecture7, 10/Dec/2013

18

Kagoshima

Fukuoka

Osaka

Hiroshima

Tokyo

NagoyaCMIP3_min
MRI‐AGCM3.1S

Comparison of Climate Change of annual value in Japanese typical cities

AGCM3.1S gives good reproducibility.
Reproducibility of CMIP3 ave. is comparable to AGCM3.1S.
IHP Training Course Lecture7, 10/Dec/2013

Nigata0.0

Kanazawa

CMIP3_max
CMIP3_ave

1.0

Sendai

Kochi

Kagoshima

CMIP3_min
MRI‐AGCM3.1S

Osaka

NagoyaTokyo

NigataSendai

Kushiro0.6

Kanazawa

CMIP3_max
CMIP3_ave

Fukuoka

0.8

5.0

Kushiro1.0

6.0

Sapporo1.2

Temperature Change : + β [K]1.4

Sapporo

Kochi

Comparison of Reproducibility in Japanese typical cities

Kagoshima

Osaka

Fukuoka

Hiroshima

Tokyo

NagoyaNigata‐10.0

Precipitation Change : ×α

‐5.0

Kanazawa

Kochi

Kagoshima

Osaka

Fukuoka

Hiroshima

Tokyo

NagoyaNigataKanazawa

Sendai0.0

CMIP3_min
MRI‐AGCM3.1S

0.0

Sendai0.5

P: 1981‐2000 F: 2081‐2100
Screening GCM on condition that correlation coefficient is higher than 0.6

CMIP3_max
CMIP3_ave

5.0

Kushiro1.0

10.0

SapporoCMIP3_min
MRI‐AGCM3.1S

Temperature BIAS [K]

CMIP3_max
CMIP3_ave

1.5

16

Advantage of Multi GCM Ensemble

Reproducibility of Present Climate

2.0

CMIP3 ave. is similar to
MRI‐AGCM3.1S

MRI‐AGCM3.1S(1979‐2003)

Hiroshima

CMIP3 ave.
(1981‐2000)

Kushiro14

Michihiro et al. (2011)

Reproducibility of Monthly Mean Precipitation

observed

Sapporo1971‐2000
2071‐2100

Monthly Change over Japanese Land
250

Reproducibility of Annual Mean Precipitation

Precipitation BIAS [ratio]

海面気圧hPa hPa
Sea Level
Pressure

300

Michihiro et al. (2011)

AMEDAS(1971‐2000)

150

30

INGV‐SXGMIROC3_2h
CCSM3UKMO‐HadGEM1
降水量 mmCSIRO‐Mk3_0
CSIRO‐Mk3_5
ECHAM5_MPI‐OM
GFDL‐CM2_0
GFDL‐CM2_1
BCCR‐BCM2_0
CGCM3_1_T63
CNRM‐CM3
MIROC3_2m
MRI‐CGCM2_3_2
PCM
FGOALS‐g1_0
UKMO‐HadCM3
IPSL‐CM4
GISS‐AOMCGCM3_1_T47
ECHO‐G
GISS‐EH
GISS‐ERINM‐CM3_0
Jan JRA‐25
Feb Mar Apr May Jun Jul Aug
Sep Oct Nov Dec
Observed
気象官署

GCM screening is necessary

比湿g/kg
Specific
Humidity
g/kg

20

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Monthly Change over Japanese Land

0‐10

顕熱フラックス W/m2

上向き短波放射量W/m2↑ Shortwave
Flux W/m
40

100

1000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Some GCMs are good and others are not.

20C3m: 1971‐2000

10

1

200

1005

0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

152

下向き短波放射量W/m22↓ Shortwave
Flux W/m
300

1010

0

20

3

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

1020

風速
m/s m/s
Wind
Speed

4

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

300

1025

0
‐10

Latent Heat Flux W/m2
潜熱フラックス W/m2

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

上向き長波放射↑LongwaveW/m
Flux2 W/m2

400

10

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

下向き長波放射↓LongwaveW/m
Flux2 W/m2

20

10

0

地表面温度
℃
SurfaceTemperature
C

30

20

20
10

0

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

20

50

400

1971‐2000
2071‐2100

05

地上気温 ℃ C
Air Temperature

30

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

100

100

30

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

40

200

降雪水量 mm
Snowfall
mm

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

150

20C3m: 1971‐2000 A1B: 2071‐2100

対流性降水量mm mm
Convective
Precipitation
40
150

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

60
10

降水量 mmPrecipitation
mm

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

300

200

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

2
上向き短波放射↑ShortwaveW/m
Flux
W/m2

80

15

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

2
下向き短波放射↓ShortwaveW/m
Flux
W/m2

250

Best 10 GCMs

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

Specific
比湿 g/kg Humidity g/kg

100

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

300

20

Application to River Basin Analysis

Temperature Change in some River Basins

The analysis of an arbitrary basin is possible by “Database”.

Major 7 river basins in Japan
Mesh shows 80km x 80km grid in database

Climate Change: air temperature [C]Data
 CMIP3 25 models
 MRI‐AGCM 3.1S, 3.2S
 AMeDAS observation

Ishikari

Ishikari: northern part

P: 1981‐2000 F: 2081‐2100

Kiso: central part

Chikugo: southern part

Period Present 1980‐1999
 Future 2080‐2099

Kiso

Elements
 Monthly precipitation
 Monthly temperature

Chikugodashed: MRI‐AGCM3.1S

solid: CMIP3 ave. (Best 7 GCMs as Reproducibility)‐‐A1B ‐‐A2 ‐‐B1

 SRES scenario differences are remarkable Temperature variability increases in the northern part
 CMIP3 ave. is similar to MRI‐AGCM3.1S
Michihiro et al. (2012)
IHP Training Course Lecture7, 10/Dec/2013

Michihiro et al. (2012)19

IHP Training Course Lecture7, 10/Dec/2013

Precipitation Change in some River Basins

Climate Change: precipitation [ratio]
Ishikari: northern part

Application to Hydrological Analysis

P: 1981‐2000 F: 2081‐2100

Kiso: central part20

Climate Change
Value

Observed
Database

Hydrological
Model

Hydrological
Environment
Projection

Chikugo: southern part

River Discharge

Temperature
Precipitation
dashed: MRI‐AGCM3.1S

solid: CMIP3 ave. (Best 7 GCMs as Reproducibility)‐‐A1B ‐‐A2 ‐‐B1

 SRES scenario differences are not remarkable Precipitation variability increases in the southern part
 Fluctuation of monthly variability exists in MRI‐AGCM3.1S
21

Ise Bay

MikawaBay

layer
ＣＣ layer
Ｄ layer

Ｄ layer

Sediments

A2

A1B B1

Sato et al. (2012)
22

Objectives of Coupled River and Ocean Model

Area

Volume

km2

km3

Mean
depth
m

Basin
area
km2

Ise Bay

1738

33.90

19.5

13411

Mikawa
Bay

604

5.54

9.2

3624

 The hydrologic environment of coasts and bays is strongly
influenced not only by ocean circulation from tides and wind,
but also by river influx such as fresh water, nutrients,
sediments and others.
 It is necessary to connect river runoff and ocean circulation
models to realize integrated environment management of thewatershed area, including rivers and ocean.

Volume of Ise Bay= 1.9 years x river discharge
Coupling of river
runoff model andocean circulation
model

Volume of Mikawa Bay= 2.8 years x river discharge

Environmental database of Ise Bay
http://www.isewan‐db.go.jp/about/index.asp

layer
ＢＢ layer

IHP Training Course Lecture7, 10/Dec/2013

Outline of Ise Bay

Bay

Water Temp.
ＡＡ layer
layer

Difference
betweenFutureandPresent

Multi-Model
Multi-Scenario

Michihiro et al. (2012)
IHP Training Course Lecture7, 10/Dec/2013+

Study on Kiso riverand Ise Bay

Impact assessmentof climate change

AdaptationImpacts of river discharge on Ise Bay
and Mikawa Bay are larger thanthose on Tokyo Bay(7 years) andOsaka Bay (5 years).

IHP Training Course Lecture7, 10/Dec/2013

23

IHP Training Course Lecture7, 10/Dec/2013

24

Specifications of coupled river and ocean model

Schematics of coupled river and ocean model

Distributed run-off modelRun-off

Hydro-BEAM

Dynamic wave
sediments

Ocean circulation

Dynamic wave model
Q,Temp,SS

Evapotranspiration

Ocean circulation mode
RIAMOM
with Advective-Diffusion

Dynamic wave

River runoff

ＡＡ layerlayer

Ocean circulation model

Channel topography
Dam operation

layer
ＢＢ layerlayer
ＣＣ layer

RIAMOM

Contents

Item

Contents

Cell size

Horizontal: 1 km Vertical: 4 layers
Rainfall,evapotranspiration,
snowmelt, outflow, undergroundseepage
Surface: kinematic wave model
Subsurface:storagefunction
model

Basic
equations

Equations of motion
Equation of continuity
Hydrostatic equation
Advective equation of tracer

Variables

Current
vector(u,v,w),pressure (surface height),
water temperature, salinity

50 m mesh DEM

Coordinate

Z (60 layers)

Forest, grass, paddy field, city,
water
Roughness, albedo, moisture
content, hydraulic permeability,
soil porosity

Horizontal
grid

Latitude and longitude (1/72
degree)Open
Ocean
Boundary
Tidal data

JCOPE2 re-analysis(1/12 degree)

Hydrologic
processes
Runoffprocess

Advective-diffusion

Rainfall

Hydro-BEAM
Item

River
channelLand use
Hydrologic
parameters

Rainfall, temperature, wind speed,
Meteorologic
pressure, water vapor pressure,
al datasunshine duration

Ｄ layer

Ｄ layer

Q,Temp,Water level

Outputs

Riverdischarge,
water
temperature,densityofsuspended sediment, others

Meteorologic
al data
Outputs

Boundary condition
JCOPE, Climate Change Modification
25

data

NAO tide modelWind,
temperature,sea
surface pressure, humidity,
cloud amount, rainfall
Current(u,v,w),water
temperature,salinity, sea surface level

IHP Training Course Lecture7, 10/Dec/2013

IHP Training Course Lecture7, 10/Dec/2013

Case study 2003/Apr.

Sea surface water temperature

Precipitation at Nagoya

25

木曽川
長良川
揖斐川

4000
3000

河川水温 ℃

2000

Water temperature

木曽川
長良川
揖斐川

20
15

Sediments transport

10

1000
0

5

23

24

25

26

27

28

29

日

23

24

Stationary front
Heavy rain at west region of Japan
Suzuki et al. (2012)25

土砂流入量 ton/s

河川流量 m3/s

5000

26

0.9
0.8
0.7
27
0.6
0.5
0.4
0.3
0.2
0.1
0.0

木曽川

Initial

28 長良川＋揖斐川
29 日

23

24

25

26

27

IHP Training Course Lecture7, 10/Dec/2013

28

29 日

Suzuki et al. (2012)27

4 days28

Suspended sediments

6 days

InitialSuzuki et al. (2012)

Suzuki et al. (2012)
IHP Training Course Lecture7, 10/Dec/2013

24 hours

Observed river water temperatures are set
as boundary conditions of advectivediffusion calculation.
IHP Training Course Lecture7, 10/Dec/2013

Sea surface water temperature

2 days12hours29

12hours

24 hours

Boundary conditions are set by the
relation between observed SS(suspended solid) and river discharge
rate.
30
IHP Training Course Lecture7, 10/Dec/2013

Suspended sediments

2 daysComparison with the satellite image

4 days6 days
Suspended sediments
calculation at 9 JST
27/Apr/2003.

Suzuki et al. (2012)31

IHP Training Course Lecture7, 10/Dec/2013

Suzuki et al. (2012)

Muddy water distribution from Aqua MODIS
satellite image at 13:15 27/Apr/2003.
32

IHP Training Course Lecture7, 10/Dec/2013

Present climate calculation

Observed data at Ise Bay

Reference:
Mie Prefecture Fisheries Research Institute: Fish and marine
information report, 2000-2010.

Conditions of present calculation
 Period from 2004/Jan to 2004/Dec.
 Monthly calculation from 16 day of previous month to the last dayof this monthInterval ： Every month Atmospheric boundary conditions are JMA MSM GPV (5km mesh)

Points Open ocean boundary conditions are JAMSTEC JCOPE2 analysis： 16 points

Observation elements :water temperature, salinity, current,suspended solids, DO, chl-a, pH,
COD, NH4-N, NO2-N, NO3-N,
PO4-P, plankton(5 min mesh) River boundary conditions are observed river discharge, watertemperature and suspended sediments
 Ise Bay is represented by 1.5 km mesh

Suzuki et al. (2013)33

IHP Training Course Lecture7, 10/Dec/2013

Verification of model result against observation
Water
temperature
水温（モデル1層or実測2m）

35

Future projection

Calculation conditions①① CMIP3 climate change values are added to
1月
the atmospheric and open ocean boundary
2月
3月
conditions.
4月② River discharge is estimated using relation
5月
between present and future calculation of
6月
7月
Hydro-BEAM.
8月③ River water temperature is estimated from9月
the difference between present and future calculation. 10月

Current
流速（モデル1層or実測5m）
speed

1.0

30

0.8

25
0.6

20
15

0.4
現在

10

現在

水温0m[℃]5

水温2m[℃]

0
1

2

3

4

5

6

7

8

9

40

0.2
流速[m/s]0.0
1

10 11 12

Salinity
塩分（モデル1層or実測2m）

2

3

4

5

6

7

8

9

10 11 12Current
direction
流向（モデル1層or実測5m）

360
315

35

11月
12月

270
30

225
180

塩分2m[PSU]

1

2

3

4

5

6

7

8

9

10 11 12現在

濁度[mg/L]

1

2

3

4

5

6

7

8

9

10 11 12

45
2

3

4

5

6

7

8

9

10 11 12Comparison between observation (green) and model(blue)
Model water temp. is larger than observation in winterModel salinity and SS are larger than observation
Current direction does not correspond withobservation due to small current speed

IHP Training Course Lecture7, 10/Dec/2013

4000
y = 1.082 x ‐ 10.603
R² = 0.988

流向[°]

0
1

Suspended
solids
濁度（海表面or0m）

50
45
40
35
30
25
20
15
10
5
0

現在

90

35

3000

2000

1000

③

Water temp. diff.(future – present)

塩分0m[PSU]

将来の日流量 m3/s

現在

15
10

②

135

20

CMIP3
average
CMIP3_15モデル平均
Temp.
Rain
SST
降水量
気温
海水温
×α倍
＋β℃
＋γ℃
1.038
3.249
2.075
1.071
3.445
2.076
1.057
3.126
2.101
1.027
2.870
2.033
1.065
2.933
1.976
1.039
2.939
1.981
1.109
2.787
1.947
1.129
2.953
1.923
1.067
3.133
1.923
0.991
3.081
1.991
0.913
2.963
1.986
0.955
3.158
2.028

CMIP3_15モデル平均 流量（木曽川）

Future daily river discharge (m3/s)

25

34

IHP Training Course Lecture7, 10/Dec/2013

Order of daily air temperature
0
0

1000

Suzuki et al. (2013)

2000
3000
4000
m3/s
IHP (mTraining
Course Lecture7, 10/Dec/2013
3/s)
Present 現在の日流量
daily river discharge

36

Future SST changes by CMIP3 models

Monthly mean SST distribution and future change in February

SST changes nearIse Bay area from SRES A1B scenario
伊勢湾近海における海面水温変化値（SRES
A1B）
4.5

30℃

Present

bccr_bcm2_0_r1

4.0

26
24

cccma_cgcm3_1_t63_r1

Future海面水温変化値
SST changes (deg)Future28

cccma_cgcm3_1_r1

3.5

22

cnrm_cm3_r1

20

gfdl_cm2_0_r1

18
16

3.0
2.5

giss_aom_r1

14giss_model_e_h_r112giss_model_e_r_r2
iap_fgoals1_0_g_r1

2.0

inmcm3_0_r1
1.5

Future change

miroc3_2_medres_r2

SST increase by about 2 deg.
Increase at coastal area is large.
3 deg. Increase at river mouth.

miub_echo_g_r1
1.0

mpi_echam5_r1
mri_cgcm2_3_2a_r1

0.5

ukmo_hadcm3_r1
ukmo_hadgem1_r1

0.0
1

2

3

4

5

6

7

8

9

10

11

12

CMIP3平均

Month
月
37

IHP Training Course Lecture7, 10/Dec/2013

Monthly mean SST distribution and future change in August

Future changes
Watertemperature
水温（モデル1層or実測2m）

35Present

Future28

Currentspeed
流速（モデル1層or実測5m）

1.0

30
30℃

38

IHP Training Course Lecture7, 10/Dec/2013

0.8

25
0.6

20
26

15

0.4

24
現在

10

22

将来

0
18
16

12

Future change
Future SST exceeds 30 deg.
Increase at coastal area is large.
SST increase is larger than winter.
→effect of temperature
stratification

2

3

4

5

6

7

8

9

10 11 12

1

Salinity
塩分（モデル1層or実測2m）

35
34
33
32
31
30
29
28
27
26
25

2

3

4

5

6

7

8

9

10 11 12Current
direction
流向（モデル1層or実測5m）

270

225

180
現在

現在

135

将来

将来

90
1

2

3

4

5

6

7

8

9

1

10 11 12

2

3

4

5

6

7

8

9

10 11 12Comparison between present calculation (blue) andfuture calculation (red)Future water temp. will increase by about 2-3 deg.
There are no clear difference among other elements.

39

IHP Training Course Lecture7, 10/Dec/2013

将来

0.0
1

14

現在

0.2

5

20

40

IHP Training Course Lecture7, 10/Dec/2013

Bias correction of water temperature calculation

Sweetfish data at Nagara river estuary barrageCount the numberof sweetfish runupin the fishway

35

Present watertemperature
水温 ℃

30
25
20

Model
計算（バイアス補正）

15160,000

観測値
Obs 0m
(0 m)

10

観測値
Obs 2m
(2 m)

1

3

4

5

6

7

8

9

10 11 12月

平成14年
2002
平成15年
2003

120,000

平成16年
2004
平成17年
2005

100,000

35
30
水温 ℃

Future andpresent watertemperature

2

2000
平成12年
平成13年
2001

140,000

5

Future water temperature
becomes not less than 10
deg. in winter, and exceeds
30 deg. in summer.

25
20
15

現在（バイアス補正）
present

10

80,000

平成18年
2006
平成19年
2007

First runup
group

平成20年
2008
平成21年
2009
平成22年
2010

60,00040,00020,000

将来（バイアス補正）
future

0
3月1日
March

5
1

2

3

4

5

6

7

8

9

10 11 12IHP Training Course Lecture7, 10/Dec/2013

41

3月21日

4月10日
April

4月30日
May

5月20日

6月9日
June

6月29日

Data source: Nagaragawa Estuary Barrage Operating & Maintenance Office

IHP Training Course Lecture7, 10/Dec/2013

42

Definition of the first runup group

Seasonal change of water temperature

Number of daily runup in 2000
Last
date of
first
group

Numberof first
group

5%
excessdate of
first
group

30

5

2000

－

2001

－

－

－

25

4

－

－

20

2002

－

－

－

2003

4/12

35,972

3/21

2004

3/26

15,153

3/15

5

2005

4/9

6,317

4/2

0

60,00040,00020,000
0

March
3月1日

April
4月1日

May
5月1日

June
6月1日

月 日

The first runup group is defined as the peak tovalley of the first, which is shaded gray.

2006

3/21

3,928

2007

4/14

17,426

3/11
3/4

2008

4/10

295,822

3/28

2009

4/23

407,244

4/12

2010

4/16

22,417

3/27

2001～2011年月別平均水温差（海域地点1―伊勢大橋）
ocean and river

15
10

伊勢大橋
river
海域地点1
ocean

3
2
1
0
‐1

1

1

2

3

4

5

6

7

8

9

10

11

4月16日

4月11日

4月11日

Runup delay

In the book of Isao Takahashi, he saysthat this relation is misunderstanding.

If water temperature difference islarge in spring, the time of same level
temperature is delayed.

1

‐1

1

2

3

‐2

4

5

6

High rivertemperature

7

8

9

10

11

12

Which results in the delay ofsweetfish runup

45

現在水温差
Present

3

Future
将来水温差

Runup date at
15/Mar in 2004

20Bias correction of
RIAMOM watertemperature, andHydro-BEAM
water temperature

Runup date at
10/Feb in 2104

‐1

Water temperature increase
river > ocean

‐2
‐3
Jan.
1月1日

Feb.
2月1日

44

4月1日
3月27日
y = 8.5228x + 41355
R² = 0.583

3月17日

y = 7.2749x + 41357
R² = 0.4727

3月22日
3月17日

2004

3月12日

2004

3月12日
3月7日

Large diff.

3月2日

Large diff.

3月2日
-3

-2

-1

0

1

2

3

4

-3

Station 1
Depth 10m

-2

-1

0

1

2

3

4

3～4月平均水温差℃（海－河川）
Water
temp. diff. in Mar.-Apr.

3～4月平均水温差℃（海－河川）
Water temp. diff. in Mar.-Apr.

In the year of high watertemperature difference,sweetfish runup of the firstgroup is delayed.

Station 2
Depth 24m

Mar.
3月1日

Apr.
4月1日

May
5月1日

Jun.
6月1日

Assumption: Runup threshhold temperature 1.4deg does
not change during this 21st Century.
Then future sweetfish runup date may become earlier by
about 1 month at the end of this century.
IHP Training Course Lecture7, 10/Dec/2013

IHP Training Course Lecture7, 10/Dec/2013

46

Conclusions

Present and future water temperature difference between
river and ocean (st.1)

4

1

11

月

Future change of sweetfish runup date

5

10

Month

IHP Training Course Lecture7, 10/Dec/2013

水温差℃ (海－河川）

水温差（℃）

2

0

9Runup delay

4月6日

4月1日

3月7日

High ocean
temperature

8

Station 2

4月16日

3月22日

3

7

IHP Training Course Lecture7, 10/Dec/2013

3月27日

4

6

From autumn to winter : ocean temperature > river temperature
From spring to summer : river temperature > ocean temperature

4月6日

River temperature increases in early spring

2001～2011年月別平均水温差（海域地点1―伊勢大橋）

5

月
Month

Station 1

Water temperature difference between ocean and river

4

Water temperature difference and date of the first group runup

River temperature is larger than ocean in winter

5

3

12

月
Month

Relation between sweetfish runup and water temperature

Sweetfish starts to runup when water temperatureof river and ocean is at the same level

2

‐2

43

IHP Training Course Lecture7, 10/Dec/2013

Water temperature difference between

Mean watertemperature 2001-2011
2001～2011年月別平均水温

水温差（℃）

Year

平成20年
80,000

水温（℃）

日別遡上数（10日移動平均）

100,00047

 Climate Change Information Database for Hydrological
Analysis is developed and the data are provided freely fromweb (http://hes.dpri.kyoto-u.ac.jp/database/) The objective of the database is to provide quick access
way to persons, who are not familiar with GCMs.
 Coupled river and ocean model was developed, which wasapplied to the sediment transport calculation from Kiso river
to Ise Bay.
 Future water temperature increase of Kiso river is about 3
degrees, and that of Ise Bay is about 2 degrees. Coastal
areas are more strongly influenced.
 Sweetfish runup time has negative correlation with watertemperature difference (sea – river). Calculated futurewater temperature difference is smaller than that of present,
which results in the early start of river run-up time ofsweetfish.
IHP Training Course Lecture7, 10/Dec/2013

48

12

Lecture 8: IHP Perspectives on Ecohydrology and Related Demonstration
Projects
Shahbaz KHAN (Regional Science Bureau for Asia and the Pacific, UNESCO)UNESCO’s IHP Ecohydrology Programme is focused on an integrated understanding of biological andhydrological processes at a catchment scale in order to create a scientific basis for a socially acceptable, costeffective and systemic approach to the sustainable management of freshwater resources. The aims of the
program are to:• advance the integration of social, ecological and hydrological research; and• generate outcomes that enable the development of effective policies and practices.
The Ecohydrology theory is based upon the assumption that sustainable water resources management
can be achieved by:• Reversing degradation and regulating the evolutionarily-established processes of water andnutrient circulation and energy flows at a catchments scale;• Enhancing the carrying capacity of ecosystems against human impact (resilience, robustness,biodiversity, ecosystem services for societies)• Using water biota interplay as water management tools.
The EHP has been formulated as a key theme of the Seventh Phase (2008-2013) of the International
Hydrology Program’s (IHP) action oriented and policy relevant knowledge generation, sharing and technicalcapacity building. This theme is contributing to a better understanding of water as both an abiotic resource andas a service delivered by ecosystems. The EHP aims to exemplify trans-disciplinary, cost-effective solutions towater related issues in variety of ecosystems and climatic zones. EHP remains a key area IHP Phase VIII(2014-2021) as eco-hydrology, engineering harmony for a sustainable world.
The main purpose of a network of Demonstration Projects is to showcase how to identify, quantify andimprove the critical interrelationships between water, biota and social systems for sustainable water
management, following the principles of the concept of UNESCO IHP Ecohydrology.
The Ecohydrology concept should operate in Demonstration Projects on four levels:• Information (monitoring, collecting of empirical data, defining interactions and hydrology-biotasociety feedbacks),• Knowledge (defining patterns, describing and explaining processes)• Wisdom (ability to formulate policy, principles for action, problem solving by system solutions,stakeholders involvement, education, implementation)• Cooperation for solving problems (willingness of different stakeholders to effectively contributeactively to the implementation of the EH approach in the demonstration site – this will ensure
cooperation among all sectors and the achievement of the demonstration site goals)
There are three key objectives of this network:• Synthesize knowledge gaps for addressing ecohydrological issues related to water ecosystems
under pressure.

• Showcase how better knowledge of the biological and hydrological interrelationships in aquatic
ecosystems can promote the long term sustainable carrying capacity of ecosystems and thus
contribute to more cost-effective and environmental-friendly water management.
• Demonstrate systems solutions and technology transfer opportunities through North-South andSouth-South linkages.
The ASPAC Ecohydrology program now has three operational projects and twelve evolving projects.

UNESCO and Water

IHP Perspectives on
Ecohydrology and Related
Demonstration Projects

Division of Water Sciences

Water Centers
under theauspices of
UNESCO

Shahbaz Khan
UNESCO Regional Science Bureau for Asia and the Pacific Region

Ecohydrology for Sustainability

HydrologicalResearch

World Water
Assessment
Programme

Intergovernmental
programme

Inter agency
Programme led by
UNESCOUNESCO-IHE
R&D, Training
Institute for Water
and Policy
Education
Advice+regional presence

IHP-VIII 2014-2021

IHP-VII (2008-2013)
IHP programmes
- IFI
- ISI
- PCCP- JIIHP- ISRAM- G-WADI- UWMP-WHYMAP- HELP- FRIEND

International
Hydrological
Programme

Key Theme:
I

THEME 1:
Adapting to the Impacts of Global Changes
in River Basins & Aquifer Systems
THEME II:
Strengthening Water Governancefor Sustainability

THEME III:
Ecohydrology for Ecosystem Sustainability

New Initiatives:
II, III, IV, V

THEME IV:
Water and Life Support Systems

THEME V:
Water Educationfor Sustainable Development

Water Resources
Management

Education & Capacity Building

IHP-VII THEME III
Ecohydrology for
Sustainability

Objective:
Enhance the designation of water bothas an abiotic resource and as a service,delivered by ecosystem processes;
identify, quantify and improve the criticallinkages for environmental sustainability.

Focal Area III-1: Ecological measures toprotect andremediate catchments process.
Focal Area III-2: Improving ecosystem
quality and services by combining structural
solutions with ecological biotechnologies.
Focal Area III-3: Risk-based environmental
management and accounting.
Focal Area III-4: Groundwater-dependent
ecosystems identification, inventory andassessment.

How Do We WorkDEMONSTRATIONThe Programme has developed a criteria to recognize sites where sustainable, innovative and transdisciplinary water
management practices based on ecohydrology principles are implemented. At the same time, the new Demonstration
Projects call was launched in 2010 to identify opportunities to demonstrate the application of the ecohydrology approach tosolve issues surrounding water, environment and people. These sites will also be featured in scientific research andtraining activities under the programme.
GENDER & SOCIAL CULTURAL BIODIVERSITY
The Programme is aiming to bridge the gap between the hydrology, social and ecological/environmental sciences by
exploring community cultural values and in so doing reframe the policy discourse in language that is more appropriate to the
purpose of engaging the ‘grass-roots’ community in the eco-social action planning processes as a basis for social change.
INTEGRATION AND UPSCALING
We are investigating the key intersections between socio-economics sciences and the hydrological/ ecological cycles. In
developing ecohydrology from its core beliefs and principles, using thesystems approach, there has been a conscious
effort to assimilate new paradigms and thinking into it. The strategy for enhancing ecohydrology science includes:• Understanding the past efforts• Integrating various disciplines• Harmonizing societies priorities by increasing the carrying capacity of an ecosystem• The formulation of a vision for sustainable water ecosystem.
MODELLING AND VISUALIZATIONThe Programme is working on tools to inform and support water managers and planners in making integrated water resource
management decisions using ecohydrology approach. This working group is aiming to cross boundaries across disciplines
such as water systems, economics and social science to promote integration to develop software tools for solving real
problems.
EDUCATION & CAPACITY BUILDINGThe Education and Capacity Building in Ecohydrology includes development of a curriculum of academic courses andpractitioner trainings to enhance ecohydrology knowledge and implementation on the ground. The Erasmus Mundus Masterof Science in Ecohydrology is a good experience.
More information can are available under the link www.ecohyd.org

Ecohydrology Actions
Local Solutions Global Impact

Action Areas

Demonstration Projects
Showcase how to identify, quantify and improve the critical interrelationships between
water, biota and social systems for sustainable water management, following theprinciples of the concept of UNESCO’s IHP Ecohydrology Programme.
The four levels of Project
Demonstration
1. Information (monitoring, collecting of
empirical data, defining interactions andhydrology-biota-society feedbacks),
2. Knowledge (defining patterns,
describing and explaining processes),
3. Wisdom (ability to formulate
policy, principles for action, problem
solving by system solutions,stakeholders involvement,
education, implementation),

Zalewski ERCE, Poland

Demin Zhou

4. Cooperation for solving problems(willingness of different stakeholders toeffectively contribute actively to theimplementation of the Ecohydrology approach in
the demonstration site – this will ensure
cooperation among all sectors and theachievement of the demonstration site goals).

Zalewski ERCE, Poland

Key Objectives ofDemonstration
Projects
1.
2.

3.

Synthesize knowledge gaps for addressing ecohydrological issues related to water
ecosystems under pressure.
Showcase how better knowledge of the biological and hydrological interrelationships inaquatic ecosystems can promote the long term sustainable carrying capacity of
ecosystems and thus contribute to more cost-effective and environmental-friendly water
management.
Demonstrate systems solutions and technology transfer opportunities through North-Southand South-South linkages by closing with the UNESCO IHP Water Family.

Demonstration Projets
NetworkGlobal Reference Projects
I - Ecohydrology based urban water
management and city planning for humanhealth and sustainable development.
City of Lodz, Poland.

II - Development and implementation of
the Ecohydrology concept for reduction
cyanobacterial blooms in a man-madereservoir.
Pilica River Catchment, Poland.

© M. Zalewski

Demonstration Projets
Network
OPERATIONAL PROJECTS
I - Improving land use policy at Lacar Lake
Watershed based on an Ecohydrologicalapproach. San Martín de los Andes,
Neuquén. Argentina.

II - Management of Regional Water
Resources linking with Maintainingof Wetland Biodiversity in the
Suburban Area of Metropolitan
Beijing, China.

Demonstration Projets
Network

OPERATIONAL PROJECTS

III - Watershed management in Northeast China: hydrologicalprocesses, vegetation optimization and sustainability.
IV - Ecohydrological analyses in
the lowland river catchment
Kielstau, Germany, for sustainable
water resources management andeducation in rural landscapes.

V - Sustainable water resourcesmanagement plans in the Tiber river
basin for environment protection,
minimum instream flows regulation andthe Trasimeno Lake ecosystempreservation. Italy.

Demonstration Projets
Network

Demonstration Projets
Network

EVOLVING PROJECTSOPERATIONAL PROJECTSI - Developing fit-for-purpose tools to addresscomplex social, ecological and economic issues
in water planning,
Australia.

VI - An Initiative to move the Naivasha
Basin towards sustainable use.: A
proposal to the supermarket buyers offlowers from Lake Naivasha, Kenya.

©David Harper

VII - Integrated Catchment Management of
Putrajaya Lake and Wetland,
Malaysia.

II - Ecohydrology in action: Addressingchanging hydrology, ecological conditionand community attitudes to water at the
Ord River, Western Australia
III - Ecohydrology of Western Sydney
Australia– Developing Solutions forEnvironmental-friendly Water
Management in Peri-urban Landscapes.
IV - Integration of Eco-hydrological Processes
Demonstration Research in Heihe River Basin, Northwestof China.

Demonstration Projets
Network

Demonstration Projets
Network

EVOLVING PROJECTSEVOLVING PROJECTS

V – Linkage of wetland ecology and hydrology with support of information
techniques for assessing the degrated inland fresh water wetland habitat inSanjiand Plain Northeast China.

VII - A methodology to estimate compensatory runoff in Costa Rica,
RANA-ICE study.
VIII - Central American Integration under the Trifinio Plan: Study ofintegrated management of natural capital in El Salvador,
Guatemala and Honduras.

IX - Development of advanced ecohydrology tools for the sustainable
management of coastal wetlands: The case of Nestos River lagoons.
Greece.
© Demin Zhou

VI - Study of irrigation management practices and impacts of soil
salinization in Manas River Valley, Northwest China.

Demonstration Projets
Network
EVOLVING PROJECTSXI - Integrating watershed management activities with the protection ofcoastal coral reef ecosystems in Micronesia.

X - Improved ecosystem management to control euthrophication
at the Saguling reservoir and relevant hydro-meteorologicaldisasters by wetland construction and river flow regulation. WestJava Province. Indonesia.

Demonstration Projets
Network
EMERGING PROJECTSI - Assessing performance of ETP (Effluent Treatment Plant) using Duckweeds and activated sludge management system and sustainably
managing limnology to develop hydro ecological regime. Bangladesh.

XII - Understanding ecohydrological connectivity in multiple
catchments to conserve groundwater, protect surface water andcontain risks in a globalizing city. Philippines.

II - Restoration of Victoria Pond: Restoring wetland habitatin historic George Town, Great Exuma for sustainable management
to control eutrophication and enhance near shore fish habitat.
Bahamas.

XIII - Adaptive water management in response to hydro-climaticchange effects on ecosystem services and biodiversity of theSwedish Norrström drainage basin. Sweden.

III - Capacity Building in
Ecohydrology and promotion of
eco-farming practices at YEHA
Institute Farmers’ Academy,
Ethiopia.

Demonstration Projets
Network

Demonstration Projets
Network

EMERGING PROJECTSEMERGING PROJECTSIV - Integrated Water Resources Management as tool to control impact
of Small-scale Diamond Mining Operations in selected communities inthe Eastern Region of Ghana.
V - Caring for Cikapundung River by Reforestation, Fetiver
Plantation, Biopores, Relocation of Squatter, Settlements along theRiver Banks, Long Storage, and Eco-Technonology for Sustainableof Water Supply for Bandung City.
Indonesia

VIII - Restoration of Fluvial Territory in the lower reaches of
Aragón and Arga rivers (Navarra-Spain), as a means toincrease biodiversity and reduce flood risk and damages.

IX - Sustainable estuarine zone management for control ofeutrophication, toxic blooms and conservation of biodiversity in
the Kaštela Bay (Croatia, Adriatic Sea).

VI - Study of avian community during wetland restorationof Zhalong Nature Reserve in China.
VII - Rehabilitation of Balagunan Watershed to restore
biodiversity and flood control in Carmen, Davao del Norte.
Philippines.

Ecohydrology Demonstration
Projects Evaluation :Overall Impressions

Geographical situation of UNESCO-IHP Ecohydrology
Programme Demonstration Projects












AustraliaOperational Projects

Thank you

Level of EH approach application differentiates the projects.
Many new players.
People interacting with the program have submitted proposals.
Longer standing commitments of institutions.
Web sites for global reference and operational sites.
Scale and focus is important for all sites.
Integration of issues is very important.
Special volume on demo site projects using a standard template.
Africa needs more demonstration projects.
Latin and North America need to have further projects.
Eastern Europe not well represented.
Ecological responses to flow regimes may be strengthened.
EH of Dry land areas need to be emphasized.
Cold regions need to be brought in.
In many countries still engineers dominate the water field. There is a need to bring.
Ecohydrology approaches.
Possible participation funds for Ecohydrology projects.
The meeting of March 21-23, 2011 held in Jakarta was to invite EH Demo projects as afirst step to make them active.

Lecture 9: Fundamentals in Optimum Operation of Reservoir Systems
Tomoharu HORI (Water Resources Research Center, DPRI, 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.

Key variables to describe state of a reservoir

Fundamentals in Optimum
Reservoir Operation
T. Hori
Water Resources Research Center
Disaster Prevention Research InstituteKyoto University

Q(t ) : Storage volume at time t

S (t ) : Storage volume at time t
R (t ) : Storage volume at time tContinuity equation of a reservoirdS (t )
 Q (t )  R(t )dt© T. Hori, Kyoto University

Objective Function (2)Objective Function (1)Evaluation of the performance of operation

Evaluation function

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

Damage or Loss in case of flood control
Minimization problem
Benefit or Income in case of water supply and power
generation
Maximization problem
Conceptually can be expressed as an functionof release discharge and storage

J ( R (t ), S (t ))  {R (t )  Rtarget }2  {S (t )  S target }2
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 )


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

Formulation

Design operation and Real time operation

 t J ( R(t ), S (t )) dt 
 0


max
R (t )
0t T

Inflow discharge sequence Q (t ) : 0  t  T
Known for all time horizon

subject toDesign operation (off‐line operation) : to derive
optimum release sequence for historical hydrographsdS (t )
 Q (t )  R(t )dt0  R(t )  Rmax

Unknown in future from the current time
Real time operation (on‐line operation) : to derive
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)Smin  S (t )  Smax
(0  t  T )© T. Hori, Kyoto University© T. Hori, Kyoto University

Discretization in time and volume
From actual viewpoint, release cannot be changed
continuously in time
Discrete time system is introduced

Qi

i 1

S i 1

Si

The case where values of inflow discharge sequence
Qi (i  1,, I ) is known (given).
 Design operation
 Inflow values (levels) are explicitly given‐> Deterministic
 Single reservoir system

i 1

i

The most fundamental case

Deterministic Dynamic Programming
Operation (referred as DDP hereafter)

Ri
Hydrologic variables such as storage,
inflow, release are also discretized inlevel expression

level 2level 1

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

Deterministic DP operation

Deterministic DP operation
Problem FormulationImax   J ( R ,Si  Si 1 )max  J ( Ri ,2
Ri
 i 1

I

i

i 1

Ri
i 1,, I

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

subject toi 1,, I

Si1  Si  Qi  Ri

SI

SI 1

I

I 1

0  Ri  Rmax
Smin  Si  S max

12(i  1, , I )

where

5
12
7

Qi and Ri : inflow and release at step i, Si :storage at the beginning of step i
J (, ) : assessment function

10

Rmax :upper limit of release, Smin and Smax : lower and upper limit of storage.
© T. Hori, Kyoto University© T. Hori, Kyoto University

Deterministic DP operationImax   J ( R ,Rii 1,, Ii 1i

Si  Si 1 )2

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

SI

Deterministic DP operation

SI 1

I

I 1

12

Ri
i 1,, Ii 1i

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

 I 1S  Si 1
Q  RI 1  )  max  J ( RI , S I  I) max   J ( Ri , i

2
2
RI


i 1
Ri

for given S Ii 1,, I 
S  Si 1 I 1)  f I (SI )  max   J ( Ri , i2
 i 1Rii 1,, I

7
18

where

18

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

6
15

for given S I© T. Hori, Kyoto University© T. Hori, Kyoto UniversityDeterministic DP operation

Rii 1,, II

f I ( S I  level 0) 

12f I ( S I  level 1) 

18

SI 1

I 1

Si  Si 1 )2
Si  Si 1)2

 I 2max  J ( R ,for given S I

i

i 1

Ri
i 1,, I  2

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

SI

I

max   J ( R ,

i

i 1

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



I 1

SI 1

30

12
18

I 1

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

15
12

12
18

9

f I ( S I  level 2) 

1010f I ( S I  level 3) 

8

8

10
20
8
© T. Hori, Kyoto University

Recursive functions derived

Defining

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

produces




i 1

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 ifor given S I iI

max   J ( R ,Rii 1,, I© T. Hori, Kyoto Universityi

for i  0, , I  1

Si  Si 1 )2
f I 1 ( S I 1 )  0.

Si  Si 1
 I 3)max  J ( Ri ,2
Ri
 i 1i 1,, I  3

Q  RI  2)  f I 1 ( S I  2  QI  2  RI  2 )   .
 max  J ( RI  2 , S I  2  I  22R I 2
for given S I 2

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.

© T. Hori, Kyoto University

Consideration of uncertainty
In the Deterministic DP model, values of inflow level are
given. Actually the inflow level differs year by year evenin the same day in the year.

© T. Hori, Kyoto University

One‐order Markov Chain
Conditional probability of inflow levels during time period i, Qi ,for the levels of inflow in the previous perid, Qi 1.

Pr[ Qi | Qi 1 ]Stochastic approach is required.
Inflow level in each period is not independent: Highcorrelation between inflow levels at neighboring time
periods is usually observed.

© T. Hori, Kyoto University Pr[Qi  1| Qi 1  1] Pr[Qi  1| Qi 1  2] Pr[Qi  1| Qi 1  3]   Pr[Qi  2 | Qi 1  1] Pr[Qi  2 | Qi 1  2] Pr[Qi  2 | Qi 1  3]  Pr[Q  3 | Q  1] Pr[Q  3 | Q  2] Pr[Q  3 | Q  3] 
i
i 1
i
i 1
i
i 1


 0.6 0.3 0.1   0.3 0.5 0.2  0.3 0.3 0.4 © T. Hori, Kyoto UniversityOne‐order Markov Chain

One‐order Markov Chain

 0.6 0.3 0.1 
Pr[ Qi | Qi 1 ]   0.3 0.5 0.2  0.3 0.3 0.4 Once you have observed that the inflow discharge at the previous time
period, Qi 1 , is level 2, then you can obtain the probability distribution

 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

 0.6 0.3 0.1   0.3 
Pr[Qi ]  Pr[Qi | Qi 1 ]  Pr[Qi ]   0.3 0.5 0.2    0.5    0.3 0.3 0.4   0.2    0.6  0.3  0.3  0.5  0.1 0.2   0.3  0.3  0.5  0.5  0.2  0.2  .
 0.3  0.3  0.3  0.5  0.4  0.2 

of inflow at current time stage as:
Pr[Qi 1 | Qi 1  level 1]=  0.3 0.5 0.2  .
Note that if you specify the release discharge at level 2 when the storage 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: 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
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 by
5  0.3+12  0.5+7  0.2=8.9.

SI

SI 1

I

I 1

Stochastic Dynamic Programmingf I i ( S I i , QI i 1 )max

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

 f I i 1 ( S I i  QI i  RI i ) for i  0, , I  1

5127© T. Hori, Kyoto University

Summary There are two types of operational approach: namely design
operation 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© T. Hori, Kyoto University

Exercise 1: Fundamentals of Data Processing
Toshio HAMAGUCHI (Water Resources Research Center, DPRI, Kyoto University)
The aim of this lecture is to learn the fundamentals of the practical exercises on data processing inscientific fields. We have nowadays many computer languages to be interpreted into the machine language.
In this lecture, we highlight the FORTRAN language and give you the significant information on it at first.
Secondly, we teach you how to install the software of FORTRAN in freeware version (named as“gfortran”). Thirdly, how to program it based on the structured formulation of “FORTRAN90” is mainly
lectured while the source codes of available and applicable commands are seen as the typical examples in
the given slides. Next, how to compile the programs you described is introduced, and finally how to
execute it is mentioned. Four exercises concerning data processing are given during the above lecture
contents. Based on the basic knowledge of this lecture, when you increase the knowledge of the FORTRAN
commands, you can apply to the numerical analysis by making your program for yourself without
commercial software because you can customize the self-programmed codes according to the special
condition of boundaries, irregular sink/source terms, and domain discretization. It can be concluded that thefundamental study in this lecture is pretty important to make any programs in numerical processing.

UNESCO IHP Training Course
Exercise 1
Fundamentals of Data Processing

What is digital data?
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,etcToshio Hamaguchi
Disaster Prevention Research 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.

Interpreter 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, PUT, PRINT, GOTO,
NEXT, RUN, LOCATE, DATA, …

→FORTRAN90→ FORTRAN95→ FORTRAN2003

Others: FORTRAN, C, Pascal, perl, Ruby, JAVA, csh, bash…

→FORTRAN2008→?
We use it herein!

How to install gfortran system (1)Please browse the following webpage :http://tdm-gcc.tdragon.net
and download the installation package of TDM-GCC.

for 32bit OS
for 64bit OS

Fundamentals

How to install gfortran system (2)Run the downloaded execution file,and click the [Create] button in the following window.

Click here

How to install gfortran system (3)Choose and click the radio button according your OS,
and click the [Next] button in the bottom.

How to install gfortran system (4)Ex. in case of 64bit OS, input the folder name to be installed.

for 32bit OS
Input the folder name.
for 64bit OS

Click here after
the above choice.

How to install gfortran system (5)Choose the download site.
(Default one would be better.)

Click here after
the above input.

How to install gfortran system (6)Click the plus mark in front of “gcc” to selectthe compiling languages to be installed.

Choose the DL sitefrom the candidates.
Click the “+” markin front of “gcc”.
Click here after
the above choice.

How to install gfortran system (7)How to install gfortran system (8)Additionally click the mark in the box of “fortran.”(Preferred languages can be installed at the same time.)

Wait for finishing the download and installation process.

Click this box of“fortran”.

Click here after theabove check(s).

How to install gfortran system (9)When you get to the following window, you can successfully
finish the installation of “TDM-GCC.”

How to install gfortran system (10)This is the last window of installation.

Click here.

How to examine the feasibility of gfortran system (1)Open the command prompt window of “TDM-GCC”
in “START” menu on your screen.

Exercise 1: output command

1. Start the text-editor such as “Memo pad.”
2. you try to open a new file and input thefollowing 4-line codes in it:
write(*,*) ‘Hello, UNESCO IHP TC’
write(6,*) ‘I am ???’
stop
end

Please input yourname in “???” part.

where “write(n,m)” : “n” is the number of outputtedplace. “m” is the number of prescribed format.
3. After saving it as “test.f90” in the current folder,
enter the command of “gfortran test.f90 (enter)”on the command line.

Click here.

How to examine the feasibility of gfortran system (2)Input “gfortran -v” on the prompt. If you get the following
outputs on the screen, you’ll successfully install it.

Exercise 1: output command (Cont.)

So that, you can get the execution file “a.exe”
and run a(.exe).
If successful, you can display the following
output on the command line.
Hello, UNESCO IHP TC
I am ???

What is “write(6,*)” different from compared with“write(*,*)” ?  (Ans.) No difference“*” : used as a wild card (Default). Default of
outputted place and format are given as “onthe display (prompt line)” and “proper format.”So that, “write(*,*)” means “write(6,*)”

Exercise 1: output command (Cont.)

Edit the test.f90 as follows:
write(*,*) ‘Hello, UNESCO IHP TC’
write(16,*) ‘I am ???’
stop
end

Recompile “test.f90” (gfortran test.f90) and run it.
You can find 1-line output
Hello, UNESCO IHP TC

and moreover the new file “fort.16” in the current
folder by inputting the command “dir /w”.
After that, input the command “type fort.16”

Exercise 1: output command (Cont.)

Re-edit the test.f90 as follows:
write(*,*) ‘Hello, UNESCO IHP TC’open(16,file=“out16.dat”)
write(16,*) ‘I am ???’
stop
end

Recompile “test.f90” (gfortran test.f90) and run
it again.
You will find the new file “out16.dat” in thecurrent folder.

I am ???

to display the contents of “fort.16”

Exercise 1: output command (Cont.)

Exercise 2: Looping operation command

Edit the test2.f90 as follows:

Default of the REAL and INTEGER numbers

write(*,*) ‘Input the integer number’read(*,*) I
write(*,*) I+3
stop
end

- 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)

Compile “test2.f90” (gfortran test2.f90) and run it.
If successful, you will find the following outputs
on the screen.
Input the integer number
5
8

Exercise 2: Looping operation command (Cont.)

Q1. Program the integer summation codes in any ranges.

Q2. Program the output codes of an arithmetic sequence “3n-1”
where n: natural numbers.

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
end dowrite(*,*) J
stop
end

Exercise 3: Conditional command

The below codes are to calculate the integersummation from 25 to 75 in the range of 1 to 100.
write(*,*) ‘IF-code test’j=0
do i=1,100
if (i>=25 .and. i<=75) j=j+i
end dowrite(*,*) j
stop
end

Exercise 3: Conditional command (Cont.)

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 the same as previous one
without “integer do-loop” (using if-else-then codes).

write(*,*) ‘IF-code test’j=0
do i=1,100
if (i>=25 .and. i<=75) thenj=j+i
end ifend dowrite(*,*) j
stop
end

Exercise 3: Conditional command (Cont.)

The below codes are the same as previous one
without “integer do-loop” (using “case”).
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.’case default
write(*,*) ‘No calc. (n>75)’
end selecti=i+1
end dowrite(*,*) j
stop
end

Exercise 4: Dimension command (Cont.)

write(*,*) ‘IF-code test’
i=1
j=0
do
if (i>=25 .and. i<=75) thenj=j+ielse if (i==100) thenexit
end ifi=i+1
end dowrite(*,*) j
stop
end

Exercise 4: Dimension command

The below codes are to calculate the matrix
production using the Dimension command.
real dimension(3,3) :: a=0.0
real dimension(3) :: b=0.0,c=0.0
write(*,*) ‘Dimension-code
test’
do i=1,3
do j=1,3
write(*,*) ‘a(’,i,‘,’,j,‘)=’
read(*,*) a(i, j)
end dowrite(*,*) ‘b(’,i,‘)=’
read(*,*) b(i)end dodo i=1,3
do j=1,3c(i)=c(i)+a(i,j)*b(j)end doend dowrite(*,*) ‘c:’(c(i),i=1,3)
stop
end
 1.0
 4.0

  3.0

 1.0
1.0
2.0

2.0   1.0   5.0   2.0   2.0    0.0 
1.0   3.0   4.0 

Exercise 5: Data arrangement

When the input data are prescribed, the below
codes are available.

The below codes are to arrange given data fromdisordered one to elder one.

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
do j=1,3c(i)=c(i)+a(i,j)*b(j)end doend do 1.0 1.0 2.0   1.0   5.0  4.0 1.0 2.0   2.0    0.0 
write(*,*) ‘c:’(c(i),i=1,3)   stop 3.0 2.0 1.0   3.0   4.0 
end

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,&-0.6,-0.3,0.7,-0.1,0.4,-0.2,0.1/
write(*,*) 'data arrangement test'b=0.0
do i=1,20b=a(i)
mx=i
do j=i+1,21
if (a(j)>b) thenb=a(j)
mx=j

end ifend doif (mx /= i) thena(mx)=a(i)a(i)=bend ifend dodo i=1,21
write(*,'(A,I2,A,F6.3)') 'Rank',i,':',a(i)
end dostop
endExercise 5: Data arrangement (Cont.)

Q3. Program the data arrangement codes in younger order.

Q4. Program the data arrangement codes to find its median andto calculate its average.

Thank you!

Exercise 2: Data Analysis of GCM Data, 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.

23rd IHP Training Course(2013/12/5)

Back ground
Recently, a sense of impending crisis for the global
warming came to be realized.
Social needs for the concrete description of futureclimate has been increasing.

Kenji Tanaka
Water Resources Research Center,
DPRI, Kyoto University
tanaka.kenji.6u@kyoto-u.ac.jp

Although RCMs have accomplished remarkabledevelopment in recent years, the requirementfrom user side on the resolution and the accuracy
has been increasing more and more for detailed
assessment of climate change impact.
Thus, there is still a gap between precision realizedby models and that required from users.

Effect of bias correction on the climate change
impact assessment (from ICCAP)

RCM output v1

RCM output ｖ２

Bias detection

RCM output v3

Bias detection

Through the exchange of bias information,
RCM is tuned or updated. Then, model
bias of the next product will be reduced.

Bias detection& correction

Result of climate change signal can be opposite when raw
RCM output has large bias, suggesting the importance ofbias correction for climate change impact assessment.

Before bias correction
Unit(mm) Prec
Present 1189.6
Warm-up 897.0
Diff(W-P) -292.6

Evap
456.8
481.1
+24.3

Runoff804.3
503.1-301.2

Irrig
61.4
73.4
+12.0

 Bias evaluation for each prefecture / river basin unit
 Not only mean value, but also frequency distribution
 Designed to be general so that it can easily fit theAfter bias correction
Unit(mm)Present
Warm-upDiff(W-P)

Prec
634.0
464.3
-169.7

Evap
411.3
373.9
-37.4

Runoff281.5
168.9-112.6

Irrig
53.8
69.7
+15.9

difference of grid coordinate or change in model
setting Items to be evaluated (forcing variables for Land

Surface Hydrological model)
Prec, Tair, Eair, SWdown, LWdown, Wind, Psfc

Original Watershed code
1.

Combined Watershed

Creation of river basin mask and administration
mask (basmask1.f)

2.

Finding corresponding grid for each observation
station3.

Re-ordering model output

4.

Detection of model mean bias, calculating
frequency distribution

5.

Bias correction considering frequency distribution

River basin mask and prefecture mask
expressed by MRI-NHM grids

each model grid iscategorized into
each river basin.

each model grid iscategorized into
administrative unit.

River basin mask and prefecture mask
expressed by NIED-RAMS grids

each model grid iscategorized into
each river basin.

each model grid iscategorized into
administrative unit.

Finding corresponding grid1.

Creation of river basin mask and prefecture mask

2.

Finding corresponding grid for each observation

grid

station3.

Re-ordering model output

4.

Detection of model mean bias, calculating

grid point

frequency distribution
5.

Bias correction considering frequency distribution

AMeDAS
stationAvailable 20km model output
Organization
MRI
NIED
Tsukuba Univ.
Tsukuba Univ.
MRI

model version name (fmod)
NHM
Ver2
NHMMRI2RAMS
Ver1RAMSDP1RAMS
Ver1RAMSTU1
WRF
Ver1
WRF-TU1
GCM
GCM20kmperiod20yr (1985-2004)
29yr (1979-2007)
20yr (1985-2004)
20yr (1985-2004)
25yr (1979-2003)

Super high-resolution GCM（Global: AGCM）

General Circulation Model（Global: AOGCM）

Atmosphere
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

MRI-TL959(1920ｘ960ｘ60)
Horizontal resolution: 20 km
Vertical: 60 layers

Boundary
Condition

Ocean
O-GCM

IPCC AR4A1B Scenario

SST

name
NHMMRI1
NHMMRI2
RAMSDP0
RAMSDP1
RAMSTU0RAMSTU1
WRF-TU1
GCM20km

1979-2003 2015-2039 2075-2099

Difference(ratio) between
Near future and present

Bias detection/correction for each prefecture unit

Difference(ratio) between
End of 21st C and present

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
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
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

Bias correction system was developedin S-5-3 project (MOE fund)Model bias of 7 surface meteorologicalelements

Prec

Observation:
SDP, AMeDAS
X: month
Y: prefecture

Tair

Difference(ratio) between
Bias corrected and raw output

Bias Corrected / raw GCM
Near future / Present
End of 21st C / Present
bias corrected amount iscomparable or evenlargerthan climate change
signal every 1hr data are classified for each month

8 class (Prec) / 10 class (Tair) 2 steps correction

Temporal correction
with fixed ratio
Adjustment ofcorrection factor
for each class

Monthly mean value

Frequency distribution

fixed ratio and variable ratio

Store（α,i,j) ：change of class j amount due tothe change of correction factor of class i by αprovisional
optimal ratio

variable
ratio

fixed
ratio
＋α

class

1

2

3

4

5

6

7

8

階級１

階級２

階級３

階級４

階級５

移動先の階級 l1

元 の階級

delS(α,i) ： total change dueto the change of correction
factor of class i by α

k

2

3

4

5

6

7

8

Example of Store(α,i,j), delS(α,i)

和1del(1)

2

del(2)

3

del(3)

4

del(4)

5del(5)

6

del(6)

7

del(7)

8

del(8)

1
2
3
4
5
6
7
8

-0.14
-0.14
-0.14
-0.14
-0.14
-0.14
-0.14
-0.14

-80.68
55.43
0
0
0
0
0
0

0
-118.42141.69
0
0
0
0
0

0
0
-302.68-17.17
0
0
0
0

0
0
0
-27.84-18.38
16.61
0
0

0
0
0
0
0
-20.76
0
0

0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0

-80.68-62.99-161-45-18.38
-4.15
0
0

1
2
3
4
5
6
7
8

0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14

80.68
-76.28
0
0
0
0
0
0

0
139.27-100.84
0
0
0
0
0

0
0
261.84-63.31
0
0
0
0

0
0
0
108.31-36.19
0
0
0

0
0
0
0
54.58
4.15
0
0

0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0

80.68
62.99
161
45
18.38
4.15
0
0

＋α

階級１

階級２

階級３

階級４

階級５

Histogram (NHMMRI2, Prec, July)

移動先の階級 l1

元 の階級

Finding the best combinationof variable ratio while keeping
monthly mean value

k

2

3

4

5

6

7

8

和

Count at each class ( Aomori

2
3

102

102

102

101

101

4
5

100

6

5

10

100
15
20
Prec(mm/hr)

ds

階級４

階級５

101

Miyagi
0

)

5

10

100
15
20
Prec(mm/hr)

25

30

10

103

103

10

2

10

2

10

1

10

1

10

1

100

10

15
20
Prec(mm/hr)
Count at each class ( Kochi

25

30

Shizuoka
0)

NHMMRI2
Correct1
Correct2
obs(07)

5

100

10

15
20
25
Prec(mm/hr)
Count at each class ( Fukuoka )

30

103

103

102

102

101

101

0

5

10

100
15
Prec(mm/hr)

20

25

30

30

)

Osaka
5

10

15

20

25

30

NHMMRI2
Correct1
Correct2
obs(07)

104

102

Kouchi25

NHMMRI2
Correct1
Correct2
obs(07)

0

103

100

15
20
Prec(mm/hr)

Prec(mm/hr)
Count at each class ( Kagoshima)

NHMMRI2
Correct1
Correct2
obs(07)

104

10

4

2

Niigata

5Count at each class ( Osaka

NHMMRI2
Correct1
Correct2
obs(07)

4

10

5

Tokyo
0

Count at each class ( Shizuoka )10)

NHMMRI2
Correct1
Correct2
obs(07)

104

103

104

階級３

30

NHMMRI2
Correct1
Correct2
obs(07)

4

0

階級２

25

Count at each class ( Niigata
10104

Aomori0100

階級１Count at each class ( Tokyo

103

和

＋γ

)

NHMMRI2
Correct1
Correct2
obs(07)

103

8

＋α

Count at each class ( Miyagi

103

7

－β

)

NHMMRI2
Correct1
Correct2
obs(07)

1

104

101

Fukuoka
0

5

10

100
15

Prec(mm/hr)

20

25

30

Kagoshima
0

5

10

15
Prec(mm/hr)

20

25

30

Histogram (NHMMRI2, Tair, August)Count at each class ( Aomori

Count at each class ( Miyagi))

NHMMRI2
Correct1
Correct2
obs(08)

Aomori
90000
80000

NHMMRI2
Correct1
Correct2

12

)

20000
NHMMRI2
Correct1
Correct2
obs(08)

Miyagi
80000
70000

70000

Prec, 1hr, 0.900, July

Prec, 1hr, 0.990, July

90.0 Percentile Prec (07p1991-2004)

Count at each class ( Tokyo

90000

100000NHMMRI2
Correct1
Correct2
obs(08)

Tokyo
18000
16000

Prec, 1hr, 0.999, July

99.0 Percentile Prec (07p1991-2004)30

99.9 Percentile Prec (07p1991-2004)
70

NHMMRI2
Correct1
Correct2

NHMMRI2
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
285

290

295

300

Tair (K)Count at each class ( Niigata

290

295

300

305

290

Tair (K)Count at each class ( Shizuoka )NHMMRI2
Correct1
Correct2
obs(08)140000120000

295

300

305

Tair (K)Count at each class ( Osaka

120000Niigata

20

0
285

305
)

160000NHMMRI2:
2)

CC= 0.316

RMSE= 2.00

Correct1: CC= 0.087

RMSE= 2.58

Correct2: CC= 0.438

RMSE= 1.78

NHMMRI2:
5

CC= 0.231

RMSE= 6.40

Correct1: CC= 0.049

RMSE= 8.21

Correct2: CC= 0.744

RMSE= 3.40

100000NHMMRI2
Correct1
Correct2
obs(08)

Osaka
16000
14000

80000

0

NHMMRI2:

10

CC= 0.533

RMSE=10.85

Correct1: CC= 0.263

18000
NHMMRI2
Correct1
Correct2
obs(08)

Shizuoka

0

RMSE=14.98

Correct2: CC= 0.610

RMSE= 9.78

0
4

6

8

10

12

10

15

20

25

30

10

20

30

40

50

60

70

12000

100000

10000
80000

60000

Tair, 1hr, 0.900, Aug

60000
40000

306

6000

40000

4000
20000

20000

2000

0

0
285

290

295

300

Tair (K)Count at each class ( Kochi

305

295

300

305

Tair (K)Count at each class ( Fukuoka

)

290

295

300

310
NHMMRI2
Correct1
Correct2

308

60000

140000

302

306

300

120000304

50000

100000

40000

80000

298

302

306

30000

60000

296
40000
20000

40000

10000

20000

0

0

294

20000

Tair

300
(K)

305

290

292

294

296 298 300
Tair (K)

302

304

306

308

290

292

294

296 298 300
Tair (K)

302

304

306

308

304

300
NHMMRI2:295

NHMMRI2
Correct1
Correct2

308

NHMMRI2
Correct1
Correct2
obs(08)Kagoshima

80000

290

312

16000070000

0NHMMRI2
Correct1
Correct2

305

Tair (K)Count at each class ( Kagoshima))

NHMMRI2
Correct1
Correct2
obs(08)

Fukuoka

60000

99.9 Percentile Tair (08p1991-2004)

310

310

80000
NHMMRI2
Correct1
Correct2
obs(08)

100000304

Tair, 1hr, 0.999, Aug

99.0 Percentile Tair (08p1991-2004)

0290

120000KouchiTair, 1hr, 0.990, Aug

90.0 Percentile Tair (08p1991-2004)

8000

CC= 0.965

RMSE= 1.19

Correct1: CC= 0.990

RMSE= 1.06

Correct2: CC= 0.990

RMSE= 0.45

302
NHMMRI2:298

292

CC= 0.917

RMSE= 1.52

Correct1: CC= 0.968

RMSE= 1.51

Correct2: CC= 0.972

RMSE= 0.54

NHMMRI2:
300

296
296

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

Program and Data list
Prec, 1hr
CC (0.900)

Prec, 1hr
CC (0.990)

Prec, 1hr
CC (0.999)

Prec, 1day
CC (0.900)

Prec, 1day
CC (0.990)

Prec, 1day
CC (0.999)

Prec, 10day
CC (0.900)

Prec, 10day
CC (0.990)

Prec, 10day
CC (0.999)

GSI/basmask1.f
basinareaMODEL.txt
prefecareaMODEL.txt
gridriverMODEL.gad
gridprefecMODEL.gad
table-riverMODEL.txt
table-prefec.txt
AMEDAS/amedas1hrYYYY.gad
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
is not enough?

•SDP (meteorological observatory) 155stations
items: Psfc, Psea, Tair, Eair, RH, Wdir, Wind,
cloud, weather, Tdew, SShr, SWdown, Prec
•AMeDAS (Automatic weather station) ～1600stations
items: Prec, Tair, SShr, Wdir, Wind
47

47

系列1

系列1

SDP

AMeDAS

42

42

37

37

32

32

27

27

Let’s use global products!
It’s free!

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

Download page for Full Data Reanalysis

APHRODITE(http://www.chikyu.ac.jp/precip/index.html)
Asian Precipitation - Highly-Resolved Observational Data
Integration
Towards Evaluation of Water Resources

Procedure of data analysis
Step0: unpack the data (gzipped file )Step1: select the region for analysis (GSMaP)(GSMAP/daily/cutgsmap.f)
Step2: select the region for analysis (GPCC)(GSMAP/daily/cutgpcc.f)
Step3: calculate climatological value and correction factor(GSMAP/daily/climanal.f)
Step4: select the region for analysis (gsmap hourly)(GSMAP/hourly/cutgsmap.f)
Step5: change resolution to fit to the model grid
(GSMAP/hourly/chgres.f)
Step6: bias correction of GSMaP by GPCC climatology(GSMAP/hourly/bcgsmap.f)
Step7: re-ordering the data for HydroBEAM (2D  1D)(GSMAP/hourly/hbinput.f)

Step2: select the region for analysis(GPCC)(GSMAP/daily/cutgpcc.f)
parameter(IDIM=360,
JDIM=180)i=1
j=JDIM

j=1
Size of target area
W180
Is calculated automaticallydo iy=1986,2011
loop for year
write(cy,'(i4.4)') iy write the year information in cy
open(22,file='NILE/GPCC-Nile-1mon'//cy//'.gad',form='unformatted',access='direct',recl=mx*my*4)

i=IDIM
j=j1

j=j2

parameter(IDIM=3600, JDIM=1200)
parameter(latN=35.,latS=-5.,lonW=20.,lonE=45.)Set the analysis region

j=1

i=1

i1=int(lonW/0.1)+1
i2=int(lonE/0.1)
j1=int((60.-latN)/0.1)+1
j2=int((60.-latS)/0.1)
mx=i2-i1+1
my=j2-j1+1
Size of target area j=JDIME00
Is calculated automaticallyi=IDIM
j=j1

i=i1
mx
my

j=j2
lonW

i=i2

N60

latN

latS
lonE

do iy=2000,2010
loop for year
write(cy,'(i4.4)') iy write the year information in cy
open(22,file='NILE/GSMAP-Nile-1mon'//cy//'.gad',form='unformatted',access='direct',recl=mx*my*4)

E360

S60

Use cy (year information) in
file name automatically

Step3: calculate climatological value and correction fact
(GSMAP/daily/climanal.f)parameter(latN=35.,latS=-5.,lonW=20.,lonE=45.)Set the analysis region

i1=int(lonW+180.)+1
i2=int(lonE+180.)
j1=int(latS+90.)+1
j2=int(latN+90.)mx=i2-i1+1
my=j2-j1+1

Step1: select the region for analysis (GSMaP)(GSMAP/daily/cutgsmap.f)

i=i1
mx
my

j=j2
j=j1
lonW

i=i2

N90

latN

latS
lonE

E180

S90

Use cy (year information) in
file name automaticallyComparison of monthly mean precipitation
Correction
factordo ii=1,mxloop for 1 degree data
do jj=1,myjj
i1=(ii-1)*10+1
i2=ii*10
1 degree grid box
j1=(jj-1)*10+1
j2
j2=jj*10bias(ii,jj,imon)=1.
avevar(ii,jj)=0.
j1
Initialization for averaging
icon2=0i1
i2
do i=i1,i2
loop for 0.1 degree data
do j=j1,j2
if(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
gridparameter(mx1=250,
my1=400) GSMaP grid
(GSMAP/hourly/chgres.f)
3 (im,jm+1) 4 (im+1,jm+1)
parameter(mx2=150, my2=240)

model griddo ii=1,mx2
data matching
im(ii)=nint((xlon2(ii)-lonW-0.5*res1)/res1)+1
enddo
do jj=1,my2
jm(jj)=nint((xlat2(jj)-latS-0.5*res1)/res1)+1
enddo

model grid1 (im,jm) 2 (im+1,jm)

GSMaP grid
dis1=(xlon2(ii)-xlon1(im(ii)))**2+(xlat2(jj)-xlat1(jm(jj)))**2
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)xlat1(jm(jj)+1))**2
www=1./dis1+1./dis2+1./dis3+1./dis4
weight(ii,jj,1)=1./(dis1*www)weight for interpolation
weight(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)

Step6: bias correction of GSMaP by GPCC
climatology(GSMAP/hourly/bcgsmap.f)
parameter(mx1=25, my1=40)
parameter(mx2=150, my2=240)

correction 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 i=1,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
loop for model griddo 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

reading correction factor

1 degree grid box

j2
j1
i1

i2
i

Step7: re-ordering the data for HydroBEAM (2D 
1D)(GSMAP/hourly/hbinput.f)
parameter(mx=150, my=240, nmax=9846)

do ii=1,nmax
loop for HB gridread(11,*) i1,i2,clon,clat,zzz
im(ii)=nint((clon-lonW-0.5*res)/res)+1
jm(ii)=nint((clat-latS-0.5*res)/res)+1
mask(im(ii),jm(ii))=1.
topo(im(ii),jm(ii))=zzz
enddo(im,jm)Finding the grid number (im,jm)
in 2-D dataset

Nile River Basin average rainfall (mm)
160
140
120

JRA

GSMaP

GPCCmoni

GPCCfullAt this moment, we cannot say which product isbetter.
Quality of input rainfall data should be evaluated by
1. Collecting more rain gauge data(some rain gauge information are not opened toresearch community)

100
80
60
40
20

Jul‐02Oct‐02Jan‐02

JRA: JRA reanalysis (Prof.Sato)
GSMaP: GSMaP (raw data)
GPCCmoni: GSMaP corrected by GPCC monitoringproduct
GPCCfull: GSMaP corrected by GPCC full analysis

Apr‐02

Jul‐01

Oct‐01

Jan‐01

Apr‐01

Jul‐00

Oct‐00Jan‐00

Apr‐00Jul‐99Oct‐99Jan‐99Apr‐99Jul‐98Oct‐98Jan‐98

Apr‐980

2. Collecting river discharge (dam inflow) data
(check water balance of each sub-catchment)
Quality of input rainfall data is the base of water
resources analysis. In the area where
evapotranspiration
is dominant in water balance component, just a smallExample in Chao-Phraya river(Thailand)

GrADSThe Grid Analysis and Display System (GrADS)
Free Software to display 2 Dimensional data

Bhumibol

Wang (W4A)

Yom (Y6)

Product A
Prec

Evap

NakhonSawan

1005

Bhumibol989

Sirikit1086

InstallSirikitActivate :grads-2.0.1.oga.1-win32_superpack.exe

Product B

Runoff

Obs

Prec

Evap

Runoff

Obs

892

113

182

1144

949

195

182

884

105

180

1101

933

168

180

977

109

322

1246

1037

209

322

Y.6 (Yom)

992

884

108

184

1164

962

202

184

W.4A (Wang)

935

832

103

88

1101

912

189

88

This installer is included in USB flash
memory(GSMAP/grads/)
 Source : http://sourceforge.net/projects/opengrads/files/
grads2: OpenGrADS Bundle Distribution(Windows/Mac/Linux/Unix)

10% difference in rainfall results in 50 – 100% difference in runo

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/
3. Activate: Grads
・ Grads・ yes

Data description file (ctl file)
Ex. BC-GSMAP-Nile-20km-1hr200003.ctl
DSET ^BC-GSMAP-Nile-20km-1hr200003.gad
Data file name
TITLE bias corrected GSMaP 20km hourly
Y axisUNDEF -9999.9 Undefined value(Latitude)
OPTIONS LITTLE_ENDIAN
XDEF = 150
XDEF 150 LINEAR 20.05 0.166667
XMIN =YDEF 240 LINEAR -4.95 0.166667
20.05
zdef 1 levels 1000DX =tdef 744 linear 00:00z01mar2000 1hr
0.166667
VARS 1 Number of variables
X axisrain 0 99 hourly precip(mm/hr)(longitude)
ENDVARS
Variable nameTdef = 744(24 hours x 31 days)
Start from 2000 Mar 1

DATA

T axis (time)Exercise 3: River Basin Modelling
Exercise 4: Impact Assessment by Hydrological Model
Yoshinobu SATO (Water Resources Research Center, DPRI, Kyoto University)
In this exercise 3 and 4, you will learn how to operate a distributed hydrological model(Hydro-BEAM) for the impact assessment on flood disasters, water resources management and the river
ecosystems under changing climate. At first, we have to create a river basin model as an input variable forthe hydrological mode using a river basin modeling tools. We will use elevation, basin boundary, channel
position and land use dataset. Then, we will prepare the precipitation dataset for the input data for the
runoff simulation. In the data preparation, the inverse distance weighting (IDW) method will be used fordata interpolation. By using several data preparation tools, we can run a distributed hydrological model
called Hydro-BEAM (Hydrological river Basin Environment Assessment Model) applied for the Yodo
River basin.
All the dataset and the program source code for the analysis will be provided by the USB memory
stick. All the program source codes are written by 'gFortran (.f90)'. So, you have to install gFortrancompiler in your PC. The compatibility of other Fortran compiler (such as g77) will not support in thisexercise except for the TDM-GCC 64bit version (tdm64-gcc-4.8.1) gfortran. Thus, all the participants whowant to use the River basin modeling tools, the data preparation tools, and the distributed hydrologicalmodel (Hydro-BEAM), are requested to bring your own windows based PC (Windows 7 is recommended)with gfortran compiler is installed. (Microsoft Excel is also used for output data analysis)
The free gfortran complier can be obtained from "http://tdm-gcc.tdragon.net/download".
In this exercise, we will create the Yodo River basin with the 1km resolution. For the runoff
analysis, we will use 114 rain gage station's data (hourly data) from 14 - 20 Sep, 2013 during the period of
Typhoon No.18. Then, we will run the Hydro-BEAM and check the simulation results with observation
data at several points of the Yodo River basin. Then, we will discuss about how to modify or improve oursimulation results through the parameter calibration. Furthermore, the results obtained in this exercise will
be linked with the Exercise 5 (Impact assessment by ecological model).

.

1.Create River Basin Cells
Ex. 3：River basin modeling

Y=240 cells

Lake Biwa

Yoshinobu Sato

Basin: 8121 cells

Kyoto

Water Resources Research Center
Disaster Prevention Research InstituteKyoto University

Uji
Osaka

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

Yodo River basin

Total: 160 x 240= 38400 cells
X=160 cells

Export data as “Basin.csv”

2.Select Major Channel Cells

3.Preparation Elevation data

Average

Lake Biwa
Katsura River

20
data

400
data
LowestUji River

0: Basin
1: Cannel

20
data

Kizu River

Export data as “Channel.csv”

4.Preparation of Slope data

5. Land use type data

Elevation
Distance
Slope gradient = Elev.(m)/Dist.(m)20Slope1 i 1Water body
Forest area

H i  Lidi20
Paddy field20


Slope 2 j 1

H j  Lj
dj
20

Urban area

6. River Basin Modeling

7. Data Format (1)Input Data
Output Data

BASIN.csv

Program(x,y)=(1,240)
Mesh_list

CHANNEL.csv

BASIN.csv, CHANNEL.csv
0: Outside
1: Inside

GEO.f90
DEM.txt(x,y)=(160,240)

0: Basin
1: Cannel

GEO

Landuse.txt(x,y)=(1,1)

9. Coordination System

8. Data Format (2)DEM.txt(x,y)=(1,240)

Landuse.txt

Forest:1
Paddy:2
Grass:3
Urban:4
Water:5-999.90/0.0000:
Outside(x,y)=(160,1)(x,y)=(160,240)

240 cells
OriginStarting point

30sec
45sec

x
y

Slope1(m/m)

average(m)
Lowest(m)

Slope2(m/m)

xyLand useType

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

10. Execution Procedure 1

160 cells

11. Execution Procedure 2

Compile source code ‘GEO.f90’
By the “gfortran” (TDM-GCC)
Command prompt(cmd.exe)
geo.exe12. Execution Procedure 3

13. Data Format (3)Mesh_list.txt

Run(geo.exe)

Mesh ID.
Calculation Order

Total mesh size settings(x=160, y= 240)
Origin (N34, E135)

Mesh CodeXXX YYY

Upstream mesh
ID1, ID2, ID3

Mesh Resolution(Lat.=30sec, Lon.=45sec)

Channel Order

Upstream
Catchment Area (km2)

Elevation(m)River mouth position(x,y)=(34,82)
Latitude(deg.)

14. Data Format (4)Longitude(deg.)

15. Flow Direction Map

GEO.txt

Area
(km2)

Mesh ID.

Land use (ratio)
Forest, Paddy, Grass, Urban, Water

Mesh code
Downstream
Mesh ID.

River Channel
Gradient (m/m)

Downstream
Mesh code

Hill Slope
Gradient (m/m)

Elevation(m)

16. Channel Network• River channel runs through the mesh lowest
elevation• direction type: 4 direction• flow direction: Steepest slope

35.6

35.4

Latitude (deg)17. Summary (Basin Modeling)

35.2• Follow the actual river channel position

35

34.8

• Depression is corrected automatically bychanging mesh elevation(at least 1m higher than the adjacent mesh)

34.6

135.4

135.6

135.8

136

Longitude (deg)

136.2

136.4

Ex. 4：Impact Assessment byhydrological model

BASIN

CHANNELDEM

Landuse1.

Flow chart
Station-list
.csv

3.
GEO

AMD
Mesh
Data

station-dist

Water Resources Research Center
Disaster Prevention Research InstituteKyoto University

mesh_list

5. HBM-INPUT

2.
ST-ID

6.

GEO

HBM

1

2
3
Hiyoshi : 5441
Amagase: 2732
Takayama: 1612
Takayama
DAM

Practice• Obtain mesh ID of the Amagase andTakayama Dam using ST-ID.
• Amagase Dam
Lat:34/52/47, Lon:135/49/04, Alt: 58m• Takatama Dam
Lat:34/45/11, Lon: 136/02/00, Alt: 104m

8.

Input Data

ST-ID.f90/exe

Mesh_list.txt
Hiyoshi
DAM

MET

MET
Data

METup

HBM

ST-ID (Station ID)

7.

METbasin

IDW

station-list

Amagase
DAM

DATA

4.

StinfoYoshinobu Sato

Observation
Point Data

4
5
6
7

1. Run st-id.exe
2. Input station name“hiyoshi”
3. Input latitude“35/8/54”(d:m:s)
4.Input longitude“135/31/11”(d:m:s)
5.Input elevation“164.4”(m)6. Mesh ID
5441
7. Catchment Area
282 km2

Data preparation
Input data:

Precipitation (mm/h)
Each observation
Point Data

1km Mesh DataDATA: Precipitation (mm/h)

Station-list.csv

Station ID

Station ID code (1001)(See Station-list.csv)¥DATA

Precipitation(mm/h)
From:
2013.9.14
To:
2013.9.20(7 days)

StationID codeLatitudeLongitude(Degree, Minute, Second)(Degree, Minute, Second)

Altitude(m)

In this practice we will use 144 observation points data.

Stinfo(Station Information)

Stinfo.f90/exe

Output Data

Input Data
Programstation_list

mesh_list

Stinfo.f90

station_dist

1. Activate command prompt
stinfo*Station-list.csv

2. Compile: Stinfo.f90“gfortran Stinfo.f90 –o stinfo”*Not used

Yodo River basin: 8212 mesh

3. Run “stinfo.exe”“stinfo”

Approx. 1 min.

Data interpolation method
Inverse Distance Weighting (IDW)

Station_list/dist.txt
station_list.txt

mt1

station_dist.txt

L1
mt

mt3

Far
Near

Mesh code

Far

Station ID

Mesh ID
Distance (km)

Xi=(Ni+hi)・cosi・cosi，
Yi= (Ni+hi)・cosi・sini，
Zi=(Ni・(1-e2)+hi)・sini

L2
n

L3

Near

Li  ( X2  X1 )2 (Y2 Y1 )2 ( Z2  Z1 )2

mt2

mt 

mt i

L

2

i 1

i

n

1

i 1

i

L

Ni=R/√(1-e2・sini)

i：Latitude
i：Longitude

2

Eccentricity：e=0.08181974
Equatorial radius：R=6,378,136m

IDW

IDW.f90/exe
1. How many days?
“7” (days)
2. How many stations?
“3”(stations are used for IDW)

Output Data
Input Data
Mesh code

Programxxxyyy.dat
8212 files

mesh_list

1
2

IDW

Station-list.csv

AMD
Mesh
Data

station_list
Observation
Point Data

DATA

station_dist

Approx.
2~5 min.

xxxx.txt Station ID code
114 files

Yodo River basin: 8212 mesh

Data format (xxxyyy.dat)

HBM-INPUT
Output Data

25 data

Input Data

7 lines

Program

Day1, data1,data2,….,data24 (Prec.: mm/h)
Day2, …
Day3, …
…
¥AMD
8212 files
Day7, …

HBM-INPUT/exe

Mesh code

HBM-INPUTAMD

HBM

xxxyyy.dat
8212 files
Mesh
Data

xxxyyy.dat Mesh
8212 files Data

upper-list.csv

Data format (HBM-INPUT.dat)
1.(Ta,Ts,Snm,E,data1,data2,…data24,PR,P
RR,PRS) x 8212 mesh
31 data x 8212 mesh = 254,572 data / day2.(… )
x 7 days = 1,782,004 data…
*Ta: Air temperature (deg C)
7. (… ) *Ts: Land Surface temperature (deg C)

12

1. Input year?
“2013”
2. How many days?
“7”

HBM-INPUT
2013.dat

mesh_list

Approx.
1~2 min.

*Snm: Snow melt (mm/d)*E: Evapotranspiration (mm/d)
Data1~2: Precipitation (mm/h)*PR: Precipitation (mm/d)*PRR: Rainfall (mm/d), *PRS: Snowfall (mm/d)*Not used

24 hours x 7 days = 168 lines

Data format (xxxyyy.dat)

Data format (upper-list.csv)

5 dataYear,day,PRup,PRRup,PRSup
Upper catchment average
PRup: Precipitation (mm/h)*PRRup: Rainfall (mm/h),*PRSup: Snowfall (mm/h)¥HBM
8212 files

mesh ID

Mesh ID*Not used

Catchment
Area (km2)

Total Numberof meshThis data is used for METupThis data is used for METup

HBM (Hydro-BEAM)

HBM.f90/exe
1. River Name: “yodo”
2. Year: “2013”
3. Days: “7” (days)
4. Mesh ID: “5441” (hiyoshi dam)
5. Output: “default” (version)

Input Data
Program1

Output Datamesh_list

2
3
4

HBM

GEO

Ver.

Parameter Settings
・Soil Thickness [m]0.14
・Porosity [%]40・Hydraulic Conductivity [m/s]
0.0001
・Runoff ratio 1, 2
0.25/0.6
・Canopy interception [m] 0.001

5Mesh ID
HBM-INPUT
2013.dat

QRH-000xxxx2013yodo.csv

Approx.
1 min.

6

Hydro-BEAM

Basic Structure

Hydrological river Basin Environment Assessment Model
Cell concentrated type model
2 Hill slope & 1 Channel

ＡＡ layer
layer

Kinematic wave modelRiver Routing / Channel Network
Input rain(= net rainfall + snowmelt)

h q

r
t x

q  f h    ( h  d )m  ahh  hs  ha

d
ha

hs
ha

layer
ＢＢ layerSurface flow
Subsurface flowlayer
ＣＣ layer
Ｄ layer

Ｄ layer

ＡＡ layer
layer
layer
ＢＢ layer

Infiltration

layer
ＣＣ layer
Ｄ layer

Ｄ layerS

k1
k2

Subsurface runoff
Infiltration

Multi-layer
Storage function modeldS I Odt

O  (k1  k 2 )  S

Hill Slope Runoff Process

Rainfall Interception
Gross rainfall

Surface runoff

Net Precipitation

qs  hsm

Subsurface runoffqa  ahaini: slope gradientn: surface roughness
K: hydraulic conductivity
θ: volumetric water content

5 4m  or
3 3

Water
Urban
Forest
Grass
nc
nf
Paddy
ng

Interception lossa  K  inw

Net rainfall

Surface runoff
Subsurface runoff

Gross Rainfall

Net Rainfall

Potential Evaporation

Groundwaterrunoff

Interception
storage capacity

Linear Storage Function Model

START

Flow Chart (HBM)

Parameter SettingdS I Odt

Input-1

Input data-1 (GEO)
Mesh ID, flow direction, land use, slope

BSNUP

Calculation order (Channel Network)

Input-2Input data-2 (Meteorological data)
Precipitation

Time step loop Start

O  (k1  k2 )  S

Mesh loop Start= ratio 1

S : warter storage [m]I : inflow [m/s]=(1-f)・r
O : outflow [m/s]k1 , k2 : coefficient

Hill slope= 0.3 (fixed)

Kinematic wave modelfor hill slope

A-Layer
B-Layer

Storage function model

C-Layer

Kinematic wave model
for river channel

River Channel

k3 = ratio 2

OutputInitial setting value

Output mesh data (river discharge)

Mesh loop End

B layer: KH=0.14/86400
B layer: KV=0.3/86400
C layer: KH=0.6/86400

Time step loop End
END

Output Data Sample

Output data
¥default

Hiyoshi Dam

Discharge (m3/s)

2000

year, day, hour

1000

500

0

River discharge(m3/s)

QRH-0005441-2013yodo.csv

1500

2013.9.14 ~ 9.20

1

25

49

73

hour

97

121

145

Reservoir Operation(Flood Control)

Observed Data

Hiyoshi Dam

Observed-Data.xls
Hiyoshi dam
Amagase dam
Takayama dam
2013.9.14~ 2013.9.20 (7days)

Discharge (m3/s)

HBM

Inflow

Outflow

Storage
70000

2000

60000
1500

50000
40000

1000

30000
20000

500

10000
0

date, hour01Storagex1000 m3

Inflow(m3/s)

25

Outflow(m3/s)

49

73

97

121

145

hour

Input Data Analysis 1(METbasin.f90/exe)

Practice• Calculate inflow to Amagase andTakayama dam using HBM.
• Compare these results with observed data.
• Discuss about the reason why thesedifferences occurs.
• Try to improve calculation results bychanging parameters (calibration).

Input Data
Program
Output Datamesh_list

METbasinAMD

MET

grid-AMDxxxyyy.dat Mesh
8212 files Data

Approx.
1 min.

Input Data Analysis 2(METup.f90/exe)

grid-AMD.txt
Input Data

Program
Output Datamesh_list

METup
HBM

LatitudeLongitude
Mesh code

Total Precipitation(mm/7days)

Daily Precipitation (mm/day)
Day1,Day2,….,Day7

xxxyyy.dat Mesh
8212 files Data

METxxxxx-day.csv

Approx.
1 sec.

Mesh ID

Reservoir Water Storage(x 1000m3)

Observed

05441-day.csv

Practice•*Not used

Rain*, Snow*

day,hourPrecipitation dataof the upstream ofmesh ID(5441).

Compare the flow characteristics of main tributaries of
the Yodo river basin.
(1.Katsura R., 2. Uji R., 3. Kizu R., 4. Yodo R)

1. Katsura River (Hazukashi)
N34/55/40, E135/43/47, 9.234m
2. Uji River (Yodo)
N34/55/0, E135/45/30, 11.017m
3. Kizu River (Yawata)N34/52/59, E135/42/25, 11.335m
4. Yodo River (Hirakata)N34/48/49, E135/38/04, 6.868m

1

2

4
3

Precipitation (mm/h)

Result 1

Result 2• Mesh ID and Area? Use “ST-ID”
1. Katsura River (Hazukashi)?

• River discharge (average, peak)? Use
“HBM”

A.

1. Katsura River (Hazukashi)?

A.

2. Uji River (Yodo)?

A.

3. Kizu River (Yawata)?

A.

4. Yodo River (Hirakata)?

2. Uji River (Yodo)?
3. Kizu River (Yawata)?
4. Yodo River (Hirakata)?

Result 3

A.
A.
A.
A.

Discussion• Precipitation (Total, Peak)? Use “METup”
1. Katsura River (Hazukashi)?
A.
2. Uji River (Yodo)?

• Compare the result of each point.
• Discuss about the reason why thesedifferences occurs.

A.
3. Kizu River (Yawata)?
A.
4. Yodo River (Hirakata)?
A.

• Discuss about how to mitigate flood
disaster, increase available water
resources, and improve environment forriver ecosystem of the Yodo River basin.

Exercise 5: Impact Assessment by Ecological Model
Sohei KOBAYASHI (Water Resources Research Center, DPRI, Kyoto University)
River ecosystems usually consist of diverse plant and animal species within a site, and all of thespecies are more or less interconnected to create a complex food web. Interactions between different
organisms and between organisms and environment always affect the community composition, primary andsecondary production, and water quality. Future climate change will surely impact river ecosystems andavailability of ecosystem resources for humans through hydrologic and hydraulic processes, thoughecological processes are complex and difficult to make predictions for future changes.
In this exercise, I firstly explained about the basic concept of an ecological model, named"compartment model". The model consists of several compartments, each of which represents a group of
species or elements (e.g., nutrient, organic matter) with similar ecological status. Basically, all of thepossible connections (ecological processes) between compartments and influences of various abiotic factors(e.g., light, temperature, flow) to each of the processes should be considered. So a large number of reaction
formula and parameter are usually required. At least, a basic structure of formula should be understood tosolve an ecological phenomenon for each particular case. Depending on focus topic, all of thecompartments are not always required in the model.
There are some ideas to make use of the output results of hydrological model (i.e., discharge, watertemperature) in an ecological model. Some methods to relate discharge values to habitat availability ofaquatic species are already familiar. Discharge is also an index of disturbance that reduces the size of
population (density, biomass) and resets community members. Water temperature governs the rate of all
biological processes, and also affects the annual cycle and the timing of important life history events, all ofwhich can ultimately influence population persistence, population size and function of species.
We will finally try to simulate the response of an aquatic species to change in hydrological andtemperature regimes by making use of the output data of Hydro-BEAM. The target species is a caddisfly,
whose outbreak is now a serious issue in the Uji City, Kyoto. The number of emergence of caddisfly is
assumed to increase if water temperature increased, while assumed to decrease if the number of floodincreased. Simple assumptions are considered in the model and Microsoft Excel will be used for the
calculation.

23rd IHP Training Course

12th Dec. 2013

Aquatic ecosystems support diverse plant and animal species.
All of them are more or less interconnected.
Top predator fish

Exercise 5:
Impact assessment by
ecological model

A typical food web structure in streams/rivers

Predator invertebrates

Predators
Consumer invertebrates

Consumers

Sohei KOBAYASHI(Disaster Prevention Research Institute, Kyoto Univ.)

Primary producers(Algae)Detritus(dead leaves)speciesPrimary producers and detritus

Trophicconnection

Allan & Castillo (2006) Stream Ecology

Interactions of organisms‐organisms and organisms‐water
always affect the community composition, production andaquatic environment (water chemistry, etc.).
Resource production

Future climate changes will surely impact aquatic ecosystems
through hydrological and hydraulic processes, butecological processes are so complicated and difficult to predict

Water clarity & color (= nutrient & organic concentration)

Sediment/nutrient
supplyDrought

Heating/coolingRiver food web

Invasion of alien species, extinction of native species
Outbreaks of undesirable plants/animals

River food web

Alien fish
Alien fishRiverecosystems

Alien clam
誰でもわかる外来種対策（リバーフロントセンター,2012）

Flood

Alien plant

Need a clear/simple idea that connects hydrol and ecol processes
Substantial emergenceof caddisfly

Outline1. Basic ideas of ecological model
Compartment model
Shape of biological reaction
Previous case studies
2. Key processes between hydrology and ecology
3. Simulating ecological processes
My talk is mainly concerned with river ecosystems

1. Basic ideas of ecological modelBasic ideas of ecological model

Basic ideas of ecological model (compartment model)

Compartment model
Consisting of several compartments and theinteractions between the compartments thatsatisfies the material cycle of the target
phenomenon. Each compartment represents agroup of species (elements) with similar
ecological status.

Nutrient (N,P,)

DOM

Heterotroph

Primary producer POM

predation

microbeGrazer Detritivore
Predator

Lakes

Littoral zone

Detritivor

Primary
producerdiffusion, transportation

Death, fecespredation

consumption

deposition

Profundal
zone

POM
Heterotroph

microbeuptakeDOM
RiversNutrient

Downstream

UpstreamSpecifying the processes of interaction

diffusiontransportation

uptake, predation, respiration,
decomposition, etc…

Pelagic zone

predation

GrazerExcretion, respiration

Frequently used compartments in aquatic
ecosystems are follows.

By connecting the similar structure,
a whole system with spatial heterogeneity
can be solved

Predatordiffusiontransportation

Model structure isdependent on theobjective.

Basic ideas of ecological model (compartment model)

Basic ideas of ecological model (compartment model)

Equation formulaLosspredation

The basic form of equation is similar among
compartments. The parameters are different.

excretion
respiration

Primary
producerdeath

Growth limitation function

predation

by resource (e.g., nutrient, food)excretion
respiration

death
feces

Grazerf(n)uptake(growth)Accrual(gain)

consumption(growth)1

Monod typePrimary producer

Grazer

Accrual

dX p
dt



0.5
AccrualLoss  p  r p   gp  g X g  k p  X p

dX g
dt

n
fn 
Kn  nLoss

Half‐saturation constant  g  r g   tg  t X t  k g  X g
0Kn

 g   g max f p f gT f X g

 p   p max f n f I f pT f X p
Maximum
growth rate

Nutrient concentration
Food density/biomass

Maximum
growth rate

Growth limitation function
by nutrient (n), light (I),temperature (pT),
its own density/biomass (Xp)

Growth limitation function
by primary producer (p),temperature (gT),
its own density/biomass (Xg)

Basic ideas of ecological model (compartment model)

Basic ideas of ecological model (compartment model)

Growth limitation function

Growth limitation function

by light

by temperature
f(I)

f(T)

1

1


T opt    T 
T  T 
f T   opt
 exp    








 I 


 exp  1  I 
fI  
 I

I opt  opt 

Optimum temperature

Optimum light intensity
0

Iopt

0
Light intensity (I)

Topt

Temperature (T)Basic ideas of ecological model (compartment model)

Growth limitation function
by its own concentration/density/biomass
f(X)
1

fT 

1
1  K inv X
Inverse half‐saturation

0.5

fT  1 
0
1/Kinv

2. Key processes
between hydrology and ecology

X
KX
Carrying capacity

Concentration/density/biomass(X)The key processes between hydrology and ecology

The key processes between hydrology and ecology

Concept of PHABSIM & WUA
Simulating flow and depth at various discharges

Make use output values of hydrological model

100 m3/s

Depth (m)Discharge

1.0

Velocity
1 m s-1

50 m3/s(water level, flow velocity, etc)
0.5Frequency / Spatial distributionof flow velocity and depth=
Potential habitat availability
for a plant/animal species

100 m

0

Based on field research
making preference curve
For target species

WUA

Depth

Velocity

Discharge

Discharge

Frequency / magnitudeof flood or drought=

Temperature

Disturbance that increase mortality
and reset community succession

Biomass orcommunity status

Ecological processes(water level, flow velocity, etc)

Affects carrying capacity of species

The key processes between hydrology and ecology

Growth rates
Activity rates

Temperature and biological activity

Temperature

Temperature

Rates of biological processes(growth, mortality, etc.,,)Timings of life history events

Development

adult
pupae

Rates of development(length of life‐cycle)

Cooler condition

3. Simulating ecological processes

larvae
egg
adult
pupae

Warmer condition

Time of a year

larvaeegg
Time of a year

Growth and survival of individuals
Persistence of populations

Temperature

Growth/develop period
Warmeryear
Cooleryear

Trigger temperatureof a life event
Time of a year

Averagevalue

Averagevalue

Simulating ecological processes

Simulating ecological processes

Uji River and City

Ecological values in Uji River

The river flows the middle of the Uji city and is one of the major rivers of Yodo basin.
The city has 2 world heritage buildings (Byodo‐in, Ujigami, since 10th century) near theriver, and long & various histories of rivers and people exist.
Uji is the city of mountains and rivers as well as Japanese tea. Better use of riverresources and pleasant environment is expected also in the future.

Due to locating downstream of the Biwa Lake, many species have been evolved to adapt
specific flow environments of the Uji River. Some animal species are known to occur
only in the Uji River (or are abundant in Uji River). Preservations of these species are
surely important for the nature and citizens.

DPRI, Kyoto Univ

Uji City
Trumpet‐shaped‐net caddisfly
宇治市ホームページ
http://www.city.uji.kyoto.jp

Ujigami shrine

Byodo‐in temple

Pale Chub abundant in Uji River

Endemic freshwater snail

Simulating ecological processes

Simulating ecological processes

Environmental issues in Uji River
Channel degradation is a common problem in Japanese rivers. In the Uji River, a large
reed vegetation area is endangered due to a decreased river‐floodplain connection, andspawning areas of some fish have been lost due to bed coarsening.
The outbreak of caddisflies is an another big issue. They have increased due to bed
stabilization and rich supply of phytoplankton from the upstream Amagase dam.
Although they are harmless, their crowd make residence and tourists uncomfortable.

1957

Amagase dam

Bed grain size distribution
Cumulative
percentage

Bed elevation (m)Longitudinal bed elevation profile

Simulating the growth and emergence of caddisflies
and predicting the effect of a climate change

Uji riverdownstream of dam

1964
2009

Invasion of Exotic
Golden Mussel

Grain size (mm)2007

Emergence of
caddisfly

Distance from the Yodo river mouth (km)Growth and productionof phytoplankton

Outbreak of caddisfly

Growth of
caddisfly
Reduced reed field on floodplain

Decrease of Pale Chub Zacco platypus

Transportof phytoplankton

Exercise 6: Optimization of Reservoir Operation
Daisuke NOHARA (Water Resources Research Center, DPRI, Kyoto University)
Optimizing reservoir operation policies considering hydrological data is crucially important foreffective management of reservoirs for both the flood control and water use purposes. In this exercise, wewill learn how to prepare and conduct optimization of reservoir operation by using dynamic programming(DP) techniques through tackling to sample problems. The exercise will consist 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 willpractice typical backward algorithm to estimate optimized release policies of the reservoir.
In the second part of the exercise, we will tackle to practical optimization problems considering asimplified 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 optimizationof the reservoir operation for water supply by use of deterministic DP and stochastic DP models. Throughtackling the problems, it will be introduced what we must prepare to set up the calculation for optimizationof reservoir operation, including: choosing numbers of levels to discretize time step and states of thereservoir; setting objective functions according to the objective to optimize the reservoir; setting constraints
for storage volume or release water defined by the physical constraints or regulations; preparation ofhydrological data and target release of the target reservoir; and setting the time horizon to optimize thewater release strategy. The optimization of operation of the target reservoir will be computed and bedemonstrated with a computer program developed for DP based optimization of reservoir operations.

CONTENTS
Introduction
General
Simple Example
Exercises
1. Optimization for water supply purpose with deterministic dynamic
programming
(DDP)
Japan
Sea
2. Optimization
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

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 Bellman’s Principle of Optimality:•2

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)rtstReservoir

Physical constraints of the reservoir:• Smin = 0, 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

SIMPLE EXAMPLESIMPLE EXAMPLE
et= 0

qt

et= 0

dt
qt

rt
st

Assessed pointst•

Assessed point
Reservoir

Objective function:3

No consideration of losses from the reservoir [i.e. 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 to:

Other settings:
•

dt
rt

Reservoir

Assumptions to simplify the problem:••

4• Smin = 0, Smax = 40, Smin ≤ st ≤ Smax
• Rmin = 0, Rmax = 40, Rmin ≤ rt ≤ Rmax
• st+1 = st + qt – rt – etDrought damage function:
H(rt) = {max[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 ) 

f(): Future damage functionr (t )

5

6

SIMPLE EXAMPLESIMPLE EXAMPLE

Calculation backward considering no damage after the last time step (t ≥ 4)
t=1

t=2

t=3

Calculation of f(3) for each storage 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)= 0Storage Level 2(10MCM)f(4)= 0Storage Level 1(0MCM)

t=2

t=3
f(3)= 0

t=4
400
100

f(4)= 0f(4)= 000f(4)= 0f(4)= 0f(4)= 0Storage Level 1(0MCM)

For Level 5 (s3=40MCM),

7SIMPLE EXAMPLE• r3=0• r3=10• r3=20• r3=30• r3=40

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 EXAMPLE

Calculation of f(3) for each storage state s3
t=1

t=2

Calculation of f(2) for each storage state s2
t=3

t=4

Storage Level 5(40 MCM)f(3)= 0f(4)= 0Storage Level 5(40 MCM)

t=1

Storage Level 4(30MCM)f(3)= 0f(4)= 0Storage Level 4(30MCM)

Storage Level 3(20MCM)f(3)= 0f(4)= 0Storage Level 2(10MCM)f(3)= 100f(4)= 0Storage Level 1(0MCM)

f(3)= 400f(4)= 0

t=2

t=3

t=4

f(3)= 0f(4)= 0f(3)= 0f(4)= 0f(3)= 0f(4)= 0Storage Level 2(10MCM)

400 f(3)= 100

f(4)= 0Storage Level 1(0MCM)

f(3)= 400f(4)= 0900
f(2)= 100

400
100

Storage Level 3(20MCM)

100

For Level 4 (s2=30MCM),

9SIMPLE EXAMPLE• r2=0• r2=10• r2=20• r2=30• r2=40

H(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 EXAMPLE

Calculation of f(2) for each storage state s2
t=1

t=2

Calculation of f(1) for each storage state s2
t=3

t=4

f(2)= 0f(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)= 500

f(3)= 100f(4)= 0Storage Level 1(0MCM)

f(2)= 800f(3)= 400f(4)= 0Storage Level 5(40 MCM)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(3)= 0f(4)= 0f(2)= 100f(3)= 0f(4)= 0f(2)= 200f(3)= 0f(4)= 0f(2)= 500

f(3)= 100f(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),

11• r1=0• r1=10• r1=20• r1=30• r1=40

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

12

SIMPLE EXAMPLESIMPLE EXAMPLE

Calculation of f(1) for each storage state s2

Getting optimal policies (decision and state trajectories) for eachstorage level at each time step

t=1

t=2

t=3

t=4

Storage Level 5(40 MCM)f(1)= 100f(2)= 0f(3)= 0f(4)= 0Storage Level 4(30MCM)f(1)= 200f(2)= 100f(3)= 0f(4)= 0

t=1

t=2

t=3

t=4

Storage Level 3(20MCM)f(1)= 300

f(2)= 200f(3)= 0f(4)= 0Storage Level 5(40 MCM)f(1)= 100f(2)= 0f(3)= 0f(4)= 0Storage Level 2(10MCM)f(1)= 600

f(2)= 500

f(3)= 100f(4)= 0Storage Level 4(30MCM)f(1)= 200f(2)= 100f(3)= 0f(4)= 0Storage Level 1(0MCM)

f(1)= 900

f(2)= 800f(3)= 400f(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(3)= 400f(4)= 0If you want to know the optimal release policy for storage level 3 at time step 1…

13

EXERCISES

EXERCISE 1: OPTIMIZATION FOR WATER SUPPLY (1)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
Purposes

Storage capacity(Oct. 11th ‐ Jun. 30th)(Jul. 1st ‐ Oct. 10th)

Water supply

173 MCM173 MCM

Flood control

80 MCM

90 MCM

Power gen.

36 MCM

26 MCMrt

The Yoshino
st
River

14

Dry season
Assessed point

SameuraReservoir• 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 (173 MCM)
• Off‐line optimization (not online optimization)• Optimize with deterministic DP (DDP) for one year from January to
December

Wet season

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

Flow (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 al. (1990):20
10 (d  r ) 2
 t t dH (rt )  
t

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

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.
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
f(T|Strorage Lv. = L) = ??

…

f(T|Strorage Lv. = L‐1) = ??

…

StorageLev. l…

f(T|Strorage Lv. = l) = ??

…

…

…

…

StorageLev. 1

Constraints:

…

…

…

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 (1.73 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:et = 0 (No loss from the reservoir considered)

19

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
observed for 30 years• Consider only water supply operation with the storage capacity for that
purpose (173 MCM)
• Off‐line optimization (not online optimization)• Optimize with stochastic DP (SDP) for one year from January to
December

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(T+T*|Lv. = L) = 0…

f(T|Lv. = l)

…

…

…

…

…

…

…

StorageLev. 1f(T|Lv. = L‐1)…

…

…
StorageLev. lt=T+1
f(T|Lv. = L)

20…

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/s)

subject to:
300.0• Smin ≤ st ≤ Smax
• Rmin ≤ rt ≤ Rmax
• st+1 = st + qt – rt – et250.0

Recursive equation:

200.0

150.0

f ( st )  min E  H ( rt , qt )  ft 1 ( st )

100.0(Neglecting persistence in streamflow)r ( t ) qt

Drought damage function:
Same as the one employed in Exercise 1:

50.0

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)23

 (dt  rt ) 2dt
H (rt )  0( rt  d t )(rt  d t )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.
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, 680pp. (with contributions from J.R. Stedinger, J.P.M. Dijkman and 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 prediction (ESP) forecasts. Journal of Hydrology, 249, 113‐133.
Turgeon, A. (1980): Optimal operation of multireservoir power systems with stochastic
inflows, Water Resources Research, 16(2), 275‐283.
Tilmant, A. Vanclooster, M., Duckstein, L. and Persoons, E. (2002): Comparison of fuzzy andnonfuzzy optimal reservoir operation policies, Journal of Water Resources Planning andManagement, 128(6), 390‐398.
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Biwa and the Uji River
forTechnical excursion of IHPTraining Course on Dec 7(Sat) 2013
Edited by
Yasuhiro TAKEMON(DPRI, Kyoto Univ)

Amagase Dam

Time Schedule
Meeting place: in front of lecture room 1 at Obaku Plaza
9:20 Bus arrival
9:30 Departure: Bus travel via local road
10:00 Amagase Dam: walking on the dam
10:20 Departure: Bus travel via highway
11:00 Seta Barrage: Lecture by the engineer, Mr. Kitano(Government officer of the Lake Biwa)
12:00 Departure: Bus travel via local roa
12:40 Biwa Lake Museum: Lunch at Cafeteria of the Museum
13:30 Biwa Lake Museum: Lecture by the researcher, Dr. Nakai+ free time inside
15:30 Departure: Bus travel via highway
16:15 DPRI Open Laboratory: Watching the riverbed degradationin the Uji River
16:30 Departure: Bus travel via local road
17:00 Obaku Plaza: Breakup

Lake Biwa

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.

Hazard Map of
Lake Biwa Area

Setagawa weir (Nango‐Araizeki)
Weir and thecontrol office

Area of < 0.5m inundation
Area of 0.5‐1.0m inundation
Area of 1.0‐2.0m inundation
Area of 2.0‐5.0m inundation
Area of >5.0m inundation

Top of the Weir is used
as a road

The control room we will visit

Visit of the Lake Biwa Museum

Natural Resources Exploitation in Lake Biwa

Meeting place
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
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

You can see a lot of freshwater fishesof Lake Biwa in the aquarium

Mukaijima area of the Uji River
Severe riverbed degradation has been occurring derived fromsediment discontinuity by the Amagase Dam and sedimentdredging in the Yodo River.

Sedimentation of Amagase‐dam

2. Construction and operation of dams in Uji River

1. Upstream watershed and sediment yield
Kisenyama dam(H=91m, V=7.2×106m3)

to Yodo River

Seta River is the outlet of Lake Biwa.
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 the first concrete‐gravity dam in Japan.
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 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 toincreasing sedimentation of Amagase‐dam.

Height: 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 andKisenyama dams.
Power generation of
Amagase dam Kisenyama Day time

Sabo works for bothhillslopes and streams

flowhydropower
stationCross section of
Amagase dam

Kisenyama

Amagasehydropower
stationNight timeflow
AmagasePresent mountains

Actual Sedimentvolume (103m3)

Annual sedimentation record

Discharge(m3/s)

4. Dam and environmental issues in Uji River
Downstream area of Amagase dam

Annual maximum discharge

Cumulative active sediment volume
Annual sedimentation volume
1967

1980

Total number of Check dams

Total area of hillside work (ha)Progress of Sabo works

1990

2000

2009

Amagasedam

Present
Total
Sedimentation
volume= 4.19×106 m3
Sedimentation
rate in planned
sedimentation
volume= 70%

Sedimentation rate was greater in
1960s and 70s. Hillside work andcheck dam construction have been
intensively implemented in 1890‐
1970 and 1950‐2000 respectively.
These Sabo works have drasticallyreduced sediment yield from Daido
River to Amagase dam.

46.0k site
1964
2009
Grain size (mm)Bed elevation (m)Planned
Total
Sedimentation
volume= 6×106 m3

Bed grain size distribution
Cumulative
percentage

3. Reservoir sedimentation in 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. Bed materials havebeen coarsened.

Distance from the Yodo river mouth (km)Environmental issues and future works
Outbreak of caddisfly
Supply ofplankton‐rich water

Almost 70% of planned sedimentation
volume has been already filled.
Capacity loss of total storage is 16%.

Stable bed material

Invasion of Exotic
Golden Mussel

1948

Reduced reed field on floodplain
2009

Less sandy bars

Most sediment deposit can be observed in
the downstream of submerged O‐minedam, where deposit depth is almost 20 m.

Deposited materials change at submerged
O‐mine dam, coarse sediment in the
upstream, while fine one in the downstream.

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.