International
Hydrological
Programme

International Hydrological Programme

Coastal Vulnerability and Freshwater Discharge
The Twenty-six IHP Training Course
27 November - 10 December, 2016
Nagoya, Japan

Institute for Space-Earth Environmental Research, Nagoya University

Supported byOutline
A short training course “Coastal Vulnerability and Freshwater Discharge” will be
programmed for participants from Asia-Pacific regions as a part of the Japanese contribution to
the International Hydrological Program (IHP). The course is composed of a series of lecturesand practice sessions.

Objectives
Large number of population is living in coastal area of Asian countries. The area isalso important for various human activities including fisheries, transportation, farming, andmany other industries. The population explosion of the coastal area often makes pollution ofwaters, both fresh and salt waters, inducing environmental problems in the area. Freshwater
input to the coastal area modified the circulation of waters. Large amount of materials are
known to be discharged to the coastal water with the freshwater as natural, and they played
important roles to keep the coastal ecosystem; however, the pollution of the freshwater also
alternate the coastal ecosystem. River is known as a major source of freshwater, and more
recently importance of underground discharge has been also recognized. Those freshwater
discharges are also changing significantly by the climate change, construction of dams on theriver, and use of freshwater. Coastal shallow area is often destructed to make a land forfarming, industry or living area with reclamation and other human activities. Recently, it was
shown that those coastal areas are vulnerable for tsunami caused by earthquake and storm surge
caused by typhoon, and radical changes can be happened by those natural hazards. It isnecessary to manage the area to make comfortable, productive and safe.
In this training course, the basic knowledge of physical, biological and chemical
environments of coastal waters, and forcing including freshwaters from river and underground
discharge, will be covered. Furthermore, interaction between nature of coastal area and humanwill be discussed. Technical training on-board of Training Vessel Seisui-Maru, Mie University,
will cover the basic technics to sample waters, analyze the quality and interpret the data in large
estuarine Ise and Mikawa Bay. Demonstration of satellite and numerical models will be also
covered.

1

Key Note (Tentative)
K1: Satoumi Concept

YANAGI T.

K2: Melting Tibetan Ice Shield

CHEN A.

Lectures (Tentative)
L1: River Discharge

TANAKA K.

L2: Submarine Ground Water Discharge

TANIGUCHI M.

L3: Coastal Water Circulation

KASAI A.

L4: Nutrient Dynamics

UMEZAWA Y.

L5: Plankton Ecosystem

ISHIZAKA J.

L6: Influence to Fisheries

ISHIKAWA S.

L7: Tsunami and Disaster Prevention

TOMITA, T.

L8: Tidal Flat Conservation

YAMASHITA H.

Exercise
E1: Satellite Data Analysis

TERAUCHI G.

E2: Cruise Data Analysis

ISHIZAKA J.

E3: Coastal Model Output Analysis

AIKI H.

Field Workshop and Exercise
W1: Cruise in Ise Bay by T/V Seisui-Maru, Mie University

2

ISHIZAKA J., AIKI, H., and MINO Y.

Schedule (27 November to 10 December, 2016)
27 (Sunday)

Arrival at Central Japan International Airport and Move to Nagoya University

28 (Monday)

09：30-09：40

Registration & Guidance

09：40-12：10

Lecture 1 by TANAKA K.

13：30-16：00

Lecture 2 by TANIGUCHI M.

17：00-19：00

Welcome Party

09：30-12：00

Lecture 3 by KASAI A.

14：00-16：30

Keynote 1 by YANAGI T.

09：30-12：00

Lecture 4 by UMEZAWA Y.

14：00-16：30

Keynote 2 by CHEN A.

29 (Tuesday)

30 (Wednesday)(Move to Mie)1 (Thursday)

Cruise in Ise/Mikawa Bay

2 (Friday)

Cruise in Ise/Mikawa Bay

3 (Saturday)

Tour to Ise Shrine (Back to Nagoya)

4 (Sunday)Off

5 (Monday)

09：30-12：00

Lecture 5 by ISHIZAKA J.

13：30-17：00

Exercise 1 by TERAUCHI G.

09：30-12：00

Lecture 6 by ISHIKAWA S.

13：30-16：00

Exercise 2 by ISHIZAKA J.

09：30-12：00

Lecture 7 by TOMITA, T.

13：30-17：00

Exercise 3 by AIKI H.

09：30-12：00

Lecture 8 by YAMASHITA H.

13：30-17：00

Making reports and discussions

09：30-11：30

Report presentations and discussions

11：30-12：00

Completion ceremony of this course

13：30-15：30

Farewell party

6 (Tuesday)

7 (Wednesday)

8 (Thursday)

9 (Friday)

10 (Saturday)

Departure from Central Japan International Airport3

4

K1: Concept and Practices of Satoumi in Japan and Lessons Learned
Tetsuo Yanagi (International EMECS Center, Kobe)

Abstract

The coastal seas in the world suffer from environmental problems such as
eutrophication, natural disaster, fish resources decreasing, environmental
degradation and so on. In order to solve such complicated problems, successful
Integrated Coastal Management (ICM) is necessary. Method of ICM in Japanese
SATOUMI (the coastal sea with high biodiversity and productivity under the humaninteraction such as the Seto Inland Sea, Shizukawa Bay and the Sea of Japan) is
introduced in this lecture.
ICM is an increasingly important topic for the public, scientist, policy makers andNPO related to environmental problems in the coastal sea. ICM is also a very rapidly
evolving field. Dissemination of ICM in Satoumi where high biodiversity andproductivity are realized under the human interaction is very useful for the peopleinterested in the environmental problems in the coastal seas of the world.
A new concept “Satoumi” was firstly proposed by Prof.T.Yanagi in 1998 and thefirst book “Sato-Umi” was published in 2006 and the second book “Japanese
Commons in the Coastal Seas: How the Satoumi Concept Harmonizes Human
Activity in Coastal Seas with High Productivity and Diversity” was published in 2010
with Springer.
This lecture will present the most advanced results on ICM in Satoumi. This
lecture would target graduate students and advanced college students as well as
stakeholders (such as policy makers and environmental organizations),
oceanographers and economists.

5

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2016/9/29

Concept and Practices
of Satoumi in Japan andLessons Learned
International EMECS Center
Professor Emeritus of Kyushu University

Tetsuo YANAGI

Satoyama and Satoumi
Satoyama : Forest with high productivity and biodiversity under the human interaction
Satoumi : Coastal sea with high productivity andbio-diversity under the humaninteraction
Yanagi (1998, 2006)

２００１

Claim of some ecologists

Satoyama ‐ Satoumi• Human interaction in
Satoyama may increase bio‐diversity,• but human interaction incoastal sea may decrease bio‐diversity

Eco‐tone

Deep Sea

SatoumiShallow sea
Sea grass bed
Tidal flats

Satoyama

High
Mountain

Eco‐tone

VillageCity

7

1

2016/9/29

Bio‐diversity and Human interaction

Example of increasing bio‐diversity under the humaninteraction

High bio‐diversity =Many kinds of habitat (nursery ground, feeding
place, spawning ground) andno‐climax (climax=simple habitat)

Tidal stone weir (Nagaki, Kachi in Okinawa)

1) Human interaction to arrange
habitat – High biodiversity2) Human interaction to stop thetransfer to climax of flora –High biodiversity

High water
Low water

Yanagi (2009） Human interaction and bio‐diversity. Ocenography in Japan,
18, 393‐398 (in Japanese)

Siraho Conservation Organization HP

Nagaki at Shiraho,
Ishigaki Island, Okinawa

Tawa ed. (2007)

Data of Kamimura
Species number
Spr.

Aut.

Spr.

2006 Autumn
Reconstruction

Aut.

Spr.

Aut.

Spr.

Aut.

Spr

Kamimura (2011)

Reconstructed by local people of Shiraho Village in 2006

Mitsukuchi Bay

Spot harvest in eel grass bed resulted in increasing bio-diversity

Seeds of eel grass
Few fish in the central part of eel grass bed

Fisheries in the eel grass bed
Many fish at the spot without eel grass
Many fish at the rim of eel grass bed

Fish roads

Suitable scale, formation and arrangement of eel grass bed

Tanimoto(2009)

Tanimoto (2012)82

2016/9/29

Field experiment of fish catch inside and outside of eel grass bed in Mitsukuchi Bay
2009/8/26～27
Outside (rim)：Stas. 1、2、5
inside：Stas. 3、4

2009/8/27 Mitukuchi Bay
134.267

Mitsukuchi Bay

34.28

34.26534.27534.2643
4

Tanimoto (2009)2
0 to 2
2 to 44 to 6
6 to 88 to 1010 to 1212 to 14

34.27

1
3

34.263

5

34.2610132.77132.775132.78

100

200

300

400

500

132.785

Too much interaction

数量
1
8
2
1
2
1
13
1
1
1

アマモ場内
inside8outside
20
15

6

4

2

0

1

Biodiversity and human interaction

魚種
メバル
コノシロ
コチ
サバ
キス
コイワシ
イシガニ
シャコ
ニシ
ヒトデ

outside5

outside

5
数量
1
2

10

10

inside

魚種
オコゼ
フグ

25

132.761 132.762 132.763 132.764 132.765 132.766 132.767

Gill net

Tanimoto (2009)

数量
1
2
3
2
1
3

12

species132.765132.76

4

魚種
メバル
フグ
コノシロ
ネコサメ
ハゼ
イシガニ

2009年8月27日

individual

132.76

3
数量
1
4
1
1
1
3
1
1
2
1

アマモ場内
inside34.26
132.757 132.758 132.759
132.755

魚種
ギザミ
メバル
コノシロ
コチ
タイ
ハゼ
オコゼ
タナゴ
イシガニ
ナマコ

30

34.26

34.255132.75

2
数量
1
3
1
1
1
1
1
5
2
1

35

5
34.2622

個体数

434.265

魚種
ギザミ
メバル
コノシロ
アイナメ
タイ
ハゼ
エソ
イシガニ
ウニ
ニシ

1

34.266

種類数

2009/8/4 Mitukuchi Bay (217ha)
34.285

2

3
測点

4

5

0
1

2

3
測点

4

5

Hinase Fishermen’s Union, Okayama

Tidal stone weir

Habitat creation
high
high
Seagrass cutting
biodiversity

low

Sta.22
Stop transfer to climax

No interaction

Too much interaction

No interaction

Decrease of eel grass beds
Decrease of fish catch
1940s

1960

eel grass bed
in 2011more than ２００ha

1985

Recovery to 1/3 ofeel grass bed in
1940s

Due to agricultural chemical
and increase of turbidity

Fishermen in Hinase
Fishermen’s Union
began the eel grass bed creationin 1985and continued until now

Fish catch by set net

93

2016/9/29

Oyster culture grounds

Oyster culture and eel grass bed
win‐win relation

Oyster culture was began in 1963 → expanded
in 1980s→ Okayama Oyster brand in 1996
鹿久居島ｂ
アマモ場再生区域ｂｃ• Decrease of water temperature in eel grass bed（due to leaves of eel
grass）→ decrease of mortality of oyster

幼稚仔
保育場

ａ

ｂ

ｄ
ｄ
ａ
ａ

ｂ
ｄ

ｂ
ｄ

ａ

• Attached diatom and small animal on the leaves of eel grass→ increase of growth rate of oyster

頭島ａａ
カキ養殖区画漁業権

ａ
ａ

カキ養殖
区画漁業権

鶴島

ｅ

ａ

ａ

；Oyster culture groundｂ；eel grass bed

ｃ；artificial reefｄ

；誘導滞留礁
；成魚生息場

ｅ

；消波施設

ｆ

大多府島

・ Decrease of wave height by oyster raft → decrease of eel‐grass root
damage・ Grazing of phytoplankton and detritus → Increase of transparency →Increase of eel‐grass beds area

＜凡例＞

ｆ
ａ

0

200

400

600

800

1000m

Fish stock enhancement

Recovery of sea bed litter andDirect selling of harvest

Fishery of Hinase Fishermen’s Union• Marine environment conservation： eel grass
bed creation, recovery of sea bed litters、Sea bed cultivation• Resources management：Release of juvenile、Days of prohibition of fishing• Added values：Direct selling of harvest、
Oyster baking restaurant, information from direct selling→Adjustment of fishing activity
Necessity of dissemination of fishermen’s activities：
Consumer may pay extra money for the marine environment conservation

Agreement for
Hinase Fisheries
May 2012

Fishery andMarine Ecosystem Conservation• Fishery is said to be the worst environment destroying
activity• It may result in conservation of marine ecosystem toharvest all levels biota in marine food chain

1)
2)
3)
4)

Fishermen’s Union
Okayama Prefecture
Okayama COOP
Research Institute for Satoumi
Creation

Galcia et al. (2012) Reconsidering the consequences of
selective fisheries. Science, 335, 1045‐1047
・ Hinase set net＝ Selective fishing• Cooking of all kinds of fish from small to large →Dissemination of Hinase culture – fishing and cooking

104

2016/9/29

Committee for management
Hinase Fishermen
Union

Local FishermenCommittee for ICM
Management
C
Rule making
Monitoring
Penalty

Chamber ofCommerce
Local Industry
Local people
University
NPO
Professional

Local government
Okayama Pref.
Bizen City

Terra Scientific Publishing
Company2007

Sato-Umi－A new concept for coastal sea
management－
1.Introduction
2.Mankind and coastal sea
2.1 Richness of the coastal sea
2.2 Crisis of the coastal sea
3. Mankind and the forest
3.1 Sato-Yama
4. Sato-Umi
4.1 Concept of Sato-Umi
4.2 Harvest of sea-glass bed
4.3 New technology
4.4 Stock enhancement and fishculture4.5 Sea farming
4.6 Fish resources management
5. Environmental ethics
5.1 Environmental ethics andCommons
5.2 Preservation and Conservation
5.3 Environmental education
6. Concluding remarks

Satoumi: Japanese Commons in the
Coastal Sea

Java Sea

12 examples of satoumi creation by Japanesefishermen and international activities
on Satoumi creation are introduced

Seribu Island

Java Sea

Jakarta Bay

Northern Coast
Karawang
published in 2012 from Springer

Brackish water
Pond

Sato Umi Session-EMECS9 Conference –Baltimore-28/8-2011

28

Consulting Activities in 2008

Aquaculture pond

Sato Umi Session-EMECS9 Conference –Baltimore-28/8-2011

11

30

5

2016/9/29

ZONATION MODEL OF THE DIVERSITY PRODUCT of GAPURAAND ENVIRONMENTAL SITUATION

Integrated Multi Trophic AquacultureJava Sea

Rice Field
Tilapia

Tilapia

Tilapia/Cat Fish
Shrimp‐Sea Weed
Milk Fish

Sea cucumber

ShrimpSea grass
Ｍａｎｇｒｏｖｅ

ChannelShrimpIrrigation/
ChannelSea
Bivalve

CoastalEnvironment

Pond

Gracilaria

Sea weed

Zonation Model

coast

Pond

ExperimentJun.‐Sep., 2010Mangrove
Zero emission aquaculture

Mangrove Plant

Green
MuscleSato Umi Session-EMECS9 Conference –Baltimore-28/8-2011

EXPERIMENTAL DESIGNINTEGRATED MULTI-TROPIC AQUACULTURE (IMTA)
Bio-recycling-System
P-1 : Shrimp Pond
P-2 : Shrimp and Tilapia Pond
P-3 : Shrimp ,Tilapia andSeaweed Pond
P-4 : Shrimp ,Tilapia , Seaweed
and Green Muscle Pond

P-1

P-2

Prototype P-3
IMTA
P-2

P-4

PHYSICAL‐CHEMICAL Water Quality Profile of
the Treated Breackish water Pond

PhysicalTSS BOD5DO Turb. (mg/l (mg/l
Treat Temp Salinityo))ment ( C) (ppt) pH (mg/l) (NTU)
P‐1
P‐2
P‐3

30.81 24.94 7.92 6.02 121.83 36.5 1.66
30.77 23.11 7.87 6.16 127.46 22.33 0.71
30.92 22.48 7.90 6.43 157.08 22.83 0.24

P-4 30.94 22.91 7.91 6.47 177.67± 500 m2

32

Chemical18 1.18
2.500

DIN
DIP Sulfide Iron
Treatment(ppm) (ppm) (ppm) (ppm)
P1.3
1.081 0.33 0.03 0.12
P2.3
2.154 0.21 0.03 0.21
P3.3
2.086 0.74 0.03 0.53

P4.3

2.000
1.500
1.000
0.500

1.207 0.15 0.02 0.39

0.000

P-4
Sato Umi Session-EMECS9 Conference –Baltimore-28/8-2011

33

P1.3

P2.3

DIN (ppm)

DIP (ppm)

P3.3
Sulfide (ppm)

P4.3
Iron (ppm)

Sato Umi Session-EMECS9 Conference –Baltimore-28/8-2011

34

Expansion of Dissemination Program
Black Tiger
ShrimpTreatment T‐0
T‐3

P‐1
P‐2
P‐3
P-4

(gr) (gr)
0.1 21.7
0.1
8.0
0.1
8.1
0.1 34.2

Tilapia

Sea Weed Green Muscle Total Biomass Weight
Gain
T‐0
T‐3
T‐0
T‐3
T‐0
T‐3 Total Total Biomass
T‐0T‐3(gr) (gr) (gr) (gr) (gr) (gr) (gr) (gr)(gr)
0.1 21.7
21.6
18.7 187.6
18.8 195.6
176.8
27.4 238.7 43.1
70.7 246.8
176.1
29.4 159.0 44.4 2048.0 66.6 57.7 140.6 2298.8 2158.3

Weight Gain Biomass
(gr)
4000
2000
0

22

177

176

P-1

P-2

P-3

2.

3.

4.
5.

1.
1. Karawang

2. Kampar

3. Anambas

4. Bantaeng

5. Tual2158

P-4

Success of IMTA (Integrated Multi-Trophic level
Aquaculture) in Karawan

Suhendar, Yanagi and Ratu (2014) Coastal Marine Science, 37 35

12

Sato Umi activities in Indonesia from 36
2011

6

2016/9/29

Establishment of Fisheries Management
System based on Satoumi Concept in
the Pan‐Pacific Region (2012‐2016)

Satoumi‐GAPRA International Workshop
at Jakarta, Indonesia on 13‐14 March, 2013

• 0.12 million US dollars/year sponcered byJFA• Western, central and eastern parts in the
North‐Pacific• Manual, Workshops, Data‐base• Under the umbrella of PICES（Pacific ICES;International Council for the Exploration of the Sea)

United Nation University
2012.32012.3

International Workshop
on Satoumi

Difference of human‐nature relation
between Japan and Western Countries• 1st Workshop in 2008 at Shanghai• 2nd Workshop in 2009 at Manila• 3rd Workshop in 2010 at Kanazawa• 4th Workshop in 2011 at Baltimore• 5th Workshop in 2012 at Hawaii• 6th Workshop in 2013 at Marmaris (Turky)• 7th Workshop in 2014 at Tokyo• 8th Workshop in 2015 at Da Nang (Vietnam)• 9th Workshop in 2016 at Saint Petersberg (Russia)• 10th Workshop in 2017 at Bordeaux (France)over-use
wise-use
under-use

Japan with high population density cannot have preserved zone

13

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2016/9/29

Sato‐umi,EBM,CBM

Christianity and Buddhism

Central Government• God separately creates Human and Nature.

EBM

Sato‐ｙama

Sato‐umi

Nature• Human cycles his life after his death :Human → animal → plant → human
Asian people think that gods live everywhere.

Human

CBM

Sato‐umi

ICM(Working Landscape)Local Comunity

Development of Coastal Management Method to Realize the Sustainable Coastal Sea (2014‐2018)
P.I.; T.Yanagi

General Discussion
on “Satoumi Creation”••

Sato‐chiTheme 1

Theme 3

Theme 2

Theme 4

１．Seto Inland Sea

2．Sanriku coastal sea

3．Japan Sea coastal area

４．Social and Human sciences

Decrease of fish catch
High biodiversity and production
Control of nutrients concentration

Recovery from Tsunami‐damage
Satoumi creation
Material flux from forest to coastal sea

Intergovernmental management
Spillover effect of MPA
Future forecast of ecosystem

Economic value of ecosystem service
MPA and fisheries
Satoumi story for citizen

Theme 5

Science and Technology
Habitat for marine biota: Artificial reef, Ishihimi (Tidal stone weir),
Tidal flats, Sea‐ grass beds, Coral reef
Local (indigenous) wisdom – heterogeneity of environment ＋
Scientific knowledge
Resilient ecosystem

Synthesis

Integrated numericalmodel development

Philosophy for coastal sea management
Measures for establishment of sustainable coastal sea area
Integrated model as a support tool for policy makers

Integrated Coastal Sea
ModelManagement
Commons; fishermen, stakeholders, managers, scientists ‐agreement
Local (indigenous) wisdom – heterogeneity of culture
local community, local government, central government –compensatory

visualization

Environmental Policy
Committee（Three types）

1.5 million US$/year

Global dispatch

Realize clean, rich and prosperous coastal sea (Satoumi)

Quantification of Satoumi(Trans‐disciplinary study)

Theme 5: Synthesis
・Integrated numerical model・Integrated Coastal
Management

Theme 5Road
map for integrated coastal sea model
統合的沿岸海域モデル実施のフロー

Submit results

2014
２６年度

Submit results

Transparency, DO in
Osaka Bay
大阪湾
透明度・DO

Nutrients, Sea‐weed in
Harima‐Nada
播磨灘
栄養塩・ノリ

Oyster in

Theme 1

Hypoxia in

Hiroshima
Bay
広島湾
カキ

Dokai Bay
洞海湾
貧酸素

Submit results
Coastal management
Coastal managemet
Middle scale：region⇔prefectures⇔nation
Small scale：region⇔prefecture⇔nation
Material Theme1 Seto Inland Sea
cycling ・nutrient（water quality）
Monitoring ・transfer efficiency
・tidal flat・sea‐grass beds

Three‐steps
governence

Theme2 Sanriku
・Management（sea‐algaebeds）・（sea culture arrangement）
・（Committee）
・（Material cycling）
・（forest‐river‐sea）
Apply to Ago, Kamaishi Bays

Bay/Nada⇔Forest, river, sea Sea algae
beds、
forestTidal flat, sea‐
managemgrass beds,
ent
economic value

Theme3 Japan Sea
・Monitoring and management

Materaial
Cycling
Monotoring

Theme 52016

・future projection (effetcs ofglobla warming and china)

２８年度

Integrated model of natural,
自然・社会統合モデル
socialand human sciences
Visualizationof model results“見える化”

社会・人文科学のResults of social andhuman sciencesTheme 4
研究成果
Themes 1,2 and 3

Theme 5

Apply to Wakasa, Karatsu Bays

２９年度

Aquaculture raft⇔MPA

Theme4 Social and humansciences
・Satoumi management
・Ecosystem services
・Sustainability
・Fish culture・Fishing activities management

Theme 2Carrying capacity of oyster culture in
志津川
Shizukawa
山田湾 Bay
最適養殖モデルTransfer coefficient in

2015
２７年度

2017

Three‐steps
governence

Theme 1The Seto Inland
Sea
瀬戸内海
転送効率モデル

Theme 5

Japan Sea・Coastal Management
Large scale：nation⇔nation⇔nation

Field observation
現地の観測結果

2018

Cost performance ofmonitoring
Cost performance ofarrangement

３０年度

Shizukawa
Bay model
志津川湾モデルSocial science in the watershed
流域の社会科学情報“森－里－川－海
物質輸送モデル”
Material transport modelfrom forest,
field, 人の暮らしのあり方
river to the coastal sea

Theme 2

River（upper‐middle‐
lower）⇔ocenic current
Theme 5

Theme 4

Seto Inland Sea in 1950 and 2050
1950年と2050年の瀬戸内海モデル
Integrated model

Theme 1

“自然科学、社会・人文科学統合モデル”「きれいで、豊かで、賑わいのある持続可能な沿岸海域」
clean,
productive and prosperous coastal sea(Satoumi) の実現のための行政施策に反映

14

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2016/9/29

EBM and Satoumi

Clean and productive coastal sea（１）Integrated numerical model（Land＋Sea、Natural＋Social Sciences）

mgC/m2/day（

Transparency
橋本ら(1996）
多田（1996）•••

water ４００－２２００ｍｇC m2 day‐1
多田（2006）
Tidal flat ３００－３０００ ｍｇC m‐2 day‐1
門谷（2014）sea‐grass beds ２－４０ mgC m‐2 day‐1、橋本ら（2009）

EBM and Satoumim＋eutrophicISAoligotrophic

Hiu.

B H

bottom

）

O

water

releaseSea bottom
sedimentation（water column

sinking

open ocean
variability

Boundary condition

coastal sea
Material cycling・
ecosystem
biodiversity・productivity
conservation・utilization

flow out

load

land
Land cover・disaster
population・industry・
Unit load

denitrification(mgC/m2/day)

Hir.
Ha.

Primary production

faultPrimary production in the euphotic layer

Air

low

transparencyhigh nutrient

high
low

）

水清ければ魚棲まず→きれいすぎる水に魚棲まず→汚い水に魚棲まず（赤潮・貧酸素）→ 水が清くて魚棲む
生物多様性：住処整備・植生の極相化防止
太く長く滑らかな物質循環

CBM and Satoumi

Clean and productive coastal sea（２）LargeMiddle Small
stakeholders

Primary production and fish catch

ShizukawaToyamaHigh transfer efficieny is necessary（e.g.no hypoxia）
High bio‐diversity is necessary：thich、long、smooth material cycling

Left figure: only fish catch
Small
We will include aquaculture without
bait (Oyster, scallop and sea‐weed)committee

space scale

Hidaka (2014)

Coastal sea management method

Fish culture

Large

Three‐steps Committee System for ICM

Sustainable fisheries

Satoumi creation（human and nature）

MiddleNew book on Japanese estuaries
including Satoumi creation movement

clean、productive andprosperous coastal sea

Fish priceBiomass of fish
Aqua‐culture
Decrease, land‐cover
Tidal flat・sea grass beds
Transfer efficiency
MPA
transparency・DO
load
Primery production
TP・TN, red‐tide：monitoring

open：affecting process：policy making
assessment by modelC/B evaluation

Shallow areaconservation
PDCA
Adaptivemanagement

Sand‐coverreleaseSeptember in 2015

trenchOpen ocean effect

15

9

16

K2: Relating accelerated melting of Tibetan ice shield with estuaries and continental
shelves
Chen-Tung Arthur ChenSun Yat-sen Chair Professor
Department of Oceanography
National Sun Yat-Sen University
Kaohsiung, Taiwan 804
E-mail: ctchen@mail.nsysu.edu.tw
Abstract
All the world’s mountains higher than 7,000m are in Asia and all peaks above 8,000m
are in the Himalayas and Korakorams. With an average elevation of more than 4,000m, theTibetan Plateau is the largest high-elevation region of the world, and contains as much ice andsnow as each of the poles. The glaciers of the plateau are the source of most of Asia’s greatrivers: the Ganga, Indus, Brahmaputra, Ayeyarwadi, Salween, Mekong, Yangtze andHuanghe Rivers. Indeed, one of the most important services from mountain ecosystems isthe provision of freshwater.
The mountain hydrology, and for that matter, the water supply to over a billion people
downstream of rivers originated from the Tibetan Plateau, is directly affected by changes inclimate, by land use and land cover change, and by variations in the cryosphere. Variationsin the quality and quantity of freshwater and sediment supply to the adjacent areas impact on
goods and services such as slope stability of river banks, biodiversity on land and in theriparian and aquatic systems, transportation, as well as food and energy production. Henceboth climate variability and human pressure have an impact on the Tibetan Plateau and its
role as “water towers” for the surrounding regions.
Precipitation is of course the primary driver for hydrological processes but here I focus
on the effect of global warming and the retreat of glaciers. Increased runoff and sediments
carried with it due to enhanced meting of ice are beneficial to many ecosystems and humans
through increased water, energy and nutrient supplies. On the other hand, more floods,
increased mud slides, and accelerated filling of dams and waterways downstream areenvisioned.
Overall, increased discharge of melt water probably does not increase the flux ofdissolved material to the estuaries and oceans very much because the concentration of many
dissolved species is merely diluted by the meltwater. However, the flux of particulate matteris likely to increase, exponentially, due to increased melt water flux at the beginning of thesnow-melt season, especially in the event of a breach of ice dam from a large lake. As aresult, the downstream reparian system and the delta will receive increased sediment influx
leading to enhanced deposition. But, as the snow and ice masses decrease both freshwaterand particulate matter outflows will decrease to below the current level, resulting in greaterpressure on water resources, food supply to aquatic biota, and on shoreline defense at thedelta.
In addition, continental shelves are likely to be affected as well because more melt water
results in higher buoyancy which tends to increase the outflow of surface water on the shelves.
As a consequence, more nutrient-rich subsurface waters from offshore will be upwelled onto
the continental shelves, hence inducing higher primary productivity and fish catch. Once themelt water dwindles, however, the buoyancy on the shelves will decrease, resulting in reduced
primary productivity and fish catch.

17

18

Coastal Zone:

Relating accelerated melting ofTibetan ice shield with estuaries andcontinental shelves• freshwater and food resources• gentle terrain for settlements andChen-Tung Arthur Chen

agriculture transportation

Sun Yat-sen Chair Professor
Department of Oceanography,
National Sun Yat-sen University,
Kaohsiung, 80424, Taiwan
E-mail: ctchen@mail.nsysu.edu.tw• 40-60% of the global population

http://www.mgac.nsysu.edu.tw/ctchen/Publications--2015-0915v.htm
1

2

Tibetan Ice Sheet•Largest repository of freshwater

21st Century Water War

after the two poles•Sources of major rivers: Yellow, Yangtze,
Mekong, Salween, Irrawaddy, Brahmaputra.
3

4

Taken from https://en.wikipedia.org/wiki/Global_warming

Taken from http://www.21stcentech.com/wp-content/uploads/2013/09/map_rivers_tibet11.png

Global mean surface temperature change from 1880 to 2015, relative to the
1951–1980 mean. The black line is the annual mean and the red line is the
5-year running mean. Source: NASA GISS.

5

19

6

After Typhoon Morakot(The hotel of Xiaolin Village collapsed into the river)

Stronger and more frequent typhoons!



(



)

7

8

The circled area was totally
destroyed.

Xiaolin Village ()Taken from http://mag.chinareviewnews.com/doc/1010/4/4/6/101044672.html?coluid=23&kindid=292&docid=101044672&mdate=0811102225

Taken from http://ipobar.com/read.php?tid-75888.html

910

The flooding at Linbian Township( )

Taken from http://travel.xytcn.com/lyimg/%E7%8E%89%E9%BE%99%E9%9B%AA%E5%B1%B1_2.jpg

11

12

20

Sealevel Rise

13
14

15

16

A house in an area where land subsidence issevere due to overpumping of groundwater

160 cm17
18

21

Three Gorges Dam
39 × 109 m3
capacity

Dam

Stagnation
becomes colder
and suboxic

Qutang Xia Gorge, one of the Three Gorges (taken by the author in 1992)
19

20

Dam
affects migration of biota
Damn!

Severe denudation upstream of the Three Gorges Dam (taken by the author in 1998).
21

22

Shoreline
2,000−3,000
Years Before Present

Annual Outflow(km3)

Hoover Dam Completed
30

A
Colorado River

Present
Shoreline

Yangzhou20

formershoals/islands

10

Annual Sediment Discharge(x106 tons)

0
1910
300

1920

1930

1940

1950

Zhenjiang

1960

B

Shoreline
2,000−3,000
Years Before Present

200

Av. 125-150
Av. 0.1

100

NShanghai01910 1920

1930

1940

1950

1960

1970

1980
23

24

22

In the Near Future

Longer Term

!Freshwater (Food/Energy): increased.

!Nutrients: dissolved, higher concentration but!Nutrients: dissolved, lower concentration but
not much change in flux;
particulate, increased concentrationand flux.

not much change in flux;
particulate, decreased concentrationand flux.

!Sediments: increased concentration and flux,
bad for dams but good for deltas.

!Sediments: decreased concentration and flux,
bad for deltas.

!Burst of ice dams: flooding.

!Buoyancy effect on continental shelves: reduced.

!Buoyancy effect on continental shelves: increased.
25

26

Buoyancy Effectnutrient-rich(point source)

nutrient-poor

River Plume(spreads out)organic matter
nutrient-rich(upwellingalong
the shelf edge)

Composite image of chlorophyll a distribution in the South China Sea in September 1999. Nutrient richwaters, where primary production is high, are visible as green areas along the coasts of SE Asia. Derived
from SeaWiFS data provided by NASA. Data and image processing done by C. Hu of USF and I-I Lin
and C. Lian of National Center for Ocean Research, Taipei (courtesy K.K. Liu).

A typical river plume generates upwelling of nutrient-rich subsurface
waters.
27

28(Cont.) Fig.4.
Linear regressions between monthly plume areas and river discharges for the world’s 16 largest rivers. Rivers discharging
into the Arctic Ocean are excluded. The number next to the river name denotes its rank in terms of discharge.
Fig.4.
Linear regressions between monthly plume areas and river discharges for the world’s 16 largest rivers. Rivers discharging
into the Arctic Ocean are excluded. The number next to the river name denotes its rank in terms of discharge.
Taken from: Kang, Y., D. L. Pan*, Y. Bai, X.Q. He, X.Y. Chen, C.T.A. Chen, D.F. Wang (2013), Areas of the global major river plumes,
Acta Oceanologica Sinica, 32 (1), 79-88, doi: 10.1007/s13131-013-0269-5
29

Taken from: Kang, Y., D. L. Pan*, Y. Bai, X.Q. He, X.Y. Chen, C.T.A. Chen, D.F. Wang (2013), Areas of the global major river plumes,
Acta Oceanologica Sinica, 32 (1), 79-88, doi: 10.1007/s13131-013-0269-5
30

23

Higher fish catch

Lower fish catch

More rainLess rain
Cross-section of nitrate in (a) September 1988 and in (b) December 1989.

31

8.16

Zooplankton 200

8.10

P

0.4
30

0

ΣN

200

mg-3

1.2
0.8

90

Diatom75
1959-60

Mean catch perhaul of benthos

2800
2300
1800
1300

3.0

16
12

Mean density perhaul of benthos

80

380
300
220
140
60

Recruitment ofpenaeid shrimp
1959-60

lion804¥d:¥al¥910830. jnb

5 000
4 000
3 000
2 000
Africa
1 000

South America

0Austral-Asia

40

Index of species
diversity

1992-93 1998

Europe120

Fish community
biomass

3.6

2.4
1982-83

Chaetoceros
20

H

105

6

1982-83Time

106number ofindividuals

104cells m-3

120

250
200
150
100
50

Coscinodiscaceae

North America

6 000

tons

Phytoplankton

104cells m-3

0.4

Chlorophyll

120
80
40
0

Asia

7 000

kg h-1250

mg m-2d-1

300

Primary Productivity

12

104 cells m-3

µM

10
6
5
4
3
2
1

24

µΜ

Si

8 000

Zooplankton density
18

20

Dams constructed over time by region (1900-2000)

104 cells m-3

0.0

800
550
300
50

150
100
50

Number of dams

0.8

7.98

number ofindividuals h-1

µΜ

1.2

number ofindividuals m-3

8.04

mg m-3pH

32

Source: ICOLD, 1998. Note: Information excludes the time-trend of dams in China

1992-9333

34

FACTS:!The number of large dams has increased
sevenfold since 1950 (Revenga et al., 2000)!At least one large dam modifies 46% of theworld’s 106 primary watersheds (World
Commission on Dams, 2000)!More than 40% of global river discharge
is already intercepted by the 663 of theworld’s largest reservoirs (Vorosmarty etal., 1997)
Xiaolangdi Reservoir, Yellow River,
05/19/2001

35

36

24

The best laid plans of fish and men oft go astray

Resource Transfer(Bill Allen, National Geographic Magazine, April, 2001)

As a result of improvement by the Tucurui
Dam in Amazonia, people downstream
experienced a 45% drop in fish catches.
In contrast, upstream and reservoir-area
residents experienced 200 and 900%
increases in fish catches respectively
after commissioning of the dam.
(M. Niasse, 2002)
The last big bend where the southward flowing Yellow River turns eastward(upstream of the Sanmenxia Reservoir, taken on 05/19/2001)
37

38

Night lights: 2070

Night lights: 2000

Source: Nakicenovic et al., 2000, figure is on page 233.

Source: Nakicenovic et al., 2000, figure is on page 233.
39

40

41

42

25

43

44

Fig. 1. Geographical map of
Taiwan showing the terrain
and major river.

Taken from Lou, J.Y. et al. (2014). Comparison of subtropical surface water chemistry between the large Pearl River in China
and small mountainous rivers in Taiwan. Journal of Asian Earth Sciences, 79 (A): 182-190, doi:10.1016/j.jseaes.2013.09.001.!

45

Taken from Chen, C.T.A., Liu, J.T. & Tsuang, B. (2004). Regional Environmental Change, 4(1): 39-48. doi:10.1007/s10113-003-0058-3

46

47

Taken from Lou, J.Y. et al. (2014). Comparison of subtropical surface water chemistry between the large Pearl River in China
and small mountainous rivers in Taiwan. Journal of Asian Earth Sciences, 79 (A): 182-190, doi:10.1016/j.jseaes.2013.09.001.!

48

26

Fig. 2. Mean monthly discharge of the Kaoping River and the monthly dischargefor 1999 and the first three months of 2000 (data from Water Resources Bureau).
Taken from Lou, J.Y. et al. (2014). Comparison of subtropical surface water chemistry between the large Pearl River in China
and small mountainous rivers in Taiwan. Journal of Asian Earth Sciences, 79 (A): 182-190, doi:10.1016/j.jseaes.2013.09.001.!

Taken from Chen, C.T.A., Liu, J.T. & Tsuang, B. (2004). Regional Environmental Change, 4(1): 39-48. doi:10.1007/s10113-003-0058-3

49

Fig. 5. Projected annual sediment discharge of a flood of 100-year return frequency
on a tributary of the Kaoping River. The two solid lines show one increasing and
one decreasing trend, and the dashed line shows the long-term decreasing trend.
Taken from Chen, C.T.A., Liu, J.T. & Tsuang, B. (2004). Regional Environmental Change, 4(1): 39-48. doi:10.1007/s10113-003-0058-3

Fig. 6. Projected annual discharge of 100-yr drought on a tributary of the Kaoping River.
The two solid lines show one decreasing and one increasing trend, and the dashed line
shows the long-term decreasing trend.
Taken from Chen, C.T.A., Liu, J.T. & Tsuang, B. (2004). Regional Environmental Change, 4(1): 39-48. doi:10.1007/s10113-003-0058-3

51

52

Figure 1.
The Zhujiang (Pearl River) Basin and the location of sampling stations. The thick lines represent
the main channel of the three main rivers in the Zhujiang Basin, the Xijiang, Beijiang andDongjiang, and the thin lines represent the tributaries. The solid circles represent the main
channel stations (the Xijiang: 1. Zhanyi; 2. Xiqiao; 3. Gaoguma; 4. Xiaolongtan; 5. Jiangbianjie; 6.
Bajie; 7. Longtan; 8. Du'an; 9. Qianjiang; 10. Wuxuan; 11. Dahuangjiangkou; 12. Wuzhou;
13. Gaoyao; the Beijiang: 68. Shijiao; the Dongjiang: 73. Boluo) and the open circles represent thetributary stations. The shadowed areas indicate the distribution of carbonate rocks in the basin.

Figure 7 Annual harvested volume of lumber (COA, 2000).
Taken from Chen, C.T.A., Liu, J.T. & Tsuang, B. (2004). Regional Environmental Change, 4(1): 39-48. doi:10.1007/s10113-003-0058-3

50

Taken from: Zhang, S.-R., X. X. Lu, D. L. Higgitt, C.-T. A. Chen, H.-G. Sun, and J.-T. Han (2007), Water chemistry of the Zhujiang(Pearl River): Natural processes and anthropogenic influences, J. Geophys. Res., 112, F01011, doi:10.1029/2006JF000493.
54

53

27

Figure 2.
Monthly variation of water discharge of theZhujiang (average for the period of 1958–2002)
at station Gaoyao (number 13), Shijiao (number68) and Boluo (number 73), which are the mostdownstream stations of Xijiang, Beijiang andDongjiang, respectively.

Figure 7.
Plots of the relationship between major ions andwater discharge in logarithmic scales at (a) Gaoyao,(b) Shijiao, and (c) Boluo (only ions statisticallysignificant at the significance level of 0.05 in Table
5 were plotted).

Taken from: Zhang, S.-R., X. X. Lu, D. L. Higgitt, C.-T. A. Chen,
H.-G. Sun, and J.-T. Han (2007), Water chemistry of the Zhujiang(Pearl River): Natural processes and anthropogenic influences, J.
Geophys. Res., 112, F01011, doi:10.1029/2006JF000493.
55

Taken from: Zhang, S.-R., X. X. Lu, D. L. Higgitt, C.-T. A. Chen,
H.-G. Sun, and J.-T. Han (2007), Water chemistry of the Zhujiang(Pearl River): Natural processes and anthropogenic influences, J.
Geophys. Res., 112, F01011, doi:10.1029/2006JF000493.
56

Figure 12.
Significant increasing trends of SO42− at station
Qianjiang (number 9), Wuxuan (number 10),Dahuangjiangkou (number 11), Liuzhou (number 35),
and Pingle (number 57) during the period of 1958–
1990.

Figure 13.
(a) Long-term trend of annual sediment load at
station Boluo, (b) double mass plot of cumulative
annual sediment load versus cumulative annual
water discharge at station Boluo, and (c) long-term
trend of annual TDS flux at station Boluo.

Taken from: Zhang, S.-R., X. X. Lu, D. L. Higgitt, C.-T. A. Chen, H.-G.
Sun, and J.-T. Han (2007), Water chemistry of the Zhujiang (Pearl
River): Natural processes and anthropogenic influences, J. Geophys.
Res., 112, F01011, doi:10.1029/2006JF000493.
57

Taken from: Zhang, S.-R., X. X. Lu, D. L. Higgitt, C.-T. A. Chen,
H.-G. Sun, and J.-T. Han (2007), Water chemistry of the Zhujiang(Pearl River): Natural processes and anthropogenic influences, J.
Geophys. Res., 112, F01011, doi:10.1029/2006JF000493.
58

Fig. 3. Distribution of (a) total dissolved solids, (b) DO, (c) chlorophyll a, (d) AOU, (e) pH,(f) TA, (g) TCO2, (h) NO3+NO2, (i) PO4, (j) SiO2, (k) pCO2, (l) CH4, and (m) N2O vs. distancefrom the river mouth.

Taken from: Zhang, S.-R., X. X. Lu, D. L. Higgitt, C.-T. A. Chen, H.-G. Sun, and J.-T. Han (2007), Water chemistry of the Zhujiang(Pearl River): Natural processes and anthropogenic influences, J. Geophys. Res., 112, F01011, doi:10.1029/2006JF000493.
59

Taken from: Chen, C.T.A.*, S.L. Wang, X.X. Lu, S.R. Zhang, H.K. Lui, H.C. Tseng, B.J. Wang and H.I. Huang (2008), Hydrogeochemistry
and greenhouse gases of the Pearl River, its estuary and beyond, Quaternary International, 186, 79-90, doi: 10.1016/j.quaint.2007.08.024. 60

28

(Cont.)Fig. 3. Distribution of (a) total dissolved solids, (b) DO, (c) chlorophyll a, (d) AOU, (e) pH, (f) TA,(g) TCO2, (h) NO3+NO2, (i) PO4, (j) SiO2, (k) pCO2, (l) CH4, and (m) N2O vs. distancefrom the river mouth.

(Cont.)
Fig. 3. Distribution of (a) total dissolved solids, (b) DO, (c) chlorophyll a, (d) AOU, (e) pH, (f) TA,(g) TCO2, (h) NO3+NO2, (i) PO4, (j) SiO2, (k) pCO2, (l) CH4, and (m) N2O vs. distancefrom the river mouth.
Taken from: Chen, C.T.A.*, S.L. Wang, X.X. Lu, S.R. Zhang, H.K. Lui, H.C. Tseng, B.J. Wang and H.I. Huang (2008), Hydrogeochemistry
and greenhouse gases of the Pearl River, its estuary and beyond, Quaternary International, 186, 79-90, doi: 10.1016/j.quaint.2007.08.024.

Taken from: Chen, C.T.A.*, S.L. Wang, X.X. Lu, S.R. Zhang, H.K. Lui, H.C. Tseng, B.J. Wang and H.I. Huang (2008), Hydrogeochemistry
and greenhouse gases of the Pearl River, its estuary and beyond, Quaternary International, 186, 79-90, doi: 10.1016/j.quaint.2007.08.024. 62

61(Cont.)
Fig. 4. Distribution of (a) total dissolved solids, (b) DO, (c) chlorophyll a, (d) AOU,(e) pH, (f) TA, (g) TCO2, (h) NO3+NO2, (i) PO4, (j) SiO2, (k) pCO2, (l) CH4,
and (m) N2O vs. salinity from the river mouth.

Fig. 4. Distribution of (a) total dissolved solids, (b) DO, (c) chlorophyll a, (d) AOU,(e) pH, (f) TA, (g) TCO2, (h) NO3+NO2, (i) PO4, (j) SiO2, (k) pCO2, (l) CH4, and (m) N2O
vs. salinity from the river mouth.
Taken from: Chen, C.T.A.*, S.L. Wang, X.X. Lu, S.R. Zhang, H.K. Lui, H.C. Tseng, B.J. Wang and H.I. Huang (2008), Hydrogeochemistry
and greenhouse gases of the Pearl River, its estuary and beyond, Quaternary International, 186, 79-90, doi: 10.1016/j.quaint.2007.08.024. 63

Taken from: Chen, C.T.A.*, S.L. Wang, X.X. Lu, S.R. Zhang, H.K. Lui, H.C. Tseng, B.J. Wang and H.I. Huang (2008), Hydrogeochemistry
and greenhouse gases of the Pearl River, its estuary and beyond, Quaternary International, 186, 79-90, doi: 10.1016/j.quaint.2007.08.024. 64(Cont.)
Fig. 4. Distribution of (a) total dissolved solids, (b) DO, (c) chlorophyll a, (d) AOU,(e) pH, (f) TA, (g) TCO2, (h) NO3+NO2, (i) PO4, (j) SiO2, (k) pCO2, (l) CH4,
and (m) N2O vs. salinity from the river mouth.

Fig. 2. Latitudinal distribution of specific fluxes of riverine bicarbonate.
Taken from: Cai, W.J.*, X.H. Guo, C.T.A. Chen, M.H. Dai, L.J. Zhang, W.D. Zhai, S.E. Lohrenz, K. Yin, P.J. Harrison and Y.C. Wang (2008),
A comparative overview of weathering intensity and HCO3- flux in the world’s largest rivers with emphasis on the Changjiang, Huanghe,
Zhujiang (Pearl) and Mississippi Rivers, Continental Shelf Research, 28, 1538-1549, doi: 10.1016/j.csr.2007.10.014.
66

Taken from: Chen, C.T.A.*, S.L. Wang, X.X. Lu, S.R. Zhang, H.K. Lui, H.C. Tseng, B.J. Wang and H.I. Huang (2008), Hydrogeochemistry
and greenhouse gases of the Pearl River, its estuary and beyond, Quaternary International, 186, 79-90, doi: 10.1016/j.quaint.2007.08.024. 65

29

Fig. 1. Annual fluxes of(a) water; (b) DIN;(c) NO3; (d) PO4 and(e) SiO2 of the
Changjiang (Data takenfrom Shen, 2000a, b;
Shen et al., 2001; Li etal., 2007). Since the
DIN data are notcontinuous but those of
NO3 are, the latter isalso shown.
Taken from: Chen, C.T.A.* (Arthur, C.C.T) (2008), Buoyancy leads to high productivity of the Changjiang Diluted Water: a note,
Acta Oceanologica Sinica, 27 (6), 133-140.

13

Fig. 3 δ C values for the Kaoping and Kapuas Rivers. Open squares represent the Kapuas Riverduring June–July 2007 sampling; open circles represent the Kapuas River during Dec 2007–
Jan 2008 sampling; filled triangles (black) represent the tributaries draining into the Kaoping
River; and shaded triangles (gray) represent the mainstream of the Kaoping River and threelocations along its coastal zone.
Taken from: Loh, P.S.*, C.T.A. Chen, J.Y. Lou, G.Z. Anshari, H.Y. Chen and J.T. Wang (2012), Comparing lignin-derived phenols, δ13C values, OC/
N ratio and 14C age between sediments in the Kaoping (Taiwan) and the Kapuas (Kalimantan, Indonesia) Rivers, Aquatic Geochemistry, 18 (2),141-158, doi: 10.1007/s10498-011-9153-0.
68

67

Fig. 4 a molar OC/N ratios,b %OC and c %IC
for the Kaoping and Kapuas Rivers. Opensquares represent the Kapuas River during
June–July 2007 sampling; open circles
represent the Kapuas River during Dec 2007–
Jan 2008 sampling; filled triangles (black)
represent the tributaries draining into the
Kaoping River; and shaded triangles (gray)represent the mainstream of the Kaoping Riverand three locations along its coastal zone

Figure 3.
Latitudinal distribution of (a) DIC and carbonate outcrop areas (data from http://web.env.auckland.ac.nz/
our_research/karst/) and (b) DOC concentrations and soil organic carbon density.

Taken from: Loh, P.S.*, C.T.A. Chen, J.Y. Lou, G.Z. Anshari, H.Y. Chen and J.T. Wang (2012), Comparing lignin-derived phenols, δ13C values, OC/
N ratio and 14C age between sediments in the Kaoping (Taiwan) and the Kapuas (Kalimantan, Indonesia) Rivers, Aquatic Geochemistry, 18 (2),141-158, doi: 10.1007/s10498-011-9153-0.
69

Taken from: Huang, T.H., Y.H. Fu, P.Y. Pan and C.T. A. Chen* (2012), Fluvial carbon fluxes in tropical rivers, Current Opinion in Environmental
Sustainability, 4 (2), 162–169, doi: 10.1016/j.cosust.2012.02.004.
70

Welcome to visit
National Sun Yat-sen University

Taken from http://www.uu1.com/sight/cq1486.html

7130

L1: River Discharge
Kenji Tanaka (Disaster Prevention Research Institute, Kyoto University)

Abstract
River discharge is an important source of freshwater supply to oceans. According to theglobal estimates, precipitation over oceans is approximately 391 thousand Gt per year,
while river discharge is approximately 45.5 thousand Gt per year. Amount of riverdischarge changes greatly as consequences of climate, vegetation, soil type, drainage basin
relief and the human activities, etc. As river discharge is not measured at all rivers,
hydrological model is necessary to estimate the global freshwater supply from global land
areas to oceans. There are various kinds of hydrological models to calculate river discharge.
In some applications focusing on peak discharge analysis or flood forecasting, land surface
processes can be neglected. As the time and spatial scale increases, land surface processes
become more and more important, especially, in the area where evapotranspiration is adominant component.
In this training course, in-land water cycle model which consists of land surface model,
river routing model, irrigation model, reservoir operation model is introduced to show you
how time and spatial distribution of river discharge is calculated. Current achievement,difficulties, new challenges in large scale model are introduced.

31

32

26th IHP Training Course (2016/11/28)

River Discharge
River discharge is the volume of water flowing through ariver channel. This is the total volume of water flowing
through a channel at any given point. The discharge
from a drainage basin depends on precipitation, evapotranspiration and storage.

Lecture 1

River Discharge

Drainage basin discharge =precipitation – evapotranspiration +/- storage change

Kenji Tanaka
Water Resources Research Center
Disaster Prevention Research Institute,
Kyoto University, JapanThe volume of the discharge will be determined byfactors such as climate, vegetation, soil type, drainage
basin relief and the human activities.

Example of monthly River Discharge (Japan)

Example of monthly River Discharge (World)

The Global River Discharge (RivDIS) Project
https://daac.ornl.gov/RIVDIS/rivdis.shtml

The Global River Discharge (RivDIS) Project
https://daac.ornl.gov/RIVDIS/rivdis.shtml

Shinano River

Lena
River

Ishikari River
Indus
River

Amur
River

Yodo River
Congo River

Tone River

Chikugo River

ChaoPhraya
River

Climate Indicator

Global Distribution of Aridity Index (AI)(Aridity Index & Evaporation Ratio)
Annual evapotranspiration approaches annual precipitation in arid andsemi-arid regions where the available energy greatly exceeds theamount required to evaporate annual precipitation.
Evapotranspiration is a key information for water management in theregion where available water resources are limited.

Aridity Index
Energybalance

Rnet
LP

Evaporation Ratio
WaterbalanceEP

5 < AI < 12 Arid
2 < AI < 5Semi Arid
0.75 < AI < 2
Sub Humid
0.375 < AI < 0.75 Humid(Ponce et al. 2000)

Rnet: annual mean net radiation
P : annual precipitation
L : latent heat of vaporization
E : annual evapotranspiration

Humid

33

Sub-humid Semi-arid

AridGlobal Hydrological Cycle

Global Distribution of Evaporation Ratio (ER)Oki & Kanae (2006)

Freshwater supply to oceansLand Surface Process

Water budget“Land surface processes arethose associated with theexchange of water and energy
between the land surface andthe atmosphere and are,therefore, integral componentsof hydrologic and atmosphericsciences.”(by Bill Crosson (NASA MSFC))• Water is exchanged between the atmosphereand the land surface through the processesof precipitation, evaporation, andtranspiration.
• Water is exchanged between the land
surface and ocean/lake through runoff.

•ΔS = P - E - R

integral components ofhydrologic, atmospheric, andocean sciences

P : precipitation(rain/snow) input from atmosphere
E : water vapor flux by evaporation and transpiration
R : runoff flux by river system and ground water system
ΔS : change in the surface water and soil moisture

Hydrological Cycle
from GEWEX home page

Energy budget

Evapotranspiration = Evaporation + Transpiration• Rn is partitioned into fluxes of sensible, latent,
and ground heat.

Evapotranspiration is an interface
Between water cycle and energy cycle

evaporation
Interceptionwater• This partitioning is strongly dependent on boththe land cover characteristics (landuse) and its
hydrological state (wet/dry).

stomatatranspiration
evaporation• Why energy partitioning is important?

soil moisture

Rn = H + λE + G
H Æ heating lower atmosphereλE Æ heating middle atmosphereG Æ surface (time lag between RB & EB)

Root zone

Water cycle：
Rainfall reached to surface go back toatmosphere as water vapor.
Evaporation is a loss term in terms ofwater resources.
Energy cycle：
Transfer the energy of vaporization toatmosphere. Energy absorbed bysurface is redistributed to atmosphere.
Water vapor from surface willcondense(latent heat release) and fall down
again as rainfall.

34

Annual River Discharge

Integrated Water Resources Model

This map shows annual river discharge for the globe on a 0.5 X 0.5
degree global river network. Blended river flow represents a composite
of observed river discharge from the Global Runoff Data Centre andmodeled river flow.

Water Cycle
Crop Dynamics
Human Effects

GWSP Digital Water Atlas

Green area model（SiB）

Land Surface (SiBUC)
Grid box is divided into
three landuse categories
1. Green Area
2. Urban Area
3. Water Body

• Prognostic variables
temperature (canopy, ground, deep soil)interception water (canopy, ground)soil wetness (surface, root zone, recharge)• Time invariant parameter
geometrical parameter
optical parameter
physiological parameter
soil physical properties• Time varying parameter (LAI etc.)
estimate from satellite data
• Physical processes
radiative transfer
interception loss
soil hydrology
canopy resistancetranspiration
turbulent transfer,snow, freezing/melting,… etc.

１．Broadleaf-evergreen
Simple Biosphere
trees
２．Broadleaf-deciduous
trees
including Urban
３．Broadleaf
and needle leaf trees
Canopy４．Needle leaf-evergreen trees
５．Needle leaf-deciduous trees
６．Short vegetation/C4 grassland
７．Broadleaf shrubs with bare soil
８．Dwarf trees and shrubs
９．Farmland (non-irrigated)
10. Paddy field (non-irrigated)
11. Paddy field (irrigated)
12. Spring wheat (irrigated)
13. Winter wheat (irrigated)
14. Corn (irrigated)
15. Other crops (irrigated)

Prognostic equation of green area model
Temperature
Rn: net radiation
H : sensible heat
λE : latent heat

Soil Wetness
P1: infiltration
Qi,j : water exchange
Es : soil evaporation
Edc : transpiration
Q3 : drainage

Irrigation∂T
Cc c = Rnc − Hc − λEc∂t∂Tg= Rng − Hg − λEg −ωCg (Tg −Td )Cg∂t∂T
Cd d = Rng − Hg − λEg∂tBasic concept is to maintain soil
moisture/water depth withinappropriate ranges for optimal crop
growth.
Application to wheat, corn, soy bean,cotton etc…
New water layer is added to treat
paddy field more accurately.

⎤
E
∂W1
1 ⎡
=
P1 − Q 1 , 2 − s − E dc ,1 ⎥∂t
θ s D 1 ⎢⎣
ρw
⎦
∂W 2
1
[Q 1, 2 − Q 2 , 3 − E dc , 2 ]
=
θ sD2
∂t
∂W 3
1
[Q 2 , 3 − Q 3 ]=θ s D3∂t35

Crop Calendar (Growing stage)

Water control in farmland

Time series ofsatellite NDVI

Soil moisture
Days

Water control in paddy field

Water depth
Days
Feb Mar

Jan

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Land surface process andriver routing process

Annual IWR (Irrigation Water Requirement)[mm/yr]Withdrawal Sim [Gt]

1000

Model

Dam operation

100

Annual IWR for each grids areaggregated into country,
then compared with AQUASTAT

10

1

Water intake
Agricultural
Industrial
Domestic

0.1

0.01
0.01

AQUASTAT
0.1

1

10

100

1000

※ Using irrigation efficiency (Doll et al., 2002)

Withdrawal AQUASTAT [Gt]Hydro-BEAM

Basic Structure

Hydrological river Basin Environment Assessment Model
Cell concentrated type model
2 Hill slope & 1 Channel

Kinematic wave modelRiver Routing / Channel Network
Input rain(= net rainfall + snowmelt)d

ＡＡ layerlayerhaＢ layer
Ｂ layer

hs
ha

∂h ∂q
+
=r
∂t ∂x

Surface flow
Subsurface flowlayer
ＣＣ layer
Ｄ layer

Ｄ layerq = f (h ) = α ( h − d )m + ah
h = hs + ha

ＡＡ layer
layer
layer
ＢＢ layer

Infiltration

layer
ＣＣ layer
Ｄ layer

Ｄ layerS

k1
k2

Subsurface runoff
Infiltration

36

Multi-layer
Storage function modeldS= I −Odt

O = (k1 + k 2 ) ⋅ S

RiverSeasonal Change （Asia）

Monthly River Discharge

JanP
Qsf ΔSW

TWS (Total water storage)=Soil moisture + surface water(snow)

Roff i ,t = Qsf i ,t + Qgi ,t

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Water Balance（ Chao Phraya & Mekong）

Water Balance check① WB② PREC③ EVAP④ ROFF⑤ TWS⑥ delTWS

Feb Mar

E win
wout

Qg
i
catchment

WBi ,t = Pi ,t − Ei ,t + Wini ,t − Wouti ,t − ΔSi

37

Validation of River Discharge (GRDC)RIV=BLUE NILE

RIV=SANAGASTA=ROSEIRES DAM

14000
12000

Historical hydrological simulation in North California

STA=EDEA

6000

obs
nodam
damin

obs
nodam
damin

5000

10000

4000

8000

3000

6000

2000

4000

1000

2000
0

J

F

M

A

M

J

J

A

S

O

N

0

D

J F M
STA=NAKHON SAWAN
RIV=MEKONG

RIV=CHAO PHRAYA
2500

A

M

J J A S O N D
STA=PHNOM PENH (CHRO

40000

obs
nodam
damin

2000

obs
nodam
damin

35000
30000

1500

25000

1000

15000

20000
10000

500

5000
0

0

J

F

M

A

M

J

RIV=GANGESDistributed river discharge simulation

A

S

O

N

D

J

J

A

S

O

N

1

D

STA=BOGUCHANY

1. Shasta

obs
nodam
damin

2

6000
2000

M

J J A S O N
STA=BAGHDAD

D0

J F M A M
RIV=CHURCHILL RIVER

J

2. Oroville

J A S O N D
STA=BELOW FIDLER LA

9000

obs
nodam
damin

obs
nodam
damin

8000
7000
6000
5000
4000

1000

3000
2000

500
0

J

2

F

M

A

M

J

RIV=WINNIPEG RIVER
AS C

J

A

S

O

N

1000
0

D

STA=SLAVE FALLS

6000

J

F

M

A

M

J

J

RIV=SAO FRANCISCO

A

S

N

3. Yuba

3

5

D

4

obs
nodam
damin

7000
6000

4000

O

STA=JUAZEIRO

8000

obs
nodam
damin

5000

5000

5. Cache Creek

4. Folsom

4000

3000

BIAS

M

4000

1500

1

A

8000

2500

0.5

M

12000

2000

Dry region

F

14000
10000

3000

1.5

J

RIV=ANGARA
16000

obs
nodam
damin

J F M A
RIV=TIGRIS

Overestimate in dry region

J

STA=FARAKKA

45000
40000
35000
30000
25000
20000
15000
10000
5000
0

3000

2000

2000
1000

0

1000
0

0

-0.5

Dry index

Wet region
Evap/Prec
Wet

-1
0

0.2

0.4

0.6

0.8

J

F

M

A

M

J

RIV=AMAZONAS
260000240000220000200000180000160000140000120000100000
80000
60000

1

Dry

Same tendency in other global hyrological model(e.g. Doll et al., 2003; Pokhrel et al., 2012)

J

A

S

O

N

D

STA=OBIDOS

J

F

M

A

M

J

RIV=RIO JURUA

J

A

S

N

D

N

D

STA=GAVIAO

9000

obs
nodam
damin

O

obs
nodam
damin

8000
7000
6000
5000
4000
3000
2000

J

F

M

A

M

J

J

A

S

O

N

1000

D

J

F

M

A

Sim (dam)× Obs.

M

J

J

A

S

O

Joint research with Dr, Ishida @UC Davis

Sim (nodam)

Runoff Simulation is still difficult even in Japan (snow region)

Simulated runoff is overestimated in drought year

1600

120

Inflow to Lake Oroville

SiBUC
obs

100

350

1400
1200

80

1000

250

60

800

200

40

150

20

300

SWE

600
400

100

200

0

50

0

2006

2007

2008

2009

2010

2011

2006

2012

2007

2008

2009

2010

2011

2012

2015/9/1

2015/4/1

2014/6/1

2015/4/1

2014/1/1

2013/8/1

2014/11/1
2014/11/1

2013/3/1
2013/3/1

2012/5/1

2012/10/1
2012/10/1

2011/7/1

2011/2/1

2012/5/1

2010/9/1

2011/12/1
2011/12/1

2010/4/1

2009/6/1

2010/4/1

2009/1/1

2008/8/1

2009/11/1
2009/11/1

2008/3/1

2007/5/1

2007/10/1

2008/3/1

2006/7/1

2006/2/1

2005/9/1

2007/10/1

Sim

2006/12/1

2005/4/1

2004/6/1

2004/1/1

2003/8/1

2004/11/1

2003/3/1

2002/5/1

2002/10/1

2001/7/1

2001/2/1

2000/9/1

2001/12/1

2000/4/1

1999/6/1

1999/1/1

1998/8/1

1999/11/1

1998/3/1

1997/5/1

1997/10/1

1996/7/1

1996/2/1

1995/9/1

1996/12/1

1995/4/1

1994/11/1

0

Radar Analysis Precipitation（Jan to Mar）

Obs

Inflow to Folsom Lake
600
500
400
300
200
100

Sim

2015/9/1

2014/6/1

2014/1/1

2013/8/1

2011/7/1

2011/2/1

2010/9/1

2009/6/1

2009/1/1

2008/8/1

2007/5/1

2006/12/1

2006/7/1

2006/2/1

2005/9/1

2005/4/1

2004/11/1

2004/6/1

2004/1/1

2003/8/1

2003/3/1

2002/10/1

2002/5/1

2001/12/1

2001/7/1

2001/2/1

2000/9/1

2000/4/1

1999/11/1

1999/6/1

1999/1/1

1998/8/1

1998/3/1

1997/10/1

1997/5/1

1996/7/1

1996/2/1

1995/9/1

1996/12/1

1995/4/1

1994/11/1

0

Obs

Simulated Evaporation might be too small .
Physiological parameter (dry tolerant)?? Need more study
Deep root zone??

River Discharge in Snowy Region
Rs = 1 / (1 + 0.213 ⋅ Ws )Undercatch correction
can improve the acculacyof river discharge

Radar Analysis Precipitation（Jan to Mar）

Undercatch correction

Vapor pressure

Critical temperature

0

KITAKAMI: Tomeobs
row
modified

550

Omon
o

200
150
100

obs
row
modified33discharge [m /s]

Recently, many rain gauge information by local government were added
for analysis.

500
400

Shinan
o

300
200

38

300

200

3

150

50

discharge [m /s]

250
200

100

1000

Tokach
i

500
450

700

Kitakami
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

400
350
300
250
200

Yoneshir
o

150

400
300
200
100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

ABUKUMA: Tateyama
220
200
180
160
140

Abukumaobs
row
modified

120
100
80
60

100
50

Mogami600
500

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

obs
row
modifiedobs
row
modified

900
800

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AGANO: Maoroshi

800

600

350

800

900

700

400

150

discharge [m /s]

Basically due to the quality of Input snowfall data

1000

450

250

MOGAMI: Sagoshi
obsrow
modified300

YONESHIRO: Futatsui
obs
row
modified500

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

SHINANO: Odiya
1100

3

OMONO: Tsubakigawa

33discharge [m /s]

250

Ishikari
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

450

350

200
100

550

300

400
300

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

500

400

600
500

350

3

0

discharge [m /s]

3

100

800
700

3

200

Teshi
o

900

discharge [m /s]

2011

400

400
obs
row
modified

1000

discharge [m /s]

2006

500

300

1100

obs
row
modified600

discharge [m /s]3discharge [m /s]

700

TOKACHI: Moiwa

ISHIKARI: Ishikari

discharge [m /s]

TESHIO: Maruyama

Wind speed

Tc = 11.01 − 1.5e

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

700
600
500

obs
row
modified

Agano

400
300
200
100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec－black：Observation [m3/s]－blue：undercatch correction[m3/s]－red：no correction[m3/s]

Validation of River Discharge (MLIT)
Water Balance

Nash Efficiency

Precipitation products over Eastern Asia

Budget = ∑ (Qsim − Qobs ) / ∑ Qobs

Nash = 1 −∑ (Q − Q )∑ (Q − Q )
sim

obs

obs

obs

(a-1) APHRODITE (obs.)(a-2) GPCC (obs.)(a-3) H08 (obs.)(a-4) GPCP (obs. + satellite)(a-5) GSMaP (satellite)(a-6) JRA25 (reanalysis)

2
2

No.

River

StationBudget[%]

Nash

No.

River

StationBudget[%]Nash

1TeshioMaruyama

-24.5

0.613

11

Tone

Kurihashi

+ 9.7

0.892

2

Ishikari

Ishikari

- 4.0

0.674

12

Naka

Noguchi

- 9.6

0.852

3

Tokachi

Moiwa

- 8.1

0.882

13

Fuji

Kitamatsuno

+56.9

0.666

4

Mogami

Sagoshi

-19.8

0.721

14

TenryuKashima

- 1.3

0.878

5

Omono

Tsubakigawa

-18.4

0.801

15

Kiso

Inuyama

+ 1.1

0.915

6

Kitakami

Tome

-18.8

0.742

16

KatsuraKatsura

-14.6

0.692

7

Yoneshiro

Futatsui

-20.9

0.685

17

Kizu

Yawata

-21.7

0.786

8

AbukumaTateyama

-16.0

0.834

18

Gono

Kawahira

-23.1

0.685

9

Shinano

Ojiya

-22.0

0.594

19

Yoshino

Ikeda

+ 9.3

0.876

10

Agano

Maoroshi

-19.8

0.729

20

ChikugoSenoshita

-15.2

0.941

(mm/year)

Precipitation products over Eastern Asia

Runoff Ratio(mm/year)
While precipitation has large difference between products, simulated
evapotranspiration using products has small difference. Most of difference inprecipitation translates to difference in runoff. Any error in the precipitationtranslates to approximately the same absolute error in runoff over the Eastern Asia.

Need for high quality data
1800

New Challenges
系列1
Evap

1600

系列2
Runoff

Online-coupling is required to consider
dynamic land cover change caused by flooding.

1400

195

1200

113

1000
800
600
400
200

Average value depends
on data used (selected)

Full(C2)
Limit(C2)892

No Flooding

949

1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004

0

Prec
DATA A

Evap

Flooding
Evap

Prec

Evap

Flood plain

DATA B

RunoffSmall difference in rainfall
makes large difference in runoff

LSP

LSP
Runoff

River

39

River

Evaporationfrom water body

Floodplain
Fraction

Dynamic (Online) coupling
SiBUC & CaMa-Flood
Flooded area
Forcing

Runoff(SiBUC)

Discharge (CaMa-Flood)

FLD plain (CaMa-Flood)

Horizontal move
→CaMa-Flood
Vertical move
→SiBUCdM gdt
Indus Ganges

Indus Ganges
MekongFeb Marinfiltration
infiltration

Apr

May

Jun

Jul

Aug

Sep

river

dry

rainy
Oct

Nov

• River discharge is an important source of freshwatersupply to oceans.

12500

100

10000

200

7500

300

5000

400

2500

500

Rainfall
Kine

Monthly rainfall at the chatchment[mm]Monthly outflw

0• The volume of the discharge will be determined byfactors such as climate, vegetation, soil type, drainage
basin relief and the human activities.
• As the time and spatial scale increases, land surface
processes become more and more important,especially, in the area where evapotranspiration is adominant component.

600
1980/01

1981/01
CaMa
Onlinesoil

Summary

outflw Malakal
15000

As flooded area increases,
evaporation & infiltration increases

baseflow

Dec

Application to White Nile
(Sudd wetland effect)0
1979/01

Prec

Chao Phraya

dry
Jan

Evap

canopySurfacerunoff= flooded wateramount change

MekongChao Phraya

Evap Prec

Non-flooded area

1982/01• In-land water cycle model is introduced.

Observation• Current achievement, difficulties, new challenges inlarge scale model are introduced.

Absolute value and seasonal change are greatly improved

Thank you so much for your kind attention!

Nile River (Aswan)40

L2: Submarine groundwater discharge from land to the ocean
Makoto Taniguchi(Research Institute for Humanity and Nature, Kyoto, Japan)

Submarine groundwater discharge (SGD) is a hidden pathway of water and dissolved
material from land to the ocean. Interdisciplinary research by hydrologists andoceanographers during the last decay revealed less terrestrial SGD but huge material
flux by total SGD including re-circulated SGD.

Spatial and temporal variations in

SGD were evaluated in site by direct measurements including seepage meters, 222Rn,
resistivity and others, as well as numerical simulations. Global estimates of SGD andevaluations of impact of SGD on coastal ecosystem and fisheries are next future
research areas which are related to SGD.

41

42

Reviews of global SGD rate

Interactions Between Groundwater
Groundwater
Discharge
andSubmarine
Seawater in
Permeable Sediments

Authors- Another pathway of water and dissolved materials
from land to the ocean -Makoto Taniguchi
Research Institute for Humanity and Nature (RIHN)

Freshwater →Role of SGD

6 % of the total water flux

Literature

Church [1996]

0.01–10% of Surface R.

Literature

COSODII [1987]

0.3 % of Surface Runoff

Garrels and MacKenzie[1971]

10 % of Surface Runoff

Hydrologicalassumptions
Water balance

Lvovich [1974]

31% of the total water flux

Water balance

Nace [1970]

1 % of Surface Runoff

Hydrogeologic
assumptions

Zektser et al. [1973]

10 % of Surface Runoff

Water balance etc.

Zektser[1993]

6 % of the Total water flux

Hydrograph separation

andLoaiciga

Coastal ← Seawater(Oceanographers)
water(Hydrologists)

MethodBerner and Berner [1987]

Reviews of SGD(1) ～7% of total discharge may be SGD(2) SGD occur in continental scale, but
quantitative evaluations are limited(3) Intercomparisons are needed(Burnett et al., 2001)

Locations of SGD measurements (Taniguchi et al., 2002)

Intercomparisons of SDG in the world (2000-2009)

12
17

12
42
34
39

14

45

25

17 4,6,7,8,9,30
13,28
10,35

2,16,41

New York
(IOC)

21

33
1,15,31
43
26,27
3
11

17

43
18,29

36
23

5
23

23
23

38

23
23

23

37
40

39

22

23
24

42
34
45

25

17 4,6,7,8,9,30
13,28
10,35

21

33
1,15,3143
26,27

Florida11(SCOR/LOICZ)

14

Yellow River (RIHN)

43
18,29

2,16,41

3
24

36
23

Sicily(IAEA)

5
23

23
23

38

23
23

Thailand(IAHS/IAPSO)

19,20
32

37
40

22

23
23

44

Philippine
(APN)
19,20
32

Brazil (IAEA)

Perth(IOC/IHP)

44

1
43

Methods for evaluating SGD

Various types of seepage meters (1)Task1: Typology(global scale)Task3:In situ measurement (local scale)Task2: Modeling (basin scale)

Lee – type manual seepage meter

Problems of Lee –type manual
seepage meters

Methods for evaluating SGD (1)(1) the resistance of the tube and bag
should be minimized to the degree possible to prevent
artifacts ; (1cm diameter, prefilled water in bag)(2) the effects of current and wave on the bag
use of a cover for the collection bag may reduce theeffect of surface water movement due to wave,
current or streamflow activity(3) a seepage meter detection limit
of approximately 3 to 5 mL m-2 min-1 (0.4 to 0.7
cm/day) should be applied (Cable et al. 1997), and(1) Typology(2) Modeling and calculations
2-1 numerical simulations
2-2 water balance method(3) Direct measurements
3-1 seepage meters
3-2 piezometers
3-3 tracers (Rn-222, Ra-226, CH4 etc.)

Labor Intensive !!!

Various types of seepage meters (2)automated seepage meters

Methods for evaluating SGD (2)(1) Typology → need P, E, R, vegetation, geology etc.
(2-1)Water balance method → need P, E, R
(2-2) Numerical simulations →need several model parameters(3-2) Piezometers → need hydraulic conductivity(3-3) Tracers (Rn-222, Ra-226, CH4 etc.) →need wind speedetc. for modeling(3-1) Seepage meters→ only direct method for SGD flux

Continuous heat UltrasonicHeat pulse

244Artifact of seepage meters (2)Schematic diagram of continuous heat
type of automated seepage meter

Controlled

Naturalpool
20Power and Data Logger
A

Ultrasonic typeautomated seepage meter(heater &thermistor)

-10
10
-20
Pool control
Auto seepage meterLong Key tide (cm)5002-Apr(thermistor)-40
03-Apr

04-Apr

05-Apr

06-Apr07-AprTime/Data 2001

Chanton et al. (2002)

SGD - manual vs. SGD – automatedHeat pulse type automatedseepage meter(intercomparison at FSUML, Aug.2000)
50
continuousheat
heat
Continuous

SGD-auto (cm/day)

40

Bi-Directional Sonic Beam Path

Ultrasonic
ul

30
20
10
0
0

Kruper et al, (1998)

10

20
30
40
SGD-manual (cm/day)

50

What can we tell from seepage meters ?
(Intercomparison at FSUML)

Artifact of
seepage meters
Bernoulli's
Revenge

August 15, 2000

Aug 1450

40

40

X¦= 18.2 L/min.mY-¦ = 18.9 L/min.mX¦= 13.2 L/min.mY-¦ = 19.7 L/min.m

30

30

20

20

10

10

0
40
0

42

Q = 1.9 m 3/min

101

50

Q = 1.6 m3/min

0YDistance (m) 200X
Y-transect

August 16, 2000

73

73
101

40

X¦= 21.7 L/min.mY-¦ = 23.6 L/min.m

Aug 16

cm/dayAug 1750

30

10X¦= 23.0 L/min.mY-¦ = 26.1 L/min.m

30

20

10

Q =2.5Q = 2.3 m 3/min

http://www.scor-wg114.de/

168
201

201

20

X-transect

134
Distance (m)

168

40

Y-transect

August 17, 2000

42

X-transect

134

Distance (m)SCOR WG#114

Aug 1550

cm/daySGD (cm/day)August 14, 2000

50
cm /dayPaulsen et al., 1998

-30

cm/ d ay

SGD

B

0

15

Tide (cm)

Seepage (cm/d)

Power and Data Logger

10

3

0

Y-transect

42
73

0

Y-transect

42

101
Distance (m)X-transect

134

73
101

X-transect

134

168
201

Distance (m)168

SGD mostly decreases with distance from coast
201

3
45

What can we tell from seepage meters ?

Why is the high SGD during spring tide ?

Automated seepage meters can tell us continuouschanges of SGD with long term & high resolutions

40

Electric conductivity

1 5 -A u g

1 6 -A u g

1 7 -A u g

1 8 -A u g

T im e /D a te 2 0 0 0m e te r
Y 4 aL ue toe - ty p e m e te r

75
50
25
0
1 4 -A u g

1 5 -A u g

1 6 -A u g

1 7 -A u g

1 8 -A u g

3

32

1

28

-1

24
Sep-19 0:00

D a te 2 0 0 0

Tide

36

Sep-23 0:00 Sep-27 0:00

Oct-1 0:00

Oct-5 0:00

Oct-9 0:00

Tide (m)25
0
1 4 -A u g

cm/day550Electric conductivity (mS/cm)

cm/dayRelationship between fresh SGD
and recirculated saline SGD (1) Conductivity meter

Y2

75

-3
Oct-13 0:00

Spring tide: high recirculated saline SGD due to tidal pumpinga u to m e te r
L e e -ty p e m a n u a l m e te r

Long term changes in SGD at Osaka Bay, JapanGRL 2002

Suruga bay (1) GW2005

Negative relationship between SGD and tide
• Semi-diurnal change in SGD• High hydraulic gradient →high SGD(Near shore)

Positive relationship between SGD and tide
Spring tide: high SGD, Neap tide: low SGD(Offshore)

SGD=SFGD+RSGD

Semi-monthly changes of SGD confirmed by
not only seepage meter but also by Rn and CH4
160

2.5

140

0.05

120

2.0

0.04

100

0.03

1.5

0.02

1.0

0.01

0.5

0.00
0.0

-0.01
-0.02

-0.5
0

200

400

600

800

Jul. 3 Jul. 12 Jul. 20 Jul. 28 Aug. 6

2600

2800

3000

CH4(PM)

Rn Activity (Bq/L)
222

180

3.0

0.06

80
60
40
20

Tidal Height (cm)

0.07

0
-20
-40

3200

Oct. 20 Oct. 28 Nov. 5 Nov. 14

Elapsed Time (hours)

Kim and Hwang, 2002

Spring tide: high SGD, Neap tide: low SGD

Taniguchi et al. 2002

4
46

B
C

45

D
E

40

30

50

high FSGD

25

2003/8/3 12:00 2003/8/4 12:00 2003/8/5
40 12:00 2003/8/6 12:00 2003/8/7 12:00

B

Resistivity
C

D

E

cable
F

B

C

E

D

F

30

EC of groundwater20G

00.18mS/cm

Seepage meters＆
CTsensors

40

80
120
Distance from the land (m)EC of sea water
43mS/cm

EC of SGD8

SGD vs Temp.

32

SGD D

 Sting/Swift・D Multi-probe
Number of probes:28
Distance of probe:3m(Max:10m)

SGD (×10-6m/s)

6

F

31

Tempereture (℃)

Resistivity Measurements (saltwater/freshwaterinterface)Temperature D

B・C・D

4

30

2

29

E・F
0
2003/8/6 12:00

2003/8/7 12:00

SGD F

24
SGD (×10 m/s)

blue：low resistivity

31

-6

Ocean

32

Temperature F

30

8High Temp. at high SGD（E・F）

blue：((high porosity)or saltier water16

29

no effect of FSGD at locations E・F
02003/8/4 12:00

Resistivity
blue: sea water, red: fresh water

SGD vs tide
D

G

F

B・C・D

28
2003/8/5 12:00

2003/8/6 12:00

2003/8/7 12:00

8

400

6

200

4

0

Tide

2

E・F

Saltwater-freshwaterinterface0
2003/8/4 12:00

-200

SGD D
2003/8/5 12:00-400

2003/8/6 12:00

2003/8/7 12:00

40

High
tide

Temperature (℃)

32

Tide (cm)

red：((low porosity)or fresher water

2003/8/5 12:00

Effect of FSGD (low temp.)

SGD (×10-6m/s)

Land

28

2003/8/4 12:00

Low Temp. at high SGD（B・C・D）
red：high resistivity

160

400

Tide

Low
tide

30
SGD (×10-6m/s)

Negative relationshipbetween SGD and tide

Phase lag between SGD and
Tide at locations E・F

200

SGD F

20

10

-200

0
2003/8/4 12:00

0

Tide (cm)

A

B～F

F
35

Erectric conductivity (mS/cm)

Erectric conductivity (mS/cm)

50

KUMAMOTO-400
2003/8/5 12:00

2003/8/6 12:00

2003/8/7 12:00

547

Resistivity
blue: sea water, red: fresh water

Conclusions
1．Diurnal and semi-diurnal changes of SGD due totidal changes were found.
2．SGD increase from neap tide to spring tide due toincrease of recirculated submarine groundwater
discharge
3．SGD rate, EC and Temperature of SGD are different
between within interface and offshore interaface.

G

Saltwater-freshwaterinterface

High
tide

4．Fresh submarine groundwater discharge rate can be
estimated using Bokuniewicz（1992）within the
freshwater-saltwater interface.

B, C, D: within the interface
E,F: offshore the intercae

5．SGD increases from neap tide to spring tide because
recirculated seawater due to tidal pumping increases.
6．Saltwater-freshwater interface move toward offshore
during spring tide at Shiranui bay, Japan.

Low
tide

Comparisons between observed and calculated SFGD

Results

Separation of SGD into SFGD and RSGD
SGD

fresh

salineD

SGD ＝ SFGD ＋ RSGD
SGD × Csgd ＝ SFGD × Csfgd ＋ RSGD × Crsgd

F

B・C・D
E・F

EC of SGD

EC of groundwater

EC of seawater

Analyses using Bokuniewicz（1992）
SFGD

q=

( k =k =Hydraulic conductivity

Kv･i
πk

Kv
Kv

In ( coth

Horizontal hydraulicconductivity

1-6

SFGD (×10 m/s)

Results

B・C・D

SGD was simulatedwith independent X)

4l

Thickness of aquifer

)

Kh

D

πxk

Hydraulicgradient

F

E・F

0.8

Robinson et al. 2007

Ovserved D
Caluculated D

0.6
0.4
0.2

0
2003/8/4 12:00
4

SFGD can be simulated at B・C・D

2003/8/5 12:00

Ovserved F

2003/8/6 12:00

2003/8/7 12:00

Caluculated F

3-6

SFGD (×10 m/s)

Distance from thecoast

2
SFGD cannot be simulated at E・F
1

SFGD can be estimated using Bokuniewicz（1992）within
0 interface
the saltwater-freshwater
2003/8/4 12:00

2003/8/5 12:00

2003/8/6 12:00

2003/8/7 12:00

648

Obama, Fukui, Japan

Nutrient discharge （SW vs. GW）
Obama
1e+7

Moles/day

1e+6

SriRacha (Jan 04)

Hua Hin (July 04)
River flux
Seepage Flux

47%

1e+5

15%

37%
58%

44%

2%

Fisheries

71%
1%

1e+4
1e+3
NH4

NO3

PO4 SiO2

NH4

NO3

PO4

(PP)

SiO2

Importance of SGD for nutrient
discharge to the ocean

37

Sugimoto et al. 2015

Burnett et al. 2007

Water balance with SGD in Obama

How to evaluate SGD ?
(1) Seepage meter

Precipitation (P)

4.10×106 t/d(=100%)(5) Resistivity
Red：High Res⇒Low Salinity
Blue：Low Res⇒High Salinity

1.28×106 t/d(=31%)

Evatranspiration (Et)

Water Budget in Obama Bay
basin
Recharge of GW ＝ P – Et - R※ annual mean from 2003 to 2012

222Rn & Salt Mass Balance

Model in Obama Bay(2)222Rn

River Discharge (R)(6) Numerical model of SGD

Rn→ 206Pb

238U → 222

2.58×106 t/d(=63%)

Sea
bed

222Rn

12000×106 t/d

1.29×106 t/dRecharge of Groundwater (GW)(3) Stable isotope ratio(Sr, O, H)

Fresh(4) Sea bed temperaturewater

0.24×106 t/d (=6% of precipitation)

Sea
water

Groundwater
Sugimoto et al. 2015

How to evaluate Submarine Groundwater
Discharge (SGD) and Phytoplankton Production(PP)¾ Submarine Groundwater
Discharge• 222Rn isotope•

into Obama bay by river vs SGD into Bay（※10 times average）

Radon Detector (RAD7)
Fresh water discharge by SGD is 20 %, however
nutrients by SGD is 30-60 % of total loads.

magnitude higher than 222Rn levels observed in sea water.
• 222Rn is a natural short-lived radioisotope (half-life = 3.8days) that is chemically inert and easily measured.
1: Batch Survey 2: Continuous Spatial Survey 3: Continuous Tempora
Survey80

Lack of phosphate (P) for primary
production is likely be happen in bay.

Nenriched

Rn (dpm /L)
222

Si

60

Groundwater:N

40

P-enrichedSi

20
0
9:30

10:00

10:30

11:00

Sugimoto et al. 201511:30

lack ofP

Nitrate and silica aremostly delivered byriver water, but
phosphate bygroundwater
Phosphorous limits
primary production
through the year

P

River water: N & Si -Rn (dpm/L)

9:00(estimated from
Rn, =23 % of total
discharge (RD +SGD)

Obama Ratios of water and nutrient discharge

222Rn levels usually found in groundwater are 3-4 orders of

Sugimoto et al. 2015

Outer ocean

0.36×106 t/dPPrimary production
SGD

DIP-enriched SGD
would play an importantrole in primary
production in Obama
Bay.

7
49

ama: Groundwater-fisheries
Response analyses ofIntegrated model ofwater, dissolved
material, heat, andprimary production

Taniguchi M, Iwakawa H. Measurements of submarine groundwater discharge rates by a continuous heat– type automated seepage meter in Osaka Bay, Japan, J. Groundwater Hydrol. 2001; 43(4): 271－277.
Taniguchi M. Tidal effects on submarine groundwater discharge into the ocean, Geophys. Res. Lett.
2002;
29(12): 10.1029/2002GL014987.
Taniguchi M, Ishitobi T, Saeki K. Evaluation of time-space distributions of submarine groundwater
discharge. Ground Water 2005; 43: 336-342.
Taniguchi M, Ishitobi T, Shimada J, Takamoto N. Evaluations of spatial distribution of submarine
groundwater discharge. Geophys. Res. Lett. 2006a; 33: L06605, doi:10.1029/2005GL025288.
Taniguchi M, Ishitobi T, Shimada J. Dynamics of submarine groundwater discharge and freshwaterseawater interface. J. Geophys. Res. 2006b; 111: C01008, doi:10.1029/2005JC002924.
Taniguchi M, Burnett WC, Cable JE, Turner JV. Investigation of submarine groundwater discharge,
Hydrol. Process. 2002; 16: 2115-2129.
Kim G, Hwang DW. Tidal pumping of groundwater into the coastal ocean revealed from submarine
222Rn and CH4 monitoring. Geophys. Res. Lett. 2002; 29: doi:10.1029/2002GL015093.
Robinson C, Li L, Barry DA. Effect of tidal forcing on a subterranean estuary. Advances in Water
Resources 2007; 30(4): 851-865.
Burnett WC, Aggarwal PK, Aureli A, Bokuniewicz H, Cable JE, Charette MA, Kontar E, Krupa S,
Kulkarni K M, Loveless A, Moore WS, Oberdorfer JA, Oliveira J, Ozyurt N, Povinec P, Privitera A
MG, Rajar R, Ramessur RT, Scholten J, Stieglitz T, Taniguchi M, Turner JV. Quantifying submarine
groundwater discharge in the coastal zone via multiple methods. Science of the Total Environment
2006; 367: 498–543.
Taniguchi M, Burnett WC, Cable JE, Turner JV. Assessment methodologies for submarine groundwater
discharge, M. Taniguchi et al eds: Land and Marine Hydrogeology, Elsevier. 2003; 1-24.
Taniguchi M, Ono M, Takahashi M. Multi-scale evaluations of submarine groundwater discharge, IAHS
publication 2014; 365: 66-71, doi:10.5194/piahs-365-66-2015

the Nexus using
integrated
Water Balance model
Groundwater
Model(SHER)
Kita river

Flow Model(SEAWAT)(213km2)Minamiriver Data from Water(224km )balance model(SHER)↓
3D Groundwater
Flow
2

Increase in groundwater pumping inland↓
Decrease in Submarine Groundwater
Discharge↓
Decrease in Nutrients（P, N, Si）into
the ocean↓
Decrease in Chl-a & primary
production
Unjo spring↓
Groundwater Pumping Decrease in fisheries
Coast
3D salinity
distribution
in theSGD
coastal zone Salinity (kg/m3)（3D GW model)

Obama: 3D GW model

Kwon EY, Kim G, Primeau F, Moore WS, Cho H-Mi, DeVries T, Sarmiento JL, Charette MA, Cho Y-Ki.
Global estimate of submarine groundwater discharge based on an observationally constrained radiumisotope model, Geophys. Res. Lett. 2014; DOI: 10.1002/2014GL061574.
Sugimoto R, Honda H, Kobayashi S, Takao Y, Tahara D, Tominaga O, Taniguchi M. Seasonal Changes inSubmarine Groundwater Discharge and Associated Nutrient Transport into a Tideless Semi-enclosed
Embayment (Obama Bay, Japan), Estuaries and Coasts 2016; 39: 13–26.
Utsunomiya T, Hata M, Sugimoto R, Honda H, Kobayashi S, Tominaga O, Shoji J, Taniguchi M. Higher
species richness and abundance of fish and benthicinvertebrates around submarine groundwater
discharge in Obama Bay, Japan. J Hydrol. in press; DOI: doi:10.1016/j.ejrh.2015.11.012.
Hosono T, Ono M, Burnett WC, Tokunaga T, Taniguchi M, Akimichi T. Spatial distribution of submarine
groundwater discharge and associated nutrients within a local coastal area. Environmental Science &Technology 2012; 46: 5319-5326.
Taniguchi, M. Groundwater and subsurface environments: Human impacts in Asian coastal cities,
Springer, pp.3-18.
Taniguchi M, Allen D, Gurdak J. Optimizing the Water-Energy-Food Nexus in the Asia-Pacific Ring ofFire, EOS 2013; 94(47): 435.
Taniguchi M, Shiraiwa T. Dilemma of the Boundaries: a new concept for the catchment, Global
Environmental Studies No.2, Springer. 2012.

Global estimate of SGD (Kwog et al., 2014)

850

L3: Coastal Water Circulation
Akihide Kasai (Graduate School of Fisheries Sciences, Hokkaido University)

Abstract
There are many factors that control coastal water circulation. The strength of tidal
currents, river runoff, meteorological conditions (winds) and flow pattern of the outer sea
are particularly important, as well as shoreline structure and bottom topography. In general,
the salinity of coastal water is low and variable, with surface waters showing reduced levels
compared with the open ocean, because of the freshwater input via river discharges andgroundwater influx from lands. However, as each coastal area has its own characteristics,
circulation pattern in the coastal basin varies from one locality to another. It also changes
with time, because the factors fluctuate with various time scales.
Coastal area of California is famous for upwelling. Northwesterly winds blowing
parallel to the coastline drive surface water to the right of the wind flow (westward)
through the Ekman transport. Deep cold water upwells to compensate the offshore
movement of surface water. This region draws considerable attention not only because of
the physical process, but also its importance in generating high production. The deep cold
waters contain a lot of nutrients and thus stimulate primary production in the euphotic layer.
The coastal area of California is thus one of the most productive regions on earth.
In the regions of freshwater influence, surface lighter water flows toward outer sea,
while bottom denser water flows toward inner bay. This is called ‘estuarine circulation’, atype of density currents. The main force that drives the estuarine circulation is a horizontalpressure gradient caused by the density difference between the fresh water from rivers andthe salty water from outer seas. As the water flux by the estuarine circulation is several tomore than 10 times larger than the river discharge, it significantly influences waterexchange between inner and outer seas and material circulation.
Tides are most familiar as a rise and fall of seawater level once or twice a day in littoral
area. Tides considerably change the strength and direction of currents in coastal waters. The
tidal currents often dominate coastal circulation, and determine the strength of water
stratification. Tides in Ariake Bay are the largest in Japan because of the resonance of tidal
wave, while those in the Sea of Japan are generally small because the tidal waves damp at
the entrance of the sea (Tsushima strait).
The combined effects of winds, freshwater discharge, tidal currents, and oceanic forces
result in a unique circulation in each coastal area. It is important to conduct a preliminary
survey and find out the characteristics of the target area, if you want to know the water
circulation.

51

52

Ocean circulation in the surface layer

29 Nov. 2016 Coastal Vulnerability and Freshwater Discharge

Western boundary current ：1‐2 m/s

Coastal Water Circulation
Akihide Kasai(Graduate School of Fisheries Sciences,
Hokkaido University)

Tides

Sea surface temperature

🌕

🌗

🌑

🌓

1m

Hakodate
2015年

Maizuru
0.3m

0

10

20

Ohmuta30 (oC)

4m(From NASDA HP）

2015年

Lower
high
Hakodate

HigherLow

Prediction of tide in Osaka Bay

Higher high tide

Diurnal Inequality
Lower Low

OhmutaTidal range

531

Tidal resonance
M2+S2

Tidal Wave
x

0

-1

Standing wave in a bay

Tidal ellipse

Primary tidal components

K2
S2
M2
N2
K1
P1
O1

description

periodLuni-solar
Principal solar
Principal lunar
Larger lunar elliptic
Luni-solar diurnal
Principal solar diurnal
Principal lunar diurnal

11h58m12h00m12h25m12h39m23h56m24h04m25h49mamplitude
(cm)
4.36
16.88
29.71
6.34
26.06
8.00
19.60

Phase
(°)
227.50227.82215.47209.64203.77201.91
181.31※Amplitudes and phase are in Osaka Bay

Amplitude of M2 tide

Circulation and temperature in Ise Bay

水温

Log10 (H/U3)：Strength of stratification(Yanagi and Higuchi, 1981)(Yanagi, 1990)
SST in Summer

54

2

Circulation model

Upwelling caused by alongshore wind

Ekman drift

Upwelling

Estuarine circulation

Upwelling of hypoxic water

Small tide

Large tide

Freshwater
SeawaterSalt wedge

Front in Akkeshi Bay

Apr. 2006
100

Seawater intrusion distance (km)Yura River Estuary

20

Summer Winter

20
15
10
5
0
A

J

2006

May 2007
20

10
0Distance (km)Well mixed

30

20

A O D

F

A

10

J

A O D

F

2008

2007

0

Salinity

55

3

56

L4: Nutrient Dynamics
Yu Umezawa(Graduate School of Fisheries and Environmental Sciences, Nagasaki University)

Abstract
Understanding nutrient dynamics (i.e., nutrient concentration of each species,
distribution, source, and movement etc.) in aquatic ecosystem are indispensable, when we
study about phytoplankton species composition, primary production, and environmental
problems such as red tide and green tide at coastal areas and lakes. In the oligotorphic
waters at pelagic ocean and coral reefs, nutrients are tightly recycled in the ecosystems.
Then, remineralization from particulate and dissolved organic matter (POM, DOM) is also
important nutrient sources, as well as nitrogen supply through nitrogen fixation. Some
species of phytoplankton have higher ability to uptake DOM for their growth. Therefore,
POM and DOM are also categorized into nutrients in a broad sense, although component of
nutrients are generally nitrate (NO3-), nitrite (NO2-), ammonium (NH4+), phosphate (PO43-)
and silicate (SiO44-) in a narrow sense. The analytical protocol of POM and DOM arebriefly lectured in this lecture.
In the coastal areas and marginal seas, main nutrient sources are terrestrial water
including groundwater, atmospheric depositions, upwelling waters and currents fromadjacent areas. To identify the source of nutrients, stable isotopes techniques are often
effectively used as well as other physical parameters (e.g., salinity and temperature), whenstable isotopes values of nutrients from each source are distinctively different. Nutrient
dynamics based on stable isotopes are introduced with examples of studies at groundwaterin Asian mega cities, East China Sea, and Coral Reefs.
Stable isotopes techniques are also applicable to check actual uptake of each source ofnutrients by the primary producers. For example, chemical compositions in macroalgal

tissue record time-averaged information of water quality at the exact locations
during algal growing period, because most of macroalgae are growing at exact
locations, and uptake nutrients only from water column. Therefore, stable nitrogenisotope (δ15N) and nitrogen contents (%) in macroalgal tissue are effectively used to
trace nutrient sources for the primary producers at shallow coastal areas.
Transplanting experiments using macroalgae and bivalves in cages and associatedshift of chemical composition in their tissue are also introduced in this lecture as aneffective tool to check spatial variation in nutrient dynamics.

57

58

Atmospheric Transportation

Nutrients Flowat Coastal Areas

Nutrient Dynamics

Air Pollution

Ecosystem
Degradation
Yu Umezawa

River water

Nutrient
Dynamics

SGD

Groundwater（Marine Biogeochemistry Lab. Faculty of Fisheries)
International Hydrological Programme
Coastal Vulnerability and Freshwater Discharge
The Twenty six IHP Training Course

Reconstruction ofpollution history

Pollution in Freshwater

Representative Nitrogen Compounds in Seawater

C, N, P flow through the food web dynamics（Dissolved Matter： < 0.2 ~ 0.7 μM）
Ammonium
DIN (Dissolved Inorganic Nitrogen)…. Nitrate,- Nitrite,
+

従属栄養
鞭毛虫類

Heterotrophic
flagellates

PIN (Particulate Organic Nitrogen)…. (Organism、Detritus、etc. )PON (Particulate Inorganic Nitrogen)….

DIN

DIP

SiO44- Trace Metals
Vitamins

Terrestrial PON
DON
DIN

N2
DON
DIN

PON/DON

Decomposition

NH4+

DON

Diffusion

Nitrification

DIP
(SRP)

O=P-(OH)2

O=P-O(O-1) n

Orthophosphate monoester

DOP

NO3-

NO3-

O=P-(OH)2

Apatite, Clay, Oxyhydroxides

Decomposition

MixingDiffusion

Sinking

Pyrophosphate (diphosphoric acid)
Polyphosphate

NH4+

> 90%

O=P-(OH)3-O-

DIP

Advection
Amino acid
Protein for
Photosynthesis(NO, NO2, N2O etc.)

Representative Phosphorus Compounds in Seawater（Dissolved Matter： < 0.2 ~ 0.7 μM）
Orthophosphate

Assimilation

N2（Nonreactive・Stable）….
（Reactive・Unstable）….

Nitrogen (N) Cycle

NO3-(Mineral、etc. )Gaseous molecule

River & Groundwater,
Microbial Loop Atmosphere, Sediment, Upwelling

Atmospheric

NH4 )（Particulate Matter： > 0.2 ~ 0.7 μ
μM ）

POM

従属栄養
バクテリア

DOC DON DOPNO2DON(Dissolved Organic Nitrogen)…. (Amino acids、Urea、Amino sugars etc. )

カイアシ類

繊毛虫類

Phytoplankton
植物プランクトン

Heterotrophic
Bacteria( NO3

Copepods

Ciliates

・・・Intermediate metabolite during biosynthesis

Orthophosphate diester
・・・Phosphodiester bond in phospholipids, ATP・DNA・RNA

Upwelling

Phosphonate

N2
N2O（Particulate Matter： > 0.2 ~ 0.7 μM ）PIP (Particulate Inorganic Phosphorus)…. (Mineral、etc. )Denitrification

POP (Particulate Organic Phosphorus)…. (Organism、Detritus、etc. )59

Atmospheric
Terrestrial

POP
DOP
DIP

POP
DIP DOP

Coloring reaction of nitrite

Phosphorus (P) Cycle?
Advection

Assimilation

O – P –O

膜リン脂質(PL)DNA/RNA
糖リン酸 フィチン酸
糖リン酸、フィチン酸

O

Hydrolysis
C-O-P
O-P + H2O
C-P

DOP

Sinking

MixingDiffusion

POP
Decomposition

Sulfanilamide–

ATP/ADP/AMP

O

–

PO43-

Upwelling
N-1-Naphthylethylenediamine
Dihydrochloride

Diffusion

Concentration is calculated based on the intensity of color

Nutrients are analyzed by colorimetric method

Coloring reaction of phosphate

AbsorbanceAcidic condition
Lamp

Phosphate

MolybdateDiazo compound

DIP

Hydrolysis
Fe(OOH)PAzo compound

APase

Molybdenum complex（Mo(IV)）Mo(IV)）

Detectionsa

Reduction byascorbic acid
Reagent 1
Reagent 2
Air

Molybdenum complex（Mo(V)）
Mo(V)）

SampleConcentration is calculated based on the intensity of color

Ready-madecommercial productHand-madeassembling productStable, automatedc.a. 80,000 US $ for 2 ch.
General Nut. Anal.
Detection limit(0.05-0.1 μM)

Flexible,c.a. 30,000 US $ for 2 ch.
Nanomolar Nut. Anal.
Detection limit(0.005-0.01 μM)

Patey et al. (2008) TRACS

On board continuous analyses of nanomolar nutrients in surface water
expanded the understanding of nutrient dynamics in pelagic water

Hashihama et al. (2009) GRL

60

Average composition of nitrogen pools (except dissolved N2)
In open ocean (surface & deep), coastal & estuarine waters

Importance of DOM, POM andremineralized nutrients
Remineralized nutrients through the decompositionof POM and DOM support primary production at
ologotrophic waters.

Berman & Bronk (2003) MEPS

Composition of each form of N and P in the bay

DON pool dominates the surface ocean fixed N reservoir,
comprising
up to 96-99% of (TDN) in the oligotrophic ocean
海洋に存在する溶存態窒素の存在形態の場所別比較
A

B

C

D

25

North Pacific subtropical gyre

60 N

20

40 N

C

30 N

PO4 (DIP)

10

0
1.4

TON
DIN

≒10.0
Mouth of Bay

 Organic N and P aredominant form
 POM decrease offshore ,while DOM are almost
constant throughout the bay

Middle area

δ13C (‰)

A6

A8

A9

A11 A13 A15

Umezawa et al. (2015)

DOC: DOP ratio
Innermost
Mouth of Bay

40

100

20
Middle1012

3

4 5 6 7 8 10

0.01

0.05 0.1 0.2

10
0.5 1

DON (μM)
DOP (μM)
The quality of POM decreases from innermost area to themouth of bay, in the viewpoint of the source of DIN & DIP

Typical range of Marine Phytoplankton

-17

A4

Surface

Bottom

-19

A1

100
80
Mouth
of Bay60

Mouth of bay

Middle area0
Typical range ofriver-derived POM

P

Innermost

≒7.0

-21

A11 A13 A15

C/N, C/P ratios increase with distance from the river

POC: PON ratio
POC: PON ratio

-23

A9

0.4
0

80
60

-25

A6

DOC: DON ratio

Innermost area

20

A1

Innermost area

Umezawa et al. (2014)

40

0.6

DO
OC (μM)

Δδ13C：- 0.1～-0.2 (‰/day)Δδ15N：- 0.2～-0.5 (‰/day)

100

0.8

Abell et al. (2000) J. Mar Res

Shift of δ13C and δ15N along the decomposition

120

A8

A6
A4

0.2

Identification of POC based on δ13C value
Chikugo River
（n=3）

A4

POP
DOP
DIP

1

P(μM)

N (μM
μM))

DON pool dominates the surface
ocean fixed N reservoir, comprising
up to 96-99% of (TDN) in theoligotrophic ocean

N(Ariake Sea, Japan, September, 2013)
A1

1.2

A

0

PON
DON
DIN

5

B

20 N

POC connc. (μM)

15

A15
A13
A11
A9
A8

DO
OC (μM)

P (μM
μM))

D

N(μM)

TOP

50 N

10 N

Mouth of Bay ～ Inner area of Bay-15

Ozaki (2016)

Ozaki (2016)

61

Incubation Experiments: Nutrients fluxes released
through the decomposition of POM and DOM

Nutrients fluxes released through the decomposition of OM
Releassed DIN（μMμM/day/day））

Seawater in SCM layer collected from innermost area,
area, middle area and mouth of thebay in Ariake Sea were incubated in dark, and water quality was monitored .
(t=0, 1, 3, 7, 14, 30 days)
days)

Dark, in situ temp.

A. Decomposition rate of
Seawaterr

all of organic matter
Filtration

B. Decomposition rate ofdissolved organic matter(200 μm)

C. Decomposition rate of(0.7μm)湾央(July)(July)

Innermost area

湾口
(橘湾)

（September）(July)（September）

Mouth of bay

A－B =

GF/F

湾奥

（September）

Middle area

Released DIP（μMμM/day/day））
Umezawa et al. (2015)

particulate organic matter・Nutrients release rate are high at the innermost bay area
・NP ratio of released nutrients are low than that of source OM

Evaluation of remineralized nutrientsavailable for phytoplankton

DIN/P：c.a. 10
10--15）（DIN/P：（POM
POM：：c.a.35、DOM: c.a. 2525-35）

Released DINP
P/ Organic N, P

Spatial variation of DIN, P release rate per OM
NitrogenA

Phosphorus

Nutrient Dynamics in the East China Sea

B

0.4
LabileOrganic Matter

0.3

C

74%

Most damaged ecosystem by human activities

02
0.2

60%

0.1

8%

5%

0.0

St. A B C

Refractory
Organic Matter

St. A B C

Umezawa et al. (2015)

The ratio of labile component in the organic matter at themouth of bay is 55-10% of that at the innermost area of the bay

Geographic characteristics of the ECS～Broad Continental Shelf ～Marginal Seas in East Asia
RussiaSea of Okhotsk

Mongolia(< 200 m)

Continental Shelf(1/30 of the C.S. in the world)
Huanghe River

China

Japan Sea((East Sea))
Japan
Yellow Sea

Changjiang River

East China Sea

Okinawa Trough(1500-2000 m)

Pacific Ocean

South China Sea

62

Nagasaki

The North Sea
ECS & Surrounding coastal areas

Eastern Caribbean

Halpern et al. (2008) Science

Cumulative human impact in the world

How to evaluate vulnerability of ecosystem?

ECS is important area as hatchery & nursery

負荷要因 重み付け 生態系応答
Factors Weighting Response of Ecosystem
Biodiversity
Vulnerability of the ecosystem
(Di)
(Ui)
(Ej)
水温
Temperature

生態系脆弱度(I)
生態系脆弱度(I)
Human Impact
Categories

積算的な影響Cumulative Impacts

航路
Boat Damage

SSandd area

漁業
Over fishing

Rocky shore

Japanese jack mackerel

砂地

岩礁

n

積算的な要因汚濁
Pollutionsm

I = ∑ ∑Di*Ej*Ui, j
i =1 j=1

Summed Drivers

藻場

Seagrass bed

Japanese anchovyDO( 参考: Halpern et al. 2008）(Seikai National Fisheries Research Inst. )Seasonal change of NO3- distribution

Fish Catch (1000 t)

400
350
300
250

Fish Catch (1000 t)

Decrease of fishery resources at ECS
6,000

6000

Summer

Winter

5,000

5000

4,000

4000

3,000

3000

2,000

2000

1,000

1000

0

High
g NO3- (>25)
High PO43- (>1.6)
High SiO2 (>30)

0

1956

1966

1976

1

1986

1996

2006

2013

Chen et al. (1997)

High NO3River discharge

中国渔业统计年鉴

200

NE Monsoon
Low NO3- (<0.2)
N-fixing area

(KW)
Low NO3- (<0.2)
Low PO43- (<0.05)
Low SiO2 (<3)

150High NO3Upwelling

100
50

水産庁HP

Kuroshio Water (KW)SCS Intermediate Water (SCSIW)(350 – 1350 m)

01947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

63

(SCSIW)
Low NO3- (<2.0)
Low PO43- (<0.2)
Arthur--Chen (2008) JO
ArthurLow SiO2 (<5)

Increase of annual nutrients discharge through CJ River

Impact of terrestrial material input from China

Primary production at
ECS is highly supported
by material fluxes from
China.
Phytoplankton
植物プランクトン
Catchment of CJ river
N fertilizer

33

International Symposium on Coastal

Jan. 19-20, 2011

33

Wang et al., 2014

Resources, Nagasaki
Chlorophyll a distribution simulated
based on satellite data & model

Hadson Bay

Increase of fertilizer input & discharge excess N
Efficiency of fertilizer uptake have decreased with an increase of fertilizer N application

Gulf of St. Lawrence

作物として収穫さされた窒素量（t）

2000

North Sea

Baltic Sea

Laptev Sea

Kara Sea

Sea of Okhotsk

X 19901980
G. of Mexico

East China Sea
Bay of Bengal

Gulf of Thailand

Black Sea

Jan. 19-20, 2011 Sea
Mediterranean

Liu et al. (2006) Biogeochemistry

Phytoplankton Biomass & Potential Fish Production

DIN: Dissolved Inorganic Nitrogen
DIP: Dissolved Inorganic Phosphorus
DON: Dissolved Organic Nitrogen

DIP
0.68
3.54
1.47
2.55
1.28
0.29
0.22
0.14
0.47
0.09
0.50
2.44
0.58

DON
7.5
51.8
10.5
25.3
17.1
17.5
6.1
11.8
17.1
7.8
3.3
13.8
8.5

DOP
0.22
1.58
0.38
1.03
0.54
0.47
0.17
0.27
0.42
0.17
0.10
0.47
0.21

PN
4.7
99.0
8.8
71.6
29.7
5.3
8.9
6.7
18.7
10.6
4.5
7.4
8.7

PP
0.86
24.37
1.75
13.72
5.96
0.96
1.74
1.21
3.51
1.98
1.06
1.40
1.63

CDW area (km2) or Carbo
on Fixed (tonsC d-1)

based on NEWS model (Seitzinger et al., 2005)
DIN
19.8
222.5
23.6
122.7
77.2
15.7
10.4
8.3
16.0
3.4
11.9
58.4
19.9

36

Yangtze River Floods Enhance Coastal Ocean
Annualshift of nutrient composition in Yangtze River

Riverine nutrient fluxes (Gmol/yr) at Major Marginal Sea
Name
Baltic Sea
Bay of Bengal
Black Sea
East China Sea
Gulf of Mexico
Gulf of St. Lawrence
Gulf of Thailand
Hudson Bay
Kara Sea
Laptev Sea
Northern Adriatic Sea
North Sea
Sea of Okhotsk

International Symposium on Coastal
Resources, Nagasaki

POC
49
1396
100
783
340
55
100
69
200
113
61
80
93

DOP: Dissolved Organic Phosphorus
PN: Particulate Nitrogen PP: Particulate Phosphorus
POC: Particulate Organic Carbon（Riverwater Discharge）（Fixed carbon volume
by phytoplankton）（Area affected by
Changjiang Diluted
water ）

River water Disccharge (m3 s-1)

施肥された窒素量（t）

Gong et al. (2011)64

Annual variation of Phytoplankton Biomass & Potential
Fish Production, due to the different discharge rate.
July 1998

July 2004

Annual shift of nutrient composition in Changjiang River

July 2007

July 2010

DIP：Phosphorus

Si/N
N ratio

July 2009

Si / NDIP coonc. (μM)
N/P
P ratio

July 2008

DIN, Si conc. (μM)

Si : Silicate

N/P

DIN：Nitrogen
1960

1970

1980

1990

1960

2000

River-derived N & P loadings
are increasing in the ECS, while
Si input is decreasing.

Gong et al. (2011)

1970

1980

1990

2000

River-derived N loadings
relatively increase (Higher
N/P ratio)

Wang et al. (2006) ECS

Does nutrient shift change phytoplankton species?

Atmospheric N deposition strengthen N-rich condition
Shift of N* (= [DIN] – 13x[DIP]) during 10 years

DiatomN Excess（Chaetoceros affine ）silicate demand
w/ vacuole for nutrients stock

Dinoflagellate（ Prorocentrum dentatum ）N depletion

Coccolithophorid（ Emiliania huxleyi.）Higher affinity to nutrients

Kim et al. (2011) Science

Observed cell density of dinoflagellate is increasing in the ECS

Mixotrophs
Free swimming

Eutrophication causes heavy hypoxia in river mouth

Maximum cell density of P. Dentatum in each research cruise

P. Dentatum（Hokkaido Univ）

Kiyomoto et al. (2013)

Li & Daler (2003) AMBIO

65

Yangtze River Floods Enhance Coastal Ocean
Phytoplankton Biomass & Potential Fish Production

Changjiang River derived Nutrients Enhance Coastal
Phytoplankton Biomass & Potential Fish Production ??

Healthy Condition
Phytoplankton Production

River – derived nutrients

Deteriorated Condition

Excess nutrients inputs

Red tide
Micro- & Macro
MicroMacro-algal bloom

密度躍層

×

Pycnocline

Extra Organic Matter

Potential Fish Production
Decomposition
Respiration

CO2 + H2O

×

CH2O + O2

Hypoxia

Marginal Seas categorized by forcing function
ThermohalineDeep
Ice
Con Formvection ation

River
RunoffWind

Tropical
StormsTides

Various forcing functions and physical factors
influencing nutrient dynamics

Bound- ExternalaryForcing
Currents

Red Sea

5

5

0

0

3

0

1

0

0

Med. Sea

5

5

0

1

1

0

1

0

0

Black Sea

3

0

0

5

3

0

1

0

0

G. of Mexico

1

0

0

3

1

3

1

5

0

Japan Sea

3

3

3

1

3

1

1

3

1

Indonesian Seas 1
South China Sea 1

0

0

1

1

1

3

3

5

0

0

1

3

5

?

?

3

Caribbean Sea

1

0

0

1

3

3

1

3

3

Yellow Sea

1

0

1

3

5

1

5

0

1

East China Sea

1

0

0

1

3

3

1

5

1

Sea of Okhotsk

3

3

3

3

3

0

3

1

3

Bering Sea

3

3

3

1

3

0

3

0

3

[ 0 = Not Happening; 1 = Minor Factor; 3 = Important; 5 = Dominant Factor]
Ramp. S.R. (1997)

http://www.environmentservices.com/projects/programs/RedSeaCD/DATA/definitions.htm

Box-model input/output of the nutrients at ECS

Upwelling occurs by the meander/frontal eddy events

夏季

Yellow Sea -0.01
-0.3
黄海
-0.03

冬季

0.4
0.2
0.01

Atmospheric deposition

大気降下物
Changjian River

(長江）

2.9
27.6
3.7

台湾暖流水

66

2.1
0.5
0.01
1
1
1

6.7
76.7
13.1

Taiwan Current
Warm Water

Michel, J. (ed.). 2013.

1
1
1

0.1
1.0
0.1

SCS
Zhang et al (2007) Prog Oceanogr

4.8
43.8
5.1

Outflow

流出

18.8
223
24.5
8.1
29.7
10.6

ECS

Burial or export as POM

Sediment

堆積物

0.4
1.9
0.6

Kuroshio Subsurface Water

黒潮亜表層水

1.2
4.2
1.8

2.0
29.5
1.6

DIN
DIP
DISi
14.1
153
17.3

[NO3-] can be controlled by the N source & Redox conditions

Nitrate and ammonium contaminationin the groundwater of Asian megacities

Oxic condition

Nitrification

Warm and humid climate condition throughout theyear may enhance the bacterial denitrification,
resulting in reduced nitrate contamination.

NO3-

Fertilizer

NH4+

Atmosphericdepositions

Anoxic condition

SewageFertilizer

Denitrification(symbols:IAN.umces.edu/)

Geological Setting and sampling locations

The relations between nutrients and land use

Shallow GW( < 100m )ManilaDeep GW( > 100m )ManilaRiver Water

Spring Water

Bangkok
Old flood or tidal plain

JakartaBangkok

Urban Area

Volcanic
Alluvial Fan

Jakarta

Paddy Fields

NH4+

NH4+

NH4+
NH4+
NO3Sedimentary rocks
0

10

20

30 km

0

60

Volcanic
Basement rocks
120 180 km
0NO3-

Nitrate + Nitrite
Ammonium
45

30

45 km

Umezawa et al. (2009) STOTEN

Typical ranges of δ15N・δ18O in NO3- from various sources~ 800 μM WHO standard for drinking water

Nitrate δ15N・δ18O values in groundwater and river water
Minor contribution of atmospheric NO3-

NO3- in Precipitation
Automobile
ExhaustDry Fields

Fossil FuelBurning

Severe contribution of sewage derived NO3-

Taipei
Shallow GW
Deep GWRiver water
Rain water

Bangkok
Shallow GW
Deep GWRiver water

NO3- Fertilizer

Jakarta
Shallow GW
Deep GWRiver water
Spring water

Active Occurrence of denitrification!
denitrification!

Nitrification of NH4+ in fertilizer Manure and Septic Waste

ManilaKendall (1998)

Umezawa et al. (2009) STOTEN

67

Shallow GW
Deep GWRiver water
Rain water

YSCWM(Yellow Sea Cold
Water Mass)

Identification of NO3 sourcein the continental shelf of ECS

February, 2009
March, 2012
July, 2011

黄海冷水塊
（黄海混合水）CDW(Changjiang

The sources of NO3, which were actually
incorporated into phytoplankton, are identified
based on δ15N and δ18O in NO3 in the water column

Diluted water)

SMW

長江希釈水

KSW~KSSW~KIW(Shelf Mixed Water)(Kuroshio Surface Water)

大陸棚水

黒潮表層水
黒潮亜表層水
黒潮中層水

TWCW

Monitored Parameters(Taiwan Warm
Current Water)

CTD, DO, Chl. Fluorescence,
Nutrients, POC/N, DOC/N/P, Chl.a
Chl.a

台湾暖流水

Temperature- Salinity diagram in July, 2011

δ15N = 6.5–7.5‰
δ18O = 5.0–7.0‰

YSCWM
黄海冷水塊

YSCWM

CDW

CDW

SMW

長江希釈水

δ15N = 2.0–8.3‰
δ18O = 1.9
1.9–2.6
2.6‰

KSSW

大陸棚水
TWCW

Liu et al (2010) Env. Sci. Tech
Chen et al. (2013) Acta Ocean Sin

黒潮表層水
黒潮亜表層水黒潮中層水

TWCW

25 CDW

長江希釈水

大陸棚水

SMW

15
10

YSCWM
5

23.0

δ15N = 5.5–6.0‰
δ18O = 3.5–4.0‰

Rainwater

30

CCW

P. Temperature ( °
C)

SMW

KSSW

TWCW

30

P. Temperature ( °
C)

YSMW

25
20
15

台湾暖流水

黒潮表層水

TWCW

KSW

SMW

CCW

YSMW

TWCW

32.0

32.5

33.0

KSW

33.5

34.0

黒潮中層水

SMW

35.0

Salinity

KSSW

CCW

What’s actual contributionof eacheach-source NO3 tophytoplankton growth?

33.5

Salinity

34.0

34.5

Upwelling N
0

2

4

6

δ15N

KIW
33.0

Terrestrial N

15

0

32.5

Mixing

20

5

YSMW
32.0

25

10

10
5

Atmospheric N

30

20
15

34.0

* Conceptual model (not real example)

KIW
34.5

33.0

NO3 の起源と、個々の起源の寄与率の算出

KSSW

黄海混合水

5

32.0

Characteristics of δ15N (& δ18O) in NO3
Identification of the NO3 sources, &contribution of each

黒潮亜表層水

中国沿岸水

31.0

 Developed YSCWM & CDW

大陸棚水

10

March,, 2012

25

Feb., 2009

30.0

Salinity

δ15N = 0.4±2.9‰
δ18O = 73.3±9.8‰

δ15N = ? ‰δ18O = ? ‰

KIW

黄海冷水塊

29.0

July, 2011

δ18O

TWCW
台湾暖流水

KSSW

20

〜
〜

KSW
KSSW
KIW

台湾暖流水

30

P. Temmperature ( °C)（黄海混合水）

35.0

68

8

10

Liu & Kaplan (1989) L&O
Liu et al. (1996) Mar. Chem.
Leichter et al. (2007) L&O
Sugimoto et al. (2009a, b) CSS
etc.

Characteristics of δ15N (& δ18O) in NO3

Characteristics of δ15N (& δ18O) in NO3

Evidence of actual uptake by phytoplankton

Evidence of actual uptake by phytoplankton

or denitrifying bacteria

or denitrifying bacteria

Rayleigh fractionation -model
Substrate(DIN)
left in the system

15ε:18ε = 1:1

δ15N（‰）

δ15N（‰）

Substrate(DIN)
left in the system

δ15N in D
DIN (PON)

Rayleigh fractionation -model

Product(Phytoplankton)

Product(Phytoplankton)

Reaction (f)ε = -2 ~ -5

Reaction (f)LN [NO3]
Linear relationship between δ15NNO3 & Ln[NO
Ln[NO3]
Actual uptake of NO3 by primary producers

Granger et al. (2004) L&O

Characteristics of δ15N (& δ18O) in NO3

δ15N & δ18O in NO3 observed in summer
Chl. maximum at surface & subsurface layers in the SMWContribution of Nitrification and/or N2-fixation*

25

PON w/ lighter δ15N(δ18O)

Decomposition

Nitrification

NO3 in Rainwater N2 fixation

July, 2011

δ18ONO3

NO3

NH4

20

δ15N in D
DIN (PON)

lighter δ15N(δ18O)

*

15

*
* *

10

YSCWM
5 Changjiang
Riv. Estur.

0

LN [NO3]Lighter δ15N

0

[NO3 ] & δ15NNO3 on the cont. shelf of ECS
25.0

20.0
3

δ15NNO

3 m Surface water

3m

10 m

15.0
10.0

Subsurface*

65 m
Okinawa Trough30 m
16 m25 m**?

ChangjiangRiver
Li et al. (2010)

5.0
0.0
-1.0

Diluted KSSW

0.0

Logarithmicallyconverted [NO3]

100 m

1.0

ln[NO3]2.0

600
600--780 m

3.0

4.0

February, 2009

25

YSMW

KSW

Chl. Fluorescence

10.0

10 m
0m

*
0-30 m *Li et al. (2010)0-30 mDilution**

50 m

YSMW 500 m

75 m

Diluted KSSW

0.0
-1.0

0.0

1.0

ln[NO3]

Umezawa et al. (2014) Biogeosciences

69

KSSW

5.0
15.0

5.0

KSSW
Dilution

20

20.0

30 m

YSCWM

15

3

*

Mixing of YSCWM & CDW-NO3
YSCWM
Contribution of lighter-NO3
Nitrification, N2-fixation?
KSSW
Chl. Fluorescenceδ15NNO

27.5 m10[NO3 ] & δ15N in NO3 at CK line (February, 2009)

CDW

July, 20115

δ15NNO3
The increase ratio of δ18O and δ15N from source value was 1:1
Fractionation associated with NO3 uptake by phytoplankton

Deviation from the expected linear relationship
Possibility of Nitrification & N2-fixation

25.0

Changjiang Riv.

130--780 m
KSSW 130

2.0

Changjiang
River

KSSW
Dilution

3.0

4.0

Umezawa et al. (2014) Biogeosciences

32 29 33 25
46 49
40 5537
7
21
47
17
42
50
1
45 39
52
6
30
28 5
5
51
34
24
31
48

Identification of NO3 sourcein the rainwater
Application of δ15N and δ18O in NO3 and otherchemical components in atmospheric depositions

Route of air mass
bringing rain at
Nagasaki
54

15
16

58
64
Continental air
1 mass62
60
Oceanic air mass
13

Lesser100
contribution
of OH

22
14

19

18

61
9

56
11

10

20

分布

Global frequency and distribution of lightning@ Nagasaki

Fossil fuelburning59

頻度

Identification of rainwaterrainwater-NO3 source based on δ15N & δ18O

12

23
4

63 57

Lightning

3
27

4

Automobile exhausts

7/
3
5
2011
16
23
1
2011////10/
10/
3/
15
1/
14
11/
25
12/
9/ 23
29
11
12
8
6/
22
7
10/
20
2011
16
18
2/
12
6
8/
11
20
10
18
23
514
8
5/
2012
4/
5/
15
13
25
27
3
24
2011
6/
21
7/
30
4

53

δ18O of N
NO380（Fossil
F il FuelF l Burning）
Burning
B i ）60（Automobile exhaust?）
exhaust?）40

Higher20
contribution
-4
of OH

from Continent
from Ocean
Domestic
Observation from space by the Optical Transient Detector
-2

0

2

4

6

Contribution of Lightning to NO3 in rainwater was at Taipei, higher than Nagasaki!

δ15N of NO3

M.A. Cooper (University of Illinois )Effective tool to trace the source of minerals
β decay

87Rb
Radio isotopes（Half life: 50 billion years）
years）

87Sr

Stable Isotopesp

86Sr
Continental crust
Old bedrock

High

Recent volcanic rock
87Sr
86Sr

Soils in continent

87Sr/86Sr in sillicate mineral

Strontium

87Sr/86Sr reveal that flying minerals in summer are the Japan origin

Small

Soils in Japan
Spring70

SummerAutumnWinterSpringWhat is benefit of chemical
contents in macroalgal tissues
as an indicator ofenvironmental condition?

Effective tools to monitor
time-averaged nutrient conditions

50%

Kabira Reef

5.1

Terrestrial N

δ15N (‰)
5.0 ~ 20.0

2.4
3.0

DIN iin U
Upwellinglli

2.6

δ15N = 1-4 (‰)

3.2

5.5
3.5 3.4
2.5
3.2
4.7 3.93.63.6
5.2
4.14.13.3

Land

Shiraho Reef
Higher N loading※ Lower exchange ratio(Longer turnover time)※δ15N = 0.0 (‰)

3.9

5.8

PO43PO43-

N-Fixation

3.6
5.7 4.0
4.6 3.2
2.8
4.3 3.5 2.92.02.7

3.9
41
4.1
37
3.7
5.3 4.3 4.1 3.63.3 3.4
4.6

Ishigaki Is. (Japan)

Lower N loading※ Higher exchange ratio(Shorter turnover time)0%+6.0 +5.0 +4.0 +3.0

δ15N
N(%)

※

Contribution of terrestrial N to algal N demands

100 %

NO3-

+

Detection of the impact of humanhuman-derived Non marine ecosystems based on N stable isotopes(Okinawa, Japan)

NH4+ +NH4NH4・ Actual use of primary producers
PO43・ Reflect nutrient condition in the water column
・ Nutrienti
condition
di i at theh exact locationli・ Time-averaged info. during the growing period・ N content (%) and δ15N values reflect the extent
of N supplies and N sources.

～δ15N in macroalgae &sedimentary organic matter～matter～Shiraho Reef

NO3-

Atmospheric N

The impact of terrestrial N on the reef
ecosystem will be enhanced at Shiraho Reef

δ15N = -15 ~ 3 (‰)

Offshore

Algal δ15N values clearly show the dispersion of land-derived
nutrients as time-integrated infomation

Umezawa et al. (2002) L&O

Spatial distribution of δ15N in brown macroalgae(Padina spp., Dictyota sp.) at Kabira & Shiraho Reef
Shiraho Reef
Kabira Reef

Both N loadings and turnover time affect the influential area of land-N

Shiraho Reef

+3.0
2.6
2.32.4
2.9 2.7
2.5
2.9 2.5

+4.0
+5.0

2.9

1000(m)2.4
2.7 2.734
3.4
27
2.7 2.7
4.0
5.2 4.3 3.32.9 2.6
2.8
4.4 3.8
4.4
3.8°
27.9 N● Dictyota sp. ●

°
8.1 E

3.9
4.1
3.7
3.3 3.4
4 1 3.63.3
4.1
55.33 4.3
4.64 3

2.6

2.8
3.8

4.0 3.6
5.1 5.74.6 3.2
2.8 2.92.0
4.3 3.5
2.7

800

2.2
2.9

600

23
2.3
2.4
2.2 2.4
2.3
2.5 2.3
3.0 3.0 5.8
4.5
5.7

400

Developed
Reef Crest

2.4
3.0

Land

2.6

200

Kabira Reef

3.5 3.4
4.7 3.9 3.63.6
3.2
5.2
4.14.1 3.3

5.8● Dictyota sp. ●

°
8.8 E

°
15.0 E

0=

Inner Reef

Land

Low N flux

2.5

Offshore

Terrestrial Influential Area

High Mixing ratio

Sharp decrease=0

Padina sp.

Mild decrease

3.2

3.9
5.5

The gradients of δ15N distributions

Low Mixing ratio

δ15NHigh N flux

δ15N

+6.0 +5.0 +4.0 +3.0°
28.6 N

Padina sp.
400°

800(m)15.6 E

Developed
Reef Crest

The area, where waste water-derived N influenced to macroalgae,
were limited at Kabira Reef compared with Shiraho Reef.

Land

Offshore

Terrestrial Influential Area

71

Sedimentary organic matter have potential to show similar
characteristics to macroalgae due to benthic micro algal contribution

δ15N of macroalgae and sediment OM can be used in a complementarymanner, over various time scales, as indicator of the integrated effect of DIN
sources
15

Distribution of δ N in SOM at both Kabira & Shiraho Reefs

Shallow water depth& Clear water
Increase contribution ofbenthic algae to sedimentaryorganic matter

Kabira Reef
+3.0
Sharp decrease

+4.0
+5.0

NO3-

NO3-

Shiraho Reef
+6.0 +5.0 +4.0+3.0

2.5 2.8
2.2 2.3
2.0 2.4
2.2
2.9
2.0
3.5
5.4
δ15N value

1000(m)Mild decrease

800
600

7.0

3.4
4.9 3.9 3.9 4.2
3.2
2.9
3.7
4.9 4.6 4.3

400

0δ15N value0δ15N of macroalgae and sediment OM can be used in a complementarymanner, over various time scales, as indicator of the integrated effect ofterrestrial DIN
Macroalgae

Shiraho Reef

3.5

200

400

800(m)

Alternative indicator to check
check N sources for primary producers
Deploy the macroalgae with low δ15N in equal distance through the reef area, to gettime-integrated information for nutrient sources for primary producers.

(the period macroalgae
recorded nutrient regime:
2-3 weeks)

3.0 (‰ δ15N)
0.8 (%-N)

Sedimentc.a. 1 week
Sedimentaryy OrganicgMatter(the period SOM recorded
nutrient regime:
2-3 months or more)

3.0 (‰ δ15N)
0.7 (%-N)
Oligotrophic water(offshore water)

7.0 (‰ δ15N)
1.2 (%-N)
Eutrophic water(sewage seepage)

Macroalgae

Photo: Jennifer Smith
Umezawa et al. (2008) JO

As indicator of the time-integrated information on DIN
sources, δ15N of wild or manually incubated macroalgae
and sedimentary OM can be used in a complementarymanner, over various time scales, according to the
purpose.

Alternative indicator to check N sources for primary producers
This technique is applicable to everywhere in the shallow water system!

Buoy

δ15N analyses

2.7-2.9

2.2-2.4 2.7-3.4
3.0-3.2
4.3-5.2

3.4
2.5-3.4
2.6

Algal species & biomass

1.9

4.0
3.3

Plate

2.5
4.2
3.9 2.4

3.4
1.3
2.9 3.5 3.5
2.6-5.8
2.7 3.5

Higher algal δ15N at offshore reef may be caused by the use ofnutrient through the groundwater, which seep out from the faults
in the reef, as also suggested by 222Rn & resistivity surveys.

72

Anthropogenic contaminants into aquatic ecosystems

Reconstruction of the history of nutrient
dynamics (source and eutrophication)

FieldsPastureResidence area
Heavy Industry
Chemical Industry
Cr Hg Pb CN

The use of chemical components
in the sediment core samples

NitrateHgg PCB

NitrateTrichloroethane

Carbon Tetrachloride
Benzene Selenium
Trichloroethylene

PCB Fluorine Boron

Simazine(Herbicide)

Laundry
Gas Station

Pb

RefineryPower Plant

Benzene Dichloroethylene
Trichloroethylene
TetrachloroethyleneAs(symbols:IAN.umces.edu/)

Variation of δ15N value in N compounds

Variation of δ13C value in C compounds
δ13C in organic matter and DIC(Dissolved Inorganic Carbon)

δ15N in organic matter and DIN(Dissolved Inorganic Nitrogen)

Organic matter in soils
High latitude← Organic matter in soils →low latitude
Zooplankton~ Vertebrate

Fertilizer
NH4+ in Rainwater

Macroalgae

Human & domestic animals wastes

C3 pplants, MangrovesgNO3- in RainwaterC4 pplants, CAM plants、SeagrasspgPhytoplankton (Freshwater)
Phytoplankton (Ocean)

Nitrogen Fixation
DIC(Ocean)Atmospheric CO2

HCO3-

HeavierLighter-10

DIC (Dissolved Inorganic Carbon)(Freshwater)

-5

0

5

+10

+15

+20

δ15N（‰）

+25

+30

-45

Winter

-30

-25

-20

-15

δ13C（‰）-10-5

Study Site

（Higher temp. and light）
Increase inphotosynthesis

NO3
NH4

3
2 ■●
◆
1

★
★

1 ◆ ■2 ●3

◆
■ 2 3
★
●

★

★

多量

δ13C or δ15N （‰）

1

Relatively
lower δ13C
lower δ15N

δ of DIC & DIN
remaining in water
δ ofprimary producers

Relatively
higher δ13C
higher δ15N

f : fraction of completed reaction

73

0

Boutton (1991)
Yoneyama (2008)

Summer

DIN
少量

-35

Kendall (1998)

Response of δ13C & δ15N in primary producers to growth rate
（Higher temp. and light）
Decrease inphotosynthesis

-40

Osaka

ManilaArea (km2)

a1,530d1,700

Jakartaf514

Width of the mouth (km)a4 + 7d22

37.5

Ave. Depth (m)

a27.5

d17

k15

Volume (Km3)

a41.8

d28.9

－

Residence Timea5 month

e31 days－

Closeness Index

3.6

1.9-4.1

0.6

HeavyMetalsin Osakain Bay
Heavy
metalscontamination
Osaka Bay

History of pollutants in Osaka Bay
C (%)

N(%)

P (%)

Zi (ppm)δ13C(‰) δ15N(‰)

C/NCu (ppm) Pb (ppm) 206Pb/207Pb

Phytoplankton
bloom?

都市型公害
被害の減少

1970

1960
1900

1900

都市型公害
(e.g. 鉛ガソリン)

1900

Waste water?
& Phytoplankton bloom?
低レベル汚染(鉱山の影響)Umezawa et al. (in prep)

Hosono et al. (in prep)

Population growth in Asian urban areas

Heavy metals contamination in Jakarta Bay
Zn (ppm) Cu (ppm) Pb (ppm) 206Pb/207Pb

2000

1970
Modified Kaneko (2007)
Proc. of Inter. Symp.

Period when organic matteraccumulation started

Osaka
Period when heavy metals
accumulation started
Osaka

Hosono et al. (2011)

N load / LLand area

Estimated N loads with rapid economic growthat each country from 1961 to 2020

10

Japan
(Osaka)Indonesia(Jakarta)

Jakarta

ManilaAccumulation of pollutants in the sediment of the inner bay areaffected by the water retention time, in addition to the loadings.

Semi--enclosed Bay Index
Semi

Philippines(Manila)(Ministry of the Environment, Japan: 1993)

S : Surface area (km2)
W: Width at the mouth of the bay (km)D1: Deepest depth within the bay (m)D2: Deepest depth at the mouth (m)

8
6

S W

4
2
0
1961 1980 2000 2020

Jakarta Manila

Index =√S X DW X D2

1961 1980 2000 2020 1961 1980 2000 2020

Nle : N loads from energy production
NLw : N loads from human waste (based on food consumption)
NLlv : N loads from livestock waste
NLc : N loads from fertilizer input
Shindo et al. (2006) Ecol Model
NLn : N loads from Forest & Grass lands

D1

74

D2

1

Osaka = 3.9
Manila = 1.9 ~ 4.1
Jakarta = 0.6

Semi-Semi
enclosedBay

1.0

L5: Plankton Ecosystem
Joji Ishizaka(Institute for Space-Earth Environmental Research, Nagoya University)

Abstract
Freshwater discharge influences to plankton ecosystem of the coastal area.

Freshwateris less saline and often low density, and the buoyancy effect makes the low salinity water is
not easily mixed with seawater; however, entrainment and tidal pumping eventually mix
the upper low salinity water with lower high salinity water.

Freshwater discharge also

supplies various organic and inorganic materials to coastal water.
makes coastal water productive.
activities by human.

Loading of nutrients

Coastal area is also known as high population andLarge amount of nutrients loading and many constructions in shallow

areas often caused eutrophication, red tides, hypoxia and other problems in the coastal area.
In this lecture, examples of changes of plankton ecosystem in coastal area byanthropogenic activities will be given.

As the observation tool, satellite remote sensing

technology is also summarized, and examples of the usage will be shown.

One of theexamples is the East China Sea which is the radically changing environment. One of thelargest river in the world, Changjiang, influences to phytoplankton abundance and thetaxonomic groups in the East China Sea.

Increasing anthropogenic nutrient loading aswell as large sediment discharge and accumulated sediment seems to be very important for
the ecosystem.

75

76

Plankton$Ecosystem

Joji$Ishizaka$$$
Nagoya$$University$$
jishizaka@$nagoyaBu.jp

Contents
• Plankton$ecosystem$and$freshwater$
discharge$• Satellite$ObservaLon$• East$China$Sea/Yellow$Sea$and$Changjiang$
River$Discharge$• Phytoplankton$Community$VariaLon$• SatelliteBbased$phytoplankton$Community$

IHP$
2016.12.5

CharacterisLcs$of$Satellite$Remote$
Sensing;$

Food
Chain

• SynopLc$Coverage$of$Large$Area$• Frequent$and$Steady$Time$Coverage$• Only$Surface$InformaLon$• Limited$Parameters$
Lalli and Parsons• CombinaLon$with$Ship$and$Buoy$
ObservaLons$is$necessary$

Phytoplankton

e

Methods$of$Ocean$Remote$SensingOcean$ObservaLons$by$Satellite$$• Passive$Visible$(Ocean$Color,$Bo[om$CondiLon);$• Passive$Infrared$SST);
• Passive$Microwave$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$(Wind$Velocity,$Rain,$SST,$SSS,$Ice);$;;;;;;;;;;;;;;;;;;;;;;;;;;;;
• AcLve$Microwave;

• Sea$Surface$Temperature$• Ocean$Color9Phytoplankton,$SS,$CDOM$• Sea$Ice;$• Sea$Surface$Roughness$(Oil$Slick,$Internal$Wave)$Sca[erometer$(Sea$Surface$Wind$DirecLon$&$Velocity)
AlLmeter$(Sea$Surface$Height,$Geostrophic$Current);
SyntheLc$Aperture$Rader$$$$$$$$$$$$$$$(Sea$Surface$Roughness$B$Oil$Spill,$Internal$Wave)$• Sea$Surface$Height$(Current);$• Sea$Surface$Salinity;$• Sea$Surface$Wind$;9Presentely$diﬃcult$to$use$for$coastal$area$77

Ocean&Color&Remote&SensingSun

Primary$ProducLon()B$rateBC$:ChlBa,$Temperature,$Solar$RadiaLon$;B< < C$

Optical Observation from Satellite

Satellite

Atmospheric Correction
CloudMolecule (PressureAerosol

Atmosphere

Temperature

ChlBa

Remote Sensing Reflectance Sea

Scattering

Absorption

Suspended
MatterColored Dissolved
Organic Matter

Phytoplankton
Chlorophyll a

Surface
In-Water Algorithm
Chlorophyll f (Rrs)

Solar$RadiaLon

Primary$ProducLon

Chlorophyll a Concentration

Problems in YECS and possible causes
• Red tide (Choclodinium polykrikoides) 1988• Red tide (Prorocentram shikokuense) 2000• Giant Jellyfish (Nemopilema nomurai) 2002• Green tide (Enteromorpha prolifera) 2008• Eutrophication (River, Atmos.)• Climate Change• Dam Construction• Overfishing• MoreNutrient$Increase$in$Changjiang$River8
BWang$et$al.,$2006C
Si

Iwataki

N/P

DIN
DIP

Si/N

Chugoku News

Increase$of$Nitrate$:$Change$of$Changjiang$
Discharge$
DEutrophicaLon$

Development$$of$New$Algorithm
Standard CHL('%

(Siswanto$et$al.$$
J.$Oceanogr.$2011)
$Climatology$in$January$$$(Yamaguchi$et$al.$$Cont.$Shelf$Res.$2013)$(Siswanto et al., 2008)
1



1

Eutrophication

Climate based Change of
Changjian Discharge)&  "$!/--,4,2+,

#*2;;;

;;

78

#*2

Seasonal Change of New Chl-a(10 year average)

Seasonal Variation of
Chl-a and TSM (Yamaguchi et al.

34

#607

2013,
Cont. Shelf Res.)(Yamaguchi
et al.
Cont. Shelf
Res. 2013)&+5

YOC CHL

Spring Bloom

CDW

TSM

1.1Interannual$VariaLon$$
of$Satellite$ChlBa$
During$Summer$

50

50

CDW ChlBa

45

40

40

40

35
130

135

140

130

45

45

40

40

40

35

35
135

140

50

130

Sep.

45

45

45

40

40

40

35

35
130

135

135

140

130

Nov.
45

45

40

40

40

135

140

135

140

0.1

35

35
130

140

Dec.

45

35

135

50

50

Oct.



35
130

140

140

Seasonal
Chlorophyll-ain JapanSea)

50

Aug.

50

135

35
130

140

140

Jun.

45

135

135

50

May.

Apr.

Jul.

130

135

140

130

1
Chlorophyll a (µg l-1)

Phytoplankton
1mm E
0.0007mm

Internannual Change of Chl.-a
in Japan Sea

>@A?

135

50

130

=

35
130

140

50

50

Early
(El Nino)

Mar.

45

35

(Yamaguchi$et$al.$
Prog.Oceanogr.$2012)

50

Feb.

Jan.
45

Late
(La Nina)=>@A?

79

10

Cochlodinium&
polykrikoides;Red$Tide

Transport&of&C.&
polykrikoides&(2003)

Iwataki

Micro

Micro

TS:
Typical characteristics
of global ocean
Nano

Nano

ECS:
No clear tendencies(a)0.16
Ave$of aph(λ)$(mB1)$

Phytoplankton$AbsorpLon

Clear difference between TS and ECS

TS_S (N=13)
TS_SCM (N=12)(N=12)ECS_S (N=67)
ECS_SCM (N=51)(N=51)

0.12
0.08
0.04

Magnitude
Distinct peaksin the blue
and red band

0.00
400

450

500

550

600

650

700

ECS_SCM
ECS_S
TS_SCM
TS_S

Pico
High
proportion(b)0.10
Ave&of aph*(λ)$(m2$mgB1)$

Pico

ChlorophyllBa$Speciﬁc$Phytoplankton$AbsorpLon

Wavelength (nm)TS_S (N=13)
TS_SCM (N=12)(N=12)ECS_S (N=67)
ECS_SCM (N=51)(N=51)

0.08
0.06

Distinct difference both inmagnitude and spectrum shape
TS_S

0.04

Magnitude atblue bands

0.02
0.00
400

450

500

550

600

650

700

TS_SCM

Wavelength (nm)(Wang et al., BG-2013)(Wang et al., BG-2013)

Verification of
Absorption-based Phytoplankton Sizefrom in situ Remote Sensing ReflectanceIn situRrs(λ)QAA V. 5

PCA modelaph(λ)

Lee et al.
(2002, 2009)

MODIS bands 1-6(412-547 nm);minus values at
7-10 bands

ECS_S
ECS_SCM

Absorptionbased
PhytoplanktonSizemethodTchl a size
fractions

Chl a

Micro

Nano

Pico

July
2011
Pixels with minus aph(443) were removed

(Wang et al., Opt. Express-2015)80

L6: Influence to Fisheries
Satoshi Ishikawa (Research Department, Research Institute for Humanity and Nature)

Abstract
In East Asia including Southeast Asian countries, approximate 72 % of 2 billion people living inrural and urban areas in coastal zone. Livelihoods in rural area are based on various ecosystemservices provided from coastal nature that has high productivity and biodiversity, e.g.,
Mangrove trees are utilized for building materials and fuels, fisheries resources has importantroles as protein and income sources. On the other hands, residents in urban area need somefoods from rural areas, and a market in a city is quite important for both rural and urban people.
Therefore, the connectivity and logistics between of them and economic activities are also
important elements when we think the sustainable developments in a coastal area.
Land use change associated with urbanization affects on freshwater discharges, andsubsequently on carrying capacity and biodiversity in coastal area, because most part ofminerals and materials for primary production being provided from lands with freshwater.
Besides, chemical contamination and increase of bacteria of freshwater in urban area can
endanger the food safety of fisheries products in coastal area. Keep sea food safety is quite
important for economic growth and improve quality of life in coastal zone.
Fisheries resource management is indispensable for sustainable development in coastal area.
However, high biodiversity and multiple fishing gears utilization make statistical data collectiondifficult, even it is necessary for stock assessments. In addition, conservation of coastal habitatsof fisheries species are required for reproduction of the resources. Therefore, alternative way ofstock assessments and simultaneous conservation activities on coastal ecosystems are needed. In
this connection, community based fisheries resource utilization with scientific evaluation of
ecosystem health under collaboration among fisher folks, researchers and local governments areproposed as a new approach for coastal development, names as “Area-capability (AC)”approach.
In the AC approach, “care” of a target resources and its habitat, not management, is treated as
major activity. The care includes three aspects as follow, 1) cultivate interests on nature
supporting the target resources, 2) monitor the stock status based on daily utilization of theresources, 3) cure actions on injured part of nature. Biological and ecological researches cancontribute to the cultivation of the interest of users, monitor of stock status and evaluation ofcure activities. The balance between community based utilization and care is most important incoastal development. And the care of habitats should touch material flow, sanitation, logistics,
biodiversity, carrying capacity, cultural diversity, and education with human resource81development. This comprehensive development strengthen the resilience of areas against natural
disasters.

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Influence to Fisheries
in Southeast Asian coastal area

In East Asia
including ASEAN ,72% of 2 billion people
live in Coastal Zone.
• In rural area, many
residents conducts fisheryand fishery related
activity as main jobs.
• Urban residents needsome foods from ruralarea.

Research Institute for Humanity and Nature

Satoshi ISHIKAWA

Markets in Urban area
are necessary for Rural
fishery.

Urbanization changeriver flows, subsequently
material flows to coastal
ecosystems.

Connectivity and linkage
between urban and ruralshould be taken into
account in sustainable
development in Coastal
Zone.

Land use changes also
affects on fisheries
resources status andreproductions.

Garbage and waste
control and sanitation
improvements link towater quality and sea
food safety, and Prices
Values of them.

Fisheries Resource
managements and Food
Safety control arenecessary for sustainable
development in coastal
zone.
Regarding the linkage
between land and coastal
ecosystems via waterflows, quantity, andquality should be taken
into account.

However, the differencesof nature and society
should be taken into
account to come up withthe management andcontrol measures.

83

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Common Fisheries Resource Management

Crisis of Ocean Ecosystems

1995 FAO http://www.fao.org/fishery/ccrf/1/en
Code of Conduct for Responsible Fishery

Fishing down in Global trend…?

Empty Net

(Emerson, 1994)

Statistical Data
Collection

Fishing Down

Stock
Assessment

Regulation
Fishing Activity

Resource
Management(Pauly et.al, 1998 )Are the Oceans Dying?
(Newsweek, 2002)

Empty Oceans

International Commission ofseveral important Fish Resources

We cannot eat any fish in 2048

Data Collection, Stock Evaluation of Particular Fish
Limits of total catch amount of each species(Hayden, 2003)(Worm et.al, 2006)

Large‐scale Commercial Fishery can be applied.

Different Ecosystem and Society in Tropical Zone
Temperate Zone

Other issues for sustainable fishery in Asia

Tropical Zone

Single‐Species, Selective Fishery

Multi‐Species, Multi‐Fishery, Mixed Catch

Sum MSYs = Total MSY

Sum MSYs = Total MSY

Haphazardly

Over Fishing

Illegal Activity

Bad Waste Management

Deterioration of
Ecosystem Services

Inefficient Use

Vicious Cycle
Deterioration of
Productivity, Livelihoods
Aggravation of Poverty

Professional Fishery

Economic Value is Top Priority
New Approach based on Tropical
Ecosystem features is necessary

Many Fishery and Stakeholders

Various importanceof Natural Resources

Merely target resource managements
based on regulation of fishing activities are not function.

Area-capability Cycle

Towards sustainable developmentof East Asia

Evaluation
by Researchers• Conservation and Care actions for Marine
Ecosystems are necessary!
• However, conservation actions is usually not
attractive for coastal people.
• And imposed care activity dose not havesustainability.

New Utilization Development

Development User Community

Effective Utilization of the Resource
Resource

Evaluation
by Researchers

User Community

Habitat HealthCapability Enhanced

Primary production,
Biomass, Pollution,
Material flow,
Biodiversity

Driving
Force
Pride, Hopeand Anticipation

Social Capital,
Economic linkage,Compliance,
Autonomy,
Management

Care for Ecosystem HealthCare Activities

Care should be packaged with utilization

Understand Importanceof Caring

Cultivate Interest for
the Ecosystem

For decision‐making process at local coastal communities
based scientific information

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Framework of Implementation and
Evaluation in Area-capability concept

Hamana Lake

Stock enhancement of thetiger shrimp in Hamana Lake

User Community
Utilizations & Care Activities
Monitors & Managements

Evaluation
AdviceTechnical
supports

Governments
License,Legitimacy
Autonomy
Financial
Supports

Data
Information
Consultation

Collaborative
Activity
Outside Experts
Analyses & Evaluations

Water area = 6,880ha•
Periphery = 128km
Depth = 5m•
10th in Japan

What changes occurred?
1978
1979
1980

1981
1983

1985

What changes occurred?

No appropriate aquaculture
technology.

Stock enhancement Center Established and the
public project of larval prawn release started.
Environmental information
Intermediate aquaculture of larvae failed.
and stock status are quite
Researcher conducted catch data collection,
limited. Researcher could not
prawn size measurement at landing sites,
decide the release point andExamined the appropriate bait for prawn larvae, timing, and evaluate theclarified food web around prawn, determined the impact of the project
best size for release, determined the best Scientific data were collected byplace and month for release, andresearcher, but not utilized by fishermen
continue the intermediate aquaculture.
Young fishermen of Shirasu village started
No gain from release work
collaboration. And First Release (ca. 3million).
but collaboration stated
Second release (more than 10 million)
Murakushi, Iride, Yutho joined the collaboration
Washizu Maisaka Arai joined the collaboration
Prawn catch increased. They
Public project terminated

1983

1985
1990

Prof. Fushimi, H

1.
2.
3.
4.

Involvement of other sectors
Unify Fishermen Associations

Resource

User Community

Increase prawn Stock

Driving
Force

Increase incomes
Decrease conflicts

Hope, PrideEnvironmental
Monitoring
by Fishers

All activities were done by
Fishermen

Work together is Key to improve confidence of
scientific data.

New technology is key for establishment of new community.
Community activities make scientific data collection possible.
Scientific data improve community understanding of nature.
Evaluation from others sustains community activities.

1. Data of the important resources can attract users interests.
2. Collaboration of users and researchers enhance users’
understanding of scientific data and information.
3. Improvement of livelihoods foster conservation minds andactions of users.
4. Evidences of resource improvement sustains community
conservation activities.

Capability Enhanced

Care for Ecosystem HealthRelease Juveniles
By governmental
project

Conflict decreased.
Trade system improved.
Incomes increased.
They knew lake environments

From Case studies,

Effective Utilization of the Resource

Habitat HealthPublic project terminatedRelease activity were carried over by fishermen
14million release
Release activity is continued by fishers.

He was a researcher
at SE center and took
in charge of theprawn release wrok.

Area‐capability cycle of stock enhancement of Kuruma prawn in
Hamana Lake

Stop Small prawn catch

Second release (more than 10 million)
Murakushi, Iride, Yutho joined the collaboration
Washizu Maisaka Arai joined the collaboration”After everything was finished,” fishers knew how and what
to do for Kuruma prawn intermediate culture. There was noneed to explain anything to them.” (Prof. Fushimi said)

realized the impact of
release.

Improvement of market accessibility

There are traditional and modern fisheries, including both
capture and aquaculture activities.
Fishermen have been conducted as commercial activity but insmall scale.

Collaboration ofFishers andResearchers

Release Juveniles
By Fishermen

These events and phenomenon are closely linked.

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Set‐net (Teichi‐ami)Community based Set‐net fishery

Slope net

Final Chamber
Chamber net

Fish Court(Surrounding net)

Length80 ‐ 120 m

Width
20m

Depth 15m

Entrance
Leader Net
Passive Type of Gear, fixed in costal water
Large‐scale trap net with compound design
Waiting for the migrating fish schools,
and entrapping them in the chamber net

By TD/SEAFDEC and DOF/Thailand in Rayong

Location of Rayong set-net project

200m

Location of Rayong set-net project

Small scale fishery

By fisher groupconflicts

2006
2003 By researcher

Commercial fishery

Problems and Difficulties
in the 1st year

History of Set‐net Installation into Thailand Strong current
Bad Design of Net
 Anchors entangled with net
Bad Operation of fishing

2001

ASEAN‐SEAFDEC Millennium Conferenceon Sustainable Fisheries for Food Security
in the Region in Bangkok
2002
International Set‐Net Fishing Summit in Himi
2003‐2005 Rayong Set‐Net Project
SEAFDEC‐ EMDEC‐Fishermen Group
2005‐2007 JICA grass‐root project by SEAFDEC, TUMSAT and Himi City
Got Idea
2007‐Present Operated and Managed by fishers group
Start Project!
2008 Training course for Practices and Theories
Choko‐ami in Chonburi, by Kasetsart Univ.
2010 New challenge in Southern Gulf of Thailand
steered by DOF, Thailand

Fish Catch was not good!

SEAFDEC: Southeast Asian Fisheries Development Center
Mr. Aussanee Munprasit
JICA: Japan International Cooperation Agency
Senior Officer of SEAFDECTUMSAT: Tokyo University of Marine Science and Technology, JapanEMDEC: Eastern Marine Fisheries Research and Development Center, Department of Fishery, Thailand
Himi city: Located in Toyama Prefecture, in Japan Very famous as Set‐net fishery86

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Gear Improvement since 2nd year漁具設計改良
1. Modified to slimmer style

身網の設計変更

1st year2ndyear

45 x 140 x 250 m

Fishing Operation in Rayong
 Man‐power hauling Morning hauling Every 2 days
 3‐4 boats, 10‐12 fishermen
 0.5‐1.5 hours
20‐30 min
 September‐April

20 x 155 x 250 m

45

Himi 2

2. Developed to narrower and lower slope&entrance net昇り網の設計変更(Unit : m)

1st year; 5.0x3.0x14

2nd year; 0.7x8.0x8.0

3rd year; 0.7x9.0x8.0

The Catch

Daily average catch and value
1200

Variety ofcatch

Average catch (kg) and value (THB)

Most of set‐net catch arepelagic species, variety of thecatch showed an importantcharacteristics of the coastal
fisheries resources.

They caught different species by set‐net from that they collected by small gears.

Highquality

Average catch (Kg)
Average value (x10 THB)
trip

1000

1041

1001

782

800
634
600

551

516

400

200

352
175

210

254
101

52

288

298

110

98

91

225

214

108

86

0

2003‐2004

2004‐2005

2005‐2006

2006‐2007

2007‐2008

2008‐20092009‐2010

1

2

3

4

5

6

7

Management Skill of them was improved through selling and pooling system

Year of Project Implementation
28

Mean Trophic Level

4.0, for gear design / fishing ground depth
Mean Trophic Level Comparison

ACC from Development of utilization methods:
Community-based set-net fishery in Rayong, Thailand.

Set‐net

THAILAND

3.5

Community Fish Market

3.22 (Deep water bamboo stake trap)
3.15 (Shallow water bamboo stake trap)

3.0

猪口網

2004

Trad Deep water bamboo
stake trap

2005

Year

Chonburi Deep waterbamboo stake trap

2% 8%
4%

2%

2006

63%

10%

8-10 m depth(Aussanee et al., 2005)

Post
HarvestTechnology

2008

2%

Effective Utilization of Resource

New Target Species
1%11%

Chub mackerel

Valued fish

58%

29%

Driving
ForcesEnhanced Capability

Hopes, Prides

Carangidae

29%

53%

Local Community

Improved Status

Sriracha set‐net 猪口網

1% 2%

62%

Establishment of
Fishers Community

Resource
Pangnga Shallow waterbamboo stake trap

24%
23%

2007

Improvement of Trading system

Set‐Net Installation
by SEAFDEC

3.04 (Sriracha set-net)

2.5
2003

Involvement of Other sectors

Tourism Activity

Care for Ecosystems

Other

New Markets,
Decrease conflicts
Management
Skill
Operational
Skill

Trash fishes

14%

2-3 m depth

1%

ShrimpEnvironmental andFood Web estimation

4-5 m depth

Fishery Statistical
data collection

Cultivate interest
for Ecosystem

29

87

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Progresses of Stock Enhancement
Stock Enhancement••••

Local Government participation
Local Community Participation
Implementation Discussion
Base Line Survey on Livelihoods• Build Intermediate Aquaculture Ponds• First Trial of Community‐based Aquaculture• Environmental Assessment to determine the release points

1. To prepare and evaluate a stockenhancement protocol for New
Washington
2. To assess impacts of stockenhancement trials and community
activities

Results: Timeline of rearing and releases

Results: Catch monitoring (Trader’s logbook)2013 July: 1st rearing (129,000 stocked)
Aug: No release (high mortality)

Recapture info (5th batch)2014 Feb: 2nd rearing (390,000 stocked)
April: 15,000 released (4%); 100 tagged

Released:
May 2015
Total released: 250,000
Shrimp tagged: 250 (0.1%)

2014 June: 3rd rearing (270,000 stocked)
July: 120,000 released (44%); 240 tagged

Total recovery: 20 pcs (8%)*
Details: July (8 pcs), 5 g (3 %)Aug (4 pcs), 15 g (1.6%)
Sep-Oct (8), 55 g (3%)

2014 Nov: 4th rearing (400,000 stocked)
Dec: Series of typhoons caused mortality
2015 Apr: 5th rearing (483,000 stocked)
May: 250,000 released (51%); 250 tagged

Estimated total potential harvest:(4 months after release):
1,100 kg (PhP220,000)

2015 Jun: 6th rearing (630,000 stocked)
Jul: high mortality caused by early
prolonged rains and cannibalism

Fishers want to start other release works by themselves.
Like Hamana Lake.

Altamirano et al, 2015 33

Altamirano et al, 2015 34

Expected ACC From Community-based Stock
Enhancement of Tiger prawn
Improvement of market accessibility
Community –based Aquaculture

Increasing Risks of natural disasters

Involvement of other sectors
Fishermen Associations

Effective Utilization of Resource
Resource

Local Community

Improved Status
Increase prawn Stock

Driving
ForcesEnhanced Capability

Biodiversity
Loss

Increase incomes
Decrease conflicts

Hope, Pride

Care for Ecosystems
Release Juveniles
By research project

Environmental
Monitoring
by Fishers

Rockström et al., 2009, Planetary boundaries: exploring
the safe operating space for humanity. Ecology andSociety 14(2), 32

Increase Public
Awareness ofHabitats importanceSuper Typhoon attacked Panay Is, Philippines,
November 2013,

We cannot stop this change. We should adapt this change.
We have to change our lives.
How??

Release Juveniles
By Fishermen &government

88

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Impacts on livelihoods

Impacts on tiger prawn resources
Before typhoon

After typhoon

Results: Survival of shrimps

Impacts on shrimp growth and mortality rate

Low salinity put stress on shrimp

STR
NOR

Survival (%)

STRESSDURINGTYPHOON

NORMAL

Survival rate drastically decreased

ACC
ACCLIMATE

Heavy rain by “Yolanda” derive low salinity in the coastal habitats.
Days of Culture

Typhoon and heavy rain cause bad impacts both on
growth and survival rate of shrimps

Results: Growth of shrimps
ACC

STR

NOR

ACCLIMATE

STRESSDURINGTYPHOON(NORMAL)

1st (Jul 2013)

2nd (Mar 2014)[1st]
0.34 g[2nd]
0.40 gat Day 30

at Day 30(no typhoon)

BW (g)

0.09 gat Day 30(w/ typhoon)

heavy rainDays of Culture

3rd (Jun 2014)

4th (Nov 2014)

typhoon[3rd]
0.81 g[4th]
0.61 gat Day 30(no typhoon)

at Day 30(no typhoon)

0.30 gGrowth rates were also deteriorated after Typhoon.

at Day 30(w/ typhoon)89

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2016/10/5

From Sustainability To Capability

Fishery is a Safety Net in rural area
• After 2 days from Yolanda Passed,
fish market was sold fish andshrimps.
• This small scale fishery provided
food and incomes to local
residences.

Tree

Land

Fish

LandscapeOne person

Fishery

Agriculture

Architecture

Food
Processing

Education

Trade

Tourism

Boat Builder

Factory(Photos, 8:29am, 9th Nov. 2013)

Increasing AC cycles= Sustainable Development= High resilience against disasters

Numbers of AC cycles show
numbers of resources,
local communities,
utilization methods, jobs
and cares on ecosystems.

Effective Utilization
User
Community

Resource
Increased Stock¾ Efforts to keep sustainability ofeach resource are enormous.

z ACC is established for each resource.
z One Person get a place on pluralcommunities according resources use.
z If one resource is deteriorated, other
ACC can support his live.
z Cares for habitats in ACC contributes
non‐target resource reproductions.

This fact should be taken into
account for disaster management.

Pollution, Habitats
Biomass, Biodiversity¾ Under this occasion, eachresource is small and vulnerable.

Aquaculture

Coastal ecosystem and small scale
fishery have important roles assafety net for rural area.

Driving
Force
Increased¾ In high bio‐cultural diversity area,
People utilize a lot of resources
for many purposes.

Water

Resources

Improvement of QoL

Academic research can contribute to create Area‐capability cycle in collaboration with local
community. This is one of the solution towardsustainable development in coastal areas.

Capability Enhanced

Care for Ecosystem
As AC cycle is drown at each resource, number of it shows number of resources

Transdisciplinary Research

They can find their roles and places in the evaluation
processes of Ecosystem health and Society in AC cycle

Solution Oriented Academic activity

Area-capability Cycle

User Community
Utilizations & Care Activities
Monitors & Managements

Evaluation
AdviceTechnical
supports

Collaborative
Activity
Outside Experts

Data
Information
Consultation

Evaluation
by Researchers

Governments
License,Legitimacy
Autonomy
Financial
Supports

New Utilization Development

Development User Community

Effective Utilization of the Resource
Resource

User Community

Habitat HealthCapability Enhanced

Primary production,
Biomass, Pollution,
Material flow,
Biodiversity

Analyses & Evaluations

Evaluation
by Researchers

Driving
Force
Pride, Hopeand Anticipation

Social Capital,
Economic linkage,Compliance,
Autonomy,
Management

Care for Ecosystem HealthIn some times, this collaboration is quite difficult for Scientist,because they can not find their roles based on theirrepartees.

Care Activities

90

Understand Importanceof Caring

Cultivate Interest for
the Ecosystem

8

2016/10/5

Biological
CatchStudyfrom

Anthoropologic Study and Robotics

Rayongset-netYellow stripe scads

Round herring

Gar fish

Indian threadfish

Trevally

Spanish mackerel

Black pomfret

Barracuda

Indian mackerel

SailfishRabbitfish

Emperer

Sea bream

Threadfin bream

Threadfin bream

2 Town Seminars, 2 Lectures of environment, 1 Exhibition at Museum

Indo-pacific mackerel

Yellowstrip scad

Hairtail

Wolf-herring

Snapper

SardineLeather jacket

Leather jacket

Blue swimming crab

Bigfinreef squid

Loligo squid

Cuttlefish

2 Education Program, Submarine Robot Development, Environmental Survey
Collect data about behavior changes and information sharing through interviews

49

Chemical and Material flow Study
Stable Isotope Analysis show different units ofSiganus javus in Bandon Bay

Shall we start Area‐capability study
around the world?
For our future!!

Siganus javus

δ15N (‰)
East Area

West area
Central Area

δ13C (‰)

Thank you for your attentions!

91

9

92

L7: A Japanese experience of Tsunamis
Takashi Tomita(Education and Research Center for Sustainable Co-Development, Graduate School ofEnvironmental Studies, Nagoya University)
Abstract
Japan have learned many lessons from the 2011 Tohoku tsunami disaster: for example, multi-level
scenarios for disaster management and multi-layered measures to reduce possible disasters. Throughinternational cooperative research project, the Japanese experiences of tsunami disasters have been
introduced into Chile that is also in high risk areas of tsunamis. These Japanese experiences will be
introduced in the seminar.

93

94

What I want to talk
• Disaster risk reduction can provide not only to
reduce mortality, the number of affected people,
and economic loss, but also to create
sustainable society and economy.
• Structural infrastructure is a measure to reducetsunami impact to society and economy, while
the priority measure to protect human life fromtsunamis is evacuation.
• To develop structural infrastructure for tsunami
disaster mitigation, tsunami damage to them is
illustrated and how to plan and design them is
introduced.

L7: Tsunami and Disaster Prevention
Takashi Tomita
Graduate School of Environmental
Studies, Nagoya University

Disaster Risk Reduction
Sendai Framework for Disaster Risk Reduction 2015-2030
adopted at the Third United Nations World Conference on Disaster Risk
Reduction held in Sendai Japan in March 2015.

• Outcome over the next 15 years:
The substantial reduction of disaster risk and losses in lives,
livelihoods and health and in the economic, physical, social,
cultural and environmental assets of persons, businesses,
communities and countries.
• Goal to attain the expected outcome:Prevent new and reduce existing disaster risk through theimplementation of integrated and inclusive economic, structural,
legal, social, health, cultural, educational, environmental,
technological, political and institutional measures that preventand reduce hazard exposure and vulnerability to disaster,increase preparedness for response and recovery, and thus
strengthen resilience.

Global Trend

Sendai Framework for Disaster Risk Reduction

Disaster Risk Reduction for Resilience

Global targets:– To reduce global disaster mortality,– To reduce the number of affected people globally– To reduce direct disaster economic loss
– To reduce disaster damage to critical infrastructure anddisruption of basic services
Priorities for action:
Priority 1: Understanding disaster risk.
Priority 2: Strengthening disaster risk governance tomanage disaster risk.
Priority 3: Investing in disaster risk reduction for resilience.
Priority 4: Enhancing disaster preparedness for effective
response and to “Build Back Better” in recovery,
rehabilitation and reconstruction.

Priority 3: Investing in disaster risk reduction for resilience
29. Public and private investment in disaster risk prevention and reduction
through structural and non-structural measures are essential to enhancethe economic, social, health and cultural resilience of persons,communities, countries and their assets, as well as the environment.
These can be drivers of innovation, growth and job creation. Suchmeasures are cost-effective and instrumental to save lives, prevent andreduce losses and ensure effective recovery and rehabilitation.
Important activities at national and local levels:(c) To strengthen, as appropriate, disaster-resilient public and private
investments, particularly through structural, non-structural and functional
disaster risk prevention and reduction measures in critical facilities, inparticular schools and hospitals and physical infrastructures;(e) To promote the disaster risk resilience of workplaces through structuraland non-structural measures;(f) To promote the mainstreaming of disaster risk assessments into land-use
policy development and implementation;95

Disaster Risk Reduction for Resilience

Sustainable Development & Disaster Risk Reduction

Priority 4: Enhancing disaster preparedness for effective
response and to “Build Back Better” in recovery,
rehabilitation and reconstruction

The 2030 Agenda for Sustainable Development

32. --- the recovery, rehabilitation and reconstruction phase, which needs
to be prepared ahead of a disaster, ---The 17 Sustainable Development Goals (SDGs)

adopted at a UN Summit in September 2015 at an UN Summit

Important activities at national and local levels:(c) To promote the resilience of new and existing critical infrastructure,including water, transportation and telecommunications infrastructure,
educational facilities, hospitals and other health facilities, to ensure thatthey remain safe, effective and operational during and after disasters inorder to provide live-saving and essential services

The 2030 Agenda for Sustainable Development
Goal 9: Build resilient infrastructure, promote sustainableindustrialization and foster innovation
Investments in infrastructure – transport, irrigation, energy andinformation and communication technology – are crucial to achieving
sustainable development and empowering communities in many
countries. It has long been recognized that growth in productivity andincomes, and improvements in health and education outcomesrequire investment in infrastructure.

Experiences ofthe 2011 Tohoku Tsunami

Goal 11: Make cities inclusive, safe, resilient and sustainable
Considering cities are hubs for ideas, commerce, culture, science,
productivity, social development and much more, a goal is:• By 2030, significantly reduce the number of deaths and thenumber of people affected and substantially decrease the direct
economic losses relative to global gross domestic product causedby disasters.

Damage to Wooden Houses

Damage to Breakwaters

Wooden houses

Port of Hachinohe
The section of 1,400 m among the Hattaro North breakwater of total
length 3500 m was destroyed by the 9 m tsunami.

Complete destruction
Almost destruction

Inundation depth

Partial destruction
Inundation under the
ground floor
No damage

Destroyed by foundation
scoring by the tsunami
overtopping the caissons

Ratio of houses damaged
国土交通省都市局 平成23年10月4日プレスリリースより

96

Destroyed bywave force of thetsunamiDamage to Other Infrastructure
LeveesLoading/unloading machines

Soma

Ryoishi
Warehouses
Bridges

Utazu

Damage to Tide Barriers
Hachinohe

Actin of the flooding tsunami【on Feb. 20, 2011】
Courtesy of MLIT

Action of the receding tsunami

Scoring
洗掘

Scoring
洗掘（釜石港海岸須賀地区）（八戸港海岸八太郎地区）

護岸天端高T.P.+4.7
近傍痕跡高T.P.+8.03

Soma

Kamaishi

胸壁天端高T.P.+4.00
近傍痕跡高T.P.+8.64

OfunatoDestructionof body

堤体倒壊（大船渡港海岸茶屋前地区）

防潮堤天端高T.P.+3.40
近傍痕跡高T.P.+8.07
Miyako【on Mar. 13, 2011】

Destructionof body

堤体倒壊（大船渡港海岸永浜地区）
Ofunato

防潮堤天端高T.P.+3.00
近傍痕跡高T.P.+10.02

Destructionof gateDestructionof gate

陸閘が陸側に破損

陸閘が海側に破損・流出（宮古港海岸高浜地区）（釜石港海岸須賀地区）

付近防潮堤の天端高T.P.+8.50
Kamaishi
近傍痕跡高T.P.+9.84

胸壁天端高T.P.+4.00
近傍痕跡高T.P.+7.61

Courtesy of MLIT

Sunken Obstacles to Ship Navigation
Points that debris were found: 531● Points for picking up debris
shipping containers: 335
automobiles: 26
others: 74

Fire Incidence

Sendai District of the Sendai-Shiogama Port

Kesen-numa

Sunken obstacles disturbed shipnavigation for transportation of rescue
and restoration people and materials.

Oil tanks damaged

Twelve public wharfs whose depth was 4.5 m or more were available for rescue
transportation until May 31, while all were cleared until 21 May.

Courtesy of MLIT

Courtesy of MLIT

97

Tsunami Consequences
EarthqquakkeTwo Kinds of MULTI (1)The Tohoku region that had prepared to tsunamis wasdevastated by the tsunami

Tsunami

The tsunami was much higher than tsunamis estimated fordisaster management plans in each prefectures damages

Flooding

Destruction

Fire

Topographic
changeDebris

宮古市

Stochastic approach will be introduced into damage estimation.
陸前高田市

Two Kinds of MULTI (2)Tsunami Impact Reduction Caused by Infrastructure

National Land
Resilient to Tsunamis

Multi-level Estimation of
Tsunami Hazard

高田松原

Example of Kamaishi
Inundation heightof 13.7 m

Calculation (No breakwater)
Condition of
calculation: 6.7 m

204 m
20 km from a shoreline

Reduction of 40 %Actual damage
Measurement of 6.7 m tsunami
by GPS‐mounted buoys

Level-2 tsunami

Levee of 4.0 m
28 min. after EQ
Beginning time ofoverflowing

6 min delay
Tsunami of 12 m
by photo analysis

Inundation heightof 8 m by a filed
surveyLevee of 4.0 m

34 minLevel-1 tsunami

Breakwater

Resiliency of Defense Infrastructure
There were examples of tsunami damage reduction
caused by defense infrastructure, even though they
were destroyed by the tsunami higher than theirdesign tsunami.

Measures for Tsunami Disaster
Mitigation

Enhancement of resiliency of defense
infrastructure to tsunamis higher than theirdesign tsunami

98Integration of “Hard” and “Soft” Measures

Estimation of Tsunami Risk Areas• Preparation for protecting human life
at least even though the worst casescenario of disasters happens
 “Soft” measures for protecting
human life ＋ “Hard” measures toLevel-2 Tsunami

reduce inundation and others– Horizontal evacuation outside theinundation areas– Vertical evacuation in nearby RC
buildings– Relocation to higher place

H28年4月熊本地震• Reduction of tsunami impacts  ”Hard” measures– Height is the key for tsunami measures
In case of sea waves, Areal defense causing wave energy dissipation can beavailable for wave disaster mitigation: i.e. artificial reef inducing wave breaking.
出典：神奈川県ホームページ，津波浸水想定図

Emergency evacuation terrace in a port

Higashi-Matsusima City

Terrace has been utilized for fish works in the normal time.

Seawall of 11 m

A building saved houses behind it

Terrace

Seawall of 6 m

漁港

Appropriate arrangement of rigid
structures reduces tsunami damage

Aonae in Okushiri Island, Japan damaged by the 1993 Okushiri Tsunami

Photo by the Geospatial Information Authority of Japan on 2011.5.23

Safe Zone in Chile

Coastal forest
Hachinohe

Elevation 30 m

5.88m

Captured vessels
Inundation depth: 3.2m

8.38m

Pine trees of 150-300 m wide
写真：国土地理院

99

Key points for numerical simulation of tsunami

Damage Estimation in Detail
Tsunami hazard mapping canprovide estimation of• inundation areas• mortality and the number ofaffected people• the number of houses destroyed,• and others• Accuracy of numerical simulation results
depends on that of the used numerical models
as well as bathymetric and topographic data.
• We should prepare appropriate numericalmodels and suitably-accurate bathymetry,
topography and structure data to obtain the
expected results.
• If the suitable data is not available at present,
we need to proceed the simulation step by step
depending on preparation of the data.

Prediction and understanding of
damage caused by possible tsunami inorder to build a system for tsunami
disaster risk reduction

Example of Tsunami & Debris Calculation

Simulation System for Tsunami Damage Estimation
STOC System•STOC-ML
Quasi-3d (multilevel) model with hydrostatic
assumption
Applying to tsunamis propagating in a wide
ocean

Hachinohe Port, 2011.3.11•STOC-IC
3d model with turbulent model and nohydrostatic assumptionWater surface detection by the verticallyintegrated continuity equation
Applying to tsunamis affected by structures•STOC-DMModel for debris motion

Photo: Tohoku Grain Terminal Co.Ltd.

Calculation with STOC

Using output from STOC-ML and STOC-IC
Estimating blocking effects of debris throughdirect connection with STOC-ML• CADMAS-SURF/3D
 3d model with the VOF model that detects the water surface
 Connecting with STOC-IC

SATREPS Research Project supported by JST andJICA

Enhancement of Technology to Develop Tsunami-Resilient
PROJECT PURPOSE: Development of Technologies and Measures to Improve
Community
Communities and People in Chile, Japan and other countries to be WellPreparedpfor and Resilient to Tsunamis

G 4: Proposal of Programs to Create Well-Prepared/Resilient People andCommunity
Education
methodInternational Cooperation

G 1: Development ofMathematical
Simulation Methods
Database on
tsunami damage
Mathematical models
Improvement ofstructure design
methods

100

Method to utilize
ports and harbors inemergency period

Planning method for local
government system to befunctional after the disasterG 2: Proposal of
Tsunami Disaster
Estimation Method and
Mitigation Measures
Guideline for disasterestimation
Estimation of damage
in Japan and Chile
Mitigation measures

G 3: Development ofPrecise Tsunami
Warning Method
Precise tsunami
prediction methodwith seismometers
and offshore
tsunami-meters
Information
dissemination
methodHigh Damage Cost in Capital: Japan Case
• Estimated damage in the 2011 Tohoku event– 154 billion US$ approx.
– Death toll: 20,000– Complete destroyed buildings: 130,000• Possible earthquake disaster in the Capital– 431 billion US$ approx.
– Death toll: 23,000 max.
– Complete destroyed buildings: 610,000 max.

Building National Resilience toDisasters

If an big incident occurs in the Capital area that population and assets areconcentrated in Japan, its disaster can become severe

Building National Resiliency: Japan Case

National Land Use Design for Building Resiliency to Tsunamis
Estimation of tsunami
damage• Aging population and birthrate declining– Lack of persons who are responsible for taking care of handicap
persons, and working rehabilitation and restoration activities

Measure /Residual risk Necessary for keeping and increasing population of productive age

Environmental
ImpactMeasures

Development

Convenience• Building environment to easily live as well as to be safety andsecurity

EconomyNatureSociety• Balance of natural hazard resiliency, industry development,
and sustainable environment

Consensus
building

30 or 50 years later– Natural disasters, society and economy, and environment highly
depending on local characteristics• Relocation to a high place• Construction of a tall defense structure• Construction of a low defense structure

Yes

No

Yes

Sustainable
development

Happy

No
Unhappy

Summary• Strengthening of critical infrastructures including
defense structures, transportation systems,
lifelines, schools, hospitals to build national land
resilient to natural disasters such as tsunamis.
• Multi-level estimation of hazards to build
integrated and inclusive measures forenhancement of resiliency of people, society
and economy.
• Multi-layered defense system to protect people
from unexpected tsunamis• Understanding of geographical and social
vulnerability, and previous disasters in the area
and in the world.

Summary101

102

L8: Tidal Flat Conservation
Hiromi YAMASHITA, PhD. (Ritsumeikan Asia Pacific University: APU, Japan)
Abstract
A tidal flat is a curious place where mud appears in shallow areas of coastal water when
the tide is low. It supports not only an immense variety of wildlife, but also has an
economic value, including providing a source of food, water purification, erosion control,
and reducing damage from tsunamis. Among conservationists, tidal flats are regarded as
one of the most important areas to conserve for the health of the wider coastal and oceanic
environments. International convention documents, such as those produced by Ramsar,
emphasize this (e.g. Ramsar Convention Secretariat 2008).
In this context, cities within 60 km of the sea are growing. Some 60 % of the world’s
population lives within 60 km of the sea and current trends suggest that this figure will riseto 75 % by the year 2025. Three quarters of the world’s megacities are coastal, even though
coastal regions harbour many of the Earth’s most diverse, complex, and productive
ecosystems (UNESCO 1997). Many of the city developments in coastal areas have utilized
tidal flats to expand available land to use for living, transportation, and infrastructure. At
the present time, in many countries more than half of the population lives in a coastal zone,a percentage that is increasing.
Although the ecological importance of wetlands and tidal flats has been widely
communicated in recent years (e.g. Smardon 2009), they are still under great pressure fromurban and coastal development projects in Japan and abroad. In Japan, between the 1940s
and 1980s nearly 40 % of the natural tidal flats were lost through reclamation, and currently
it is said to be 50 % or more (e.g. Baba et al. 2003).
There is little detailed study on how wetlands and tidal flats are perceived by people
who have not had direct contact with them. However, it is well observed by wetland
conservationists in many countries that wetlands and tidal flats have often been referred toas “wastelands” by the general public.
This course looks at the importance of tidal flats; existing tidal flat management
arrangements and issues for the conservation and sustainable use of the areas; and how theecological importance of tidal flat can be communicated effectively.

103

104

 





 ᖸ₲

       “ Shallow, often muddy,
part of seashore, which
are covered anduncovered by the riseand fall of the tide ”

Hiromi Yamashita ᒣୗ ༤⨾
Ritsumeikan Asia Pacific University (APU)
Beppu city, Oita prefecture, JAPAN   
2016.12.08.

Toyohashi Museum of Natural History
2010


z

      Ỉ䛾䛂⇕ᖏ㞵ᯘ䛃 “ Rain forests in the water”

z Around year 1500 – 25% of cities(rich biodiversity)

Currently – 70%“ Womb of the sea” (nursery for fish)

z

ᾏ䛾Ꮚᐑ

z

ᆅ⌫䛾⭈⮚ “ Kidney of the earth” (water purification mechanisms)

Population䠖
z Currently 70% of the world population
75% in Year 2025    !"# #$
  % &“reclamation”'  &   &(    䠷ᾏᓊἲ䠹 !# Management responsibility

PurposeShore foragriculture䠄㎰ᆅᾏᓊ䠅

Ministry of Agriculture,
Forestry and Fisheries

To protect agricultural land andactivities behind the shore from
erosion and natural disasters

Shore forfisheries
䠄⁺ ᾏᓊ䠅

Ministry of Agriculture,
Forestry and Fisheries

To protect fishing ports andfishing activities

Shore for ports
䠄 ‴ᾏᓊ䠅

Ministry of Land,
Infrastructure, Transport
and Tourism䠄ᪧ㐠㍺┬䠅To protect port infrastructuresand related business

Other protected
shores䠄䛭䛾௚ᾏᓊಖ඲
༊ᇦ䠅

Ministry of Land,
Infrastructure, Transport
and Tourism 䠄ᪧᘓタ┬䞉㎰ᯘỈ⏘䛸ᪧ㐠㍺┬⟶㎄እ䠅To protect people’s lives andpossessions behind the shore line

Isahaya Bay

105  !

Types of coasts

1)*  & 
  &  &



A. How ‘invisible landscapes’ can be turned into
attractive landscapes for people, decision
makers and planners’ minds?
B. How to conserve the places that do not havenames on the planning maps?
C. How can planning be aware of 3 dimensionallandscapes, and changes in days and seasons,more effectively?
D. How would it be possible to create ‘joined-up
thinking’ between different management bodies?

A. Tidal flats not being visible
z
z
z

z

&      &

Tidal flats are invisible on various planning maps:
No names = no existence?
Being under the water sometimes, and seasonal and dailychanges not being noted on maps and designing processes
Living creatures look less significant due to small sizes orunder the mud
Out of people’s minds by being not so ‘attractive’

B. Not connected management mechanisms, anddifferent purposes for the coastal management,
although the implications are linked(e.g. creating over-riding local mechanisms or regulations)  %        &(1. Hearing the word)

1. In planners’ and people’s minds

Dirty, Big, Smelly,
Depressive, Quiet,
Empty,
Dead bodies
of creatures(2. Seeing the photo)

2. On maps

Rubbish, Strong wind,
Fun, Super big, Bird, Wet
Shellfish, Crabs, Shrimps
Gentle waves, Shock,Hopes, Many living
creatures

Dirty, Big, Smelly,
Depressive, Quiet,
Cloudy, Rubbish, Wet,
Crabs, Footsteps

3. In the existing management
mechanisms   +*% (3. After visiting),     )-.     
)    -       . /

Marketing
Product cone

essence

1. Essence

Images, characteristics ofproducts (personification: great,
scary, beautiful etc)

2. Benefit

Something beneficial (ecologicalservice, benefits to humanbeings)3. Specs

Description of products, specs(places, names of living
creatures and plants, ecology)

benefit

specs

Marketing Product Cone (Mori 2000)

106

2

“Environmental Risk Communication and( - 1   2 of Tidal Flat Restoration Projects”

8  
9   


3+   45. 6!!7' 6!"*   +. 6!#7' 6!
1) What kinds of environmental

information on tidal flat
restorations are produced andcommunicated by project contractors
and other stakeholders in the community?

       2) How do various

stakeholders perceive the‘benefits’ and ‘risks’ of theirlocal restoration projects?
3) How the findings could make acontribution to future decision
making and support for coastal     Restoration
Projects(     z Restoration project is about a local community

regaining “commons” Æ 
 z How shall it take into account“stakeholders” who existed in the past, but
not there currently (but the success of the
project means those people coming backto the area in the future)z For example, in the UK and Netherlands
cases, fishermen are completely “forgotten
stakeholders” for tidal flat restoration projects.

   
    䇺 䇻 䇺  䇻                 

wetland restorations?

z

z

z

z

z

z

z

z

Joint work. Yamashita, H. and Yasufuku, T. (2016) Coastal planning: Biodiversity conservation andownership, in Shimizu, H. and Takatori, C. (2016 in print) Labor forces and landscape management.
London: Springer.
Joint work. Yamashita, H. and Yasufuku, T. (2016) Coastal area landscape: Environmental changes and thecharacteristics of labor activities, in Shimizu, H. and Takatori, C. (2016 in print) Labor forces andlandscape management. London: Springer.
Joint work. Kato, H., Shimizu, H., Kawamura, N., Hirano, Y., Tashiro, T., Yamashita, H., Tomita, K.,
Tomiyoshi, M. and Hagihara, K. (2014) A Prospect Toward Establishment of Basic and ClinicalEnvironmental Studies by ORT (On-Site Research Training), in Shimizu, H. Murayama, A. (eds) Basic andClinical Environmental Approaches in Landscape Planning. Urban and Landscape Perspectives,
Volume 17. London: Springer. p.133-143
Individual work. Yamashita, H. (2016) ‘Discourse of risks and benefits towards tidal flat restoration: Casestudy of “opening a water gate” of Isahaya Bay land reclamation in Ariake Sea, Wetland Research. ᒣୗ༤⨾䠄2016䠅䛂ᖸ₲෌⏕䛻ᑐ䛩䜛䝸䝇䜽䞉䝧䝛䝣䜱䝑䝖ゝㄝ䠖᭷᫂ᾏㅋ᪩‴ᖸᣅ᤼Ỉ㛛䛄㛤㛛䛅䜢䜿䞊䝇䛻䛃䛄‵‵ᆅ◊✲䛅6ᕳ1
ྕ䚸3-17
Individual work. Yamashita, H. (2015) Problems of the ‘Fact’-Focused Approach in Environmental
Communication: Examples of Environmental Risk Information on Tidal Flat Developments in Japan.
Environmental Education Research, Vol.21(4), pp.586-611. DOI: 10.1080/13504622.2014.940281
Joint work. Ikegawa, T. Aoyama, T. and Yamashita, H. (2014, in Japanese) ‘Ethnographic research forcreating environmental communication: From the field work on tidal flats and surrounding environment’,“Media and Society [Media to shakai]”, Vol.6, pp.39-53. ụഃ㝯அ䞉㟷ᒣኴ㑻䞉ᒣୗ༤⨾䠄2014䠅䛂⎔ቃ䝁䝭䝳䝙䜿䞊䝅䝵䞁๰ฟ䛾䛯䜑䛾䜶䝇䝜䜾䝷䝣䜱䝑䜽䞉䝸䝃䞊䝏䠉ᖸ₲䛸࿘㎶⎔ቃ䛾䝣䜱䞊䝹䝗䝽䞊䜽䛛䜙䛃䛄䝯䝯䝕䜱䜰䛸♫఍䛅
Vol.6, pp.39-53. 䠄2014ᖺ3᭶䠅http://hdl.handle.net/2237/19815
Joint work. Hockings, C., Cooke, S., Yamashita, H. McGinty, S. and Bowl, M. (2009) ‘I’m neither entertaining
nor charismatic…’ Negotiating university teacher identity within diverse student groups. Teaching in Higher
Education, Special Issue: purposes, knowledge and identities, 14(5):470-483.
http://www.tandfonline.com/doi/abs/10.1080/13562510903186642
Individual work. Yamashita, H. (2009) Making Invisible Risks Visible: Education, environmental risk
information and coastal development. Ocean & Coastal Management, 52:327-335.
http://www.sciencedirect.com/science/article/pii/S0964569109000258

107

3

108

E1: Analysis of satellite data for monitoring and assessment for coastal eutrophication
Genki Terauchi (NOWPAP CEARAC)

Abstract
Marine eutrophication has recently become a concern for all the world’s oceans. There
are over 415 areas worldwide identified as manifesting symptoms of eutrophication.
Eutrophication causes deterioration of the coastal environment and often leads to theformation of harmful algal blooms and depletion of bottom oxygen, which may
subsequently induce fish kills and/or ecosystem damage. Eutrophication was originallyused in limnology to describe the natural process of nutrient enrichment concomitant withthe aging of lakes and ponds. However, it is known that humans, through various activities,
can greatly accelerate this natural process by increasing nutrient input into bodies of water.
Northwest Pacific region, which includes parts of northeast China, Japan, Korea andsoutheast Russia, is one of the most densely populated areas of the world, and its coastal
systems are under pressure from human activities. In deed, a significant number of red tides
and hypoxic conditions have been reported in coastal waters - possibly due toanthropogenic influences such as extensive chemical fertilizer use and sewage effluent.
In this training course, case studies of use of remotely sensed chlorophyll-aconcentration (satellite Chl-a) in Toyama Bay, Japan will be introduced. Preparing aconsistent long-term time series satellite Chl-a data sets and a methodology for quality
assurance processes will be included. Interannual and seasonal variability of satellite Chl-aand its changes associated with fresh water discharge will then be discussed. Use of Google
Earth Engine, a cloud-computing platform for processing satellite imagery, for detection ofchanges in coastal habitats will also be introduced.

109

110

Table of contents• 1. General Introduc5on• 2. Preliminary Assessment of eutrophica5on byremotely sensed chlorophyll-a in Toyama Bay• 3. Inﬂuence of river discharge on seasonal andinterna5onal varia5on of chlorophyll-a• 4. Summary• 5. Introduc5on to Google Earth Engine• 6. Hand on prac5ce

Analysis of satellite data for monitoring andassessment of coastal eutrophica5on:
a case study in Toyama Bay

Genki Terauchi
NOWPAP CEARAC

Eutropjhica5on as a global concern-Spreading Dead Zones-

Marine Eturophica5on as a global concern
Increasing Eutrophica5on
Excessive growth of marine plant life, is seriously
Disrup5ng ecosystems and threatening healththroughout the worlds: coral reefs, seagrass beds
and other vital habitats are suﬀering.
And it can trigger explosive blooms of toxic algae
Which can blight tourism, contaminate seafood
And poison people.

Diaz and Rosenberg 2008

Imai and Hori 2006

Eutrophica5on as a threat in
the Northwest Paciﬁc
Liu et al., 2010

- Northwest Paciﬁc Ac5on Plan (NOWPAP) • Regional Sea Program(RSP)

Dong et al., 2010

– Launched in 1974 by UNEP toaddress the accelerating
degradation of the world’s
oceans and coastal areas.
– RSP covers 18 regions acrossthe world today• NOWPAP

Wei et al 2007

– Adopted in 1994
– China, Japan Korea andRussia– Latitude 33 - 52ON
– Longitude 121 – 143E

111

Mission of NOWPAP CEARAC

Publications and databases• Mission– Assessment of the state of the marine, coastal
associated fresh water environment– Development of tool for environmental assessment• Activities– Harmful Algal Blooms (HAB)– Remote Sensing of Marine Environment– Assessment of eutrophication– Marine Litters– Marine biodiversityh"p://ocean.nowpap3.go.jp/

Ocean remote sensing
in the Northwest Pacific Region

Ini5a5ves to address or mi5gate eutrophica5on

Training Courses on remote sensingof marine environment
4th course in 2013

Ocean color data time series
in the Marine Environmental Watch-NOWPAP Common Procedure
for eutrophica5on assessment

3rd course in 2011

• Developed from a case study in Toyama Bay• Eutrophication is assessed by

PICES

PICES
23 trainees from China, Korea,
Canada, Cameroon and Oman

22 trainees from China, Japan, Korea,
Russia, India, Indonesia and the Philippines

91 people from 14 counties and region received the training courses

2nd course in 2008

Parameters

I

Parameters that indicate degree of nutrient enrichment (e.g.
T-N/T-P load, DIN/DIP, N/P ratio)II

Parameters that indicate direct effects of nutrient enrichment(e.g. Chlorophyll-a, red tide)III

Parameters that indicate indirect effects of nutrient
enrichment (e.g. DO, fish kill, COD)

IV

Parameters that indicate other possible effects of nutrient
enrichment (e.g. Shellfish poison)

1st course in 2007
IOC/WESTPAC

23 trainees from China, Japan, Korea,
Russia, France and Thailand

Category

23 trainees from China, Japan, Korea, Russia,
India, Indonesia, Thailand and Vietnam

Chl-a is one of indicator among the others (Harding and Perry 1997; Bricker et al. 2003)

Poten5al of remotely sensed Chlorophyll-a
for assessment of eutrophica5on

Strength and weakness ofsatellite and shipboard measurements
Means of observation
Satellite Remote
SensingSatellite observa5on of Chl-a

Preliminary
Assessment
for screening

Ship board
measurements

NASA Ocean Color Web• Regular monitoring start from afer the launch of NASA
SeaWiFS in 1997.
• More than 16 years of remotely sensed Chl-a data available

Holistic Assessment

112

Strength

Weaknesses

•Wider area andhigher temporal
coverage•Objectively detect
relative change•Free data accessover the Internet•Low accuracy inestimation of Chl-a incoastal area
•No data obtained
under cloud•Data is available onlyat sea surface•Obtain data undersea surface•Can obtain actual
measured value•Data represent only
point of information•Analysis of Chl-a needexpertise•CostlyDevelopment of procedure for
eutrophication assessment• Procedures for assessment ofeutrophication status including
evaluation of land-basedsources for nutrients for the
NOWPAP region (June, 2009
and refined in 2013)

Assessment of eutrophicationby the NOWPAP Common Procedure
Level

The Common Procedures(as of Aug 2013)

Trend

Help plan immediate mi5ga5oninterven5on ac5onHelp plan preven5ve management
ac5on

Chl-a level of 5ug l-1 is used as value forearly warning

Annual maximum value in monthly
mean

Level of Chl-a for
eutrophicationassessment proposedby Bricker et al. (2003)

Use of remote sensing is
proposed as a screening toolHypereutrophic (>60ug l−1)
High (>20, ≤60ug l−1)
Medium (>5, ≤20ug l−1)
Low (>0 and ≤5ug l−1)
NOWPAP CEARAC (2009)Material and method

2. Preliminary Assessment of eutrophica5on byremotely sensed chlorophyll-a in Toyama Bay
Jyouganji
River
KurobeRiver• Satellite Chl-a data

Jinzu
River

SensorShou River
Oyabe
Costal Surface Water
River

Algorithm
DuraTonData
Area

Tsushima Warm
Current Water

300m

NASA SeaWiFS on Orbview 2NASA MODIS on Aqua
R2009 NASA standard datasets
12 Years from Jan 1997 to Dec 2009
Monthly composite
Toyama Bay (36.5 to 38ON, 136.5 to 138.5OE)
Jun
2002

Deep Sea Water(JSP Water)

SeaWiFS

MODIS (Aqua)

Dec
2009

1,000mSep
1997

Toyama Bay and its characteris5cs

Uncertainty of satellite Chl-aand Level 2 ﬂag

Material and method• In situ data
136.5°E

138.5°E

Water sampling stationsof in situ Chl-a
Coast Center Offshore

Sea surface

38.0°N

ToyamaBay

Toyama
Bay

Mixed
Water
2m

8

1
Oyabe
River

2 3

4 5
Jinzu
River

6

7

Increase of Chl-ain summer
Total Nitroggen input
Total Phoshate input

Atmospheric correction failure
Pixel is over land

Navigation quality is reduced
possible absorbing aerosol

Pixel is in shallow water
spare
Straylight contamination is likely

Atmospheric correction is suspect
spare
Possible sea ice contamination

Probable cloud or ice
contamination
Coccolithofores detected

Bad navigation

Turbid water detected
High solar zenith
spare
Very low water-leaving radiance(cloud shadow)
Derived product algorithm
failureSST quality is reduced
SST quality is bad
High degree of polarization
Derived product failure(disabled)
If you use
the 17 level 2 ﬂags are being appliedin the NASA global
One or more product warnings
spareHigh sun glint
Aerosol iterations exceeded max
data sets in coastal zones
Observed radiance very high or ↓Moderate sun glint contamination
saturated
Most data in coastal zone willDerived
disappear
High sensor view zenith angle
product quality is reduced

9

0.5m

Dec
2004

36.5°N

Loca5on of Toyama Bay

113

Pixel rejected by user-defined filter

Aug

spare

Evalua5on of consistency between SeaWiFS and
MODIS-A Chl-a log(MODIS-A Chl-a) = -0.900 + 0.932 log(SeaWiFS Chl-a)

Valida5on of satellite Chl-a with in situ Chl-a
Quality screening
by level-2 ﬂags

10

1

0.1

0.01
0.01

Coast ﬂag &Straylight ﬂag were on
0.1

1

10

100

r2 = 0.81, N = 137173(b)
MODIS-A
N=34
r=0.81p<0.0001

SeaWiFS on Orbview-2
Pass 5me: 12:00 (noon)

1

0.1

0.01
0.01

0.1

1

10

100

Terauchi et al. 2014






















     

Number of plots

N=30837
R2=0.807
Y=-0.079+0.925*X

In situ Chl-a (mg m-3)

In situ Chl-a (mg m-3)

Fig 2.3 Comparison of satellite and in situ Chl-a during the studied period(a) Level-2 data for SeaWiFS Chl-a from 1997 to 2004 and (b) MODIS-A Chl-a from 2002 to 2009were compared with in situ Chl-a, with a 5me diﬀerence ≤3 hours. In situ data were obtained at 9
water sampling sta5ons located at the coast, center and oﬀshore of Toyama Bay. The solid line is a
1:1 ra5o; dashed lines show 1:2/2:1; doked lines show 1:3/3:1 ra5os.

MODIS on Aqua
Pass 5me: 1:30 PM

VS





MODIS-A Chl-a (mg m-3)

SeaWiFS Chl-a (mg m-3)

10(a)SeaWiFS
N=42
r=0.77p<0.001

100

MODIS-A Chl-a (mg m-3)

100



2002










N=45505
R2=0.792
Y=-0.061+0.946*X

2003











N=60831R2=0.818
Y=0.019+1.136*X

2004










SeaWiFS Chl-a (mg m-3)

Fig. 3 Comparison of SeaWiFS and MODIS-A Chl-a from 2002 to 2004.
Pixel-to-pixel comparison of daily composite data between SeaWiFS and MODIS-A Chl-a was made from2002, 2003 and 2004.

Determina5on of high and low Chl-a area

Detec5ng interannual Chl-a trend

High Chl-a

(mg m-3)(mg m-3/ year)

Low Chl-a

Increase Trend
No Trend
Decrease TrendDetec5on of High and Low Chl-a area in Toyama Bay.
(a)13-years overall mean of satellite Chl-a. (b) High and Low Chl-a area
determined by the Chl-a level more than 5 ug l-1 referring to the Medium Chl-a
condi5on (>5, <20 ug l-1) of Bricker et al. (2003).

12-years of satellite Chl-a trend.
(a) The trend of annual Chl-a max in monthly mean Chl-a and its signiﬁcance were es5matedat pixel wise by the Sen Slope test at 90% conﬁdence level. (b) Increase Trend, No Trend andDecrease Trend area were then detected.

Preliminary assessment of eutrophica5on in
Toyama Bay

Interannual change of monthly satellite and in situ
Chl-a in the detected poten5al eutrophic zone

Terauchi et al. 2014

Classiﬁca5on ofeutrophica5on status
Potentially eutrophic?

(mg m-3/ year)(mg m-3)

HD

HN

HI

LD

LN

LI

Monthly mean of merged satellite Chl-a in the detected eutrophica5on zone and mean in situ
Chl-a obtained at 3 water-sampling sta5ons (Sta5ons 3, 4 and 5)
in the detected poten5al eutrophic zone.
Dashed red line is 5 mg m-3 which is the threshold condi5on to determine High or Low Chl-alevel.

114

Interannual change of monthly satellite and in situ
Chl-a in the detected poten5al eutrophic zone
TN (ton/s)

30.00

Jinzu TN(a)Summary of preliminary eutrophica5on
assessment with satellite Chl-a
• Past studies based on spa5ally and temporally
limited (sprase) data

Oyabe TN

25.00
20.00

– Increased Chl-a in summer in Toyama Bay coastal area
as a cause of water quality degrada5on

15.00
10.00

• Preliminary assessment of eutrophica5on by
spa5ally and temporally intensive remotelysensed Chl-a

5.00
0.00

TP (ton/s)

2.50– Illustrated boundaries of the poten5ally eutrophic
zones in Toyama Bay coastal area, where mi5ga5on ofwater quality is necessary.
– Increasing TN input from Jinzu River as a cause of
eutrophica5on

(b)

2.00

Jinzu TP

1.50

Oyabe TP

1.00

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

0.00

1986

0.50

3. Inﬂuence of river discharge on seasonal andinterannual varia5on of Chlorophyll-a in Toyama Bay

Material and method• Chl-a increased afer spring (Nagata and Nakura 1993)• Inﬂuence of river discharge on seasonal and interannual variabilityof Chl-a in Toyama bay?
• Causes of poten5al eutrophica5on in Toyama Bay?

Annual Chl-a peak appears• Spring peak type
Year day <= 121• Summer peak type
Year day > 121

Terauchi et al. 2014

Year Day(a)

Chl-a

High A longer peak in summer(Sze 1993; Murakami 1996;
Yamada and Kajiwara 2004)

ToyamaBay

Increase

N,P N P

Satellite Chl-a

Step 1

Flow of data processing
Step 2

Step 3

Daily
Comp

MonthlyComp(mg m-3)

Step 4

Poten5al eutrophic zone
detected by Terauchi et al. (2014)(d)

Step 5Spa5al meanin each subarea

Step 2Monthly compositeofeach river discharge

Sub-area A
Sub-area B
Sub-area C
Referen5al site

100
Sub-area A

Step 3Monthly composite of the
5 major rivers discharge

Sub-area B

Sub-area C

10

1

1

May to Jul

Feb to Apr
0.1100• Sub area A
10
Signiﬁcant posi5ve
correla5on is observed
when river discharge is 1
less than 500 m3 sec-1 in
0.1
Aug to Oct

Comparison

River discharge data

100

• Sub area B and C
Signiﬁcant posi5ve
10
correla5on is observed
from May to October

Checking spa5al
coverage in each subarea

Chl-a concentra5on

C
B
A

Collec5ng daily
composite riverdischarge data(c)Spring peak type
Summer peak type

Seasonal variability of satellite Chl-a and riverdischarge

Spa5al coverage less than 80%
will be deleted

Step 1(b)

Spring and fall peaks
Persons et al. 1984
Summer Fall
Chl-a seasonal pakerns andEutrophica5on in temperate zone

Decrease

Level 2 data
(Pass data)

Average of year day of annual
maximum from 1998 to 2009

Winter Spring

No trend

Oyabe Jinzu River
River

(a)

0.1
100

10

1

Aug to Oct
0

300

600

900

1200

Aug to Oct

0.1
0

300

600

900

1200

River discharge(m3 sec-1)

Comparison of monthly means of satellite Chl-a in each sub-area and the reference site,and sum of monthly mean river discharge in the 5 Class-A rivers from 1998 to 2009.

115

Discussions

Interannual variability of satellite Chl-a andriver discharge from 1998 to 2009
1200

Higher thanearlier years

10

900

600

Nutrients was always higher than thethresholds at Jinzu river mouthRiver discharge(m3 sec-1 )100

Chl-a concentra5on(mg m-3)

Tsujimoto
No seasonal varia5on was observed innutrients from Apr 2006 to Feb 2008

Nutrient limita5on was altered at theoﬀshore sta5on (Sta5on 3)(Nitrogen limita5on in 2006 and phosphate
limita5on in 2007)

1
300

0.1

0
MJ J A SO MJ J A SO MJ J A SO MJ J A SO MJ J A SO MJ J A SO MJ J A SO MJ J A SO MJ J A SO MJ J A SO MJ J A SO MJ J A SO
1998

1999
RD

2000
Subarea-A

2001

2002

Subarea-A

2003

2004

Subarea-B

2005

2006

Subarea-B

2007

Subarea-C

2008

Sta5on 3 in Sub-area B

2009

Subarea-C

Sta5on 1 and 2 in Sub-area A
Satellite chl-a in subarea A is higher is later years than earlier years
Thre is4. Summary

Types chl-a variability pakerns in Toyama Bay

Assessment by trend of satellite Chl-a

Not enough for mi5ga5on interven5on
Two peaks pakern typically found intemperate zone
(Person et al. 1984; Yamada et al. 2004))

Sub-area A
Sub-area B
Sub-area C

Middle typeChl-a increase afer spring towards
summer, but peak is not as clear as subarea A.
Un consumes nutrients in subarea A aretransported to this sub-area.
Nutrient limi5ng factor changes each year.
One long peak pakern appears in summer(Nagata and Nakura 1993; Ohnishi et al.
2007)

Kahru and Mitchell, 2008
Assessment by trend of satellite Chl-a

Sub-area A
Sub-area B
Sub-area C

Single image indicates poten5al eutrophic
zones for mi5ga5on & preven5ve ac5ons
Spa5al and temporally dense data were used
To characterize water types in Toyama Bay

Not enough preven5ve management.
Gohin et al., 2008
Assessment by Level of satellite Chl-a

Nutrients are rich through the year

Characterizing water types
and inﬂuence of river discharge

Limita5ons of the proposed methodology

Tsujimoto (2012)

Boyce et al. 2010

Nagata and Nakura (1993)

Integra5on with historicalin situ data
Decadal-scale phytoplankton ﬂuctua5ons
Ohnishi et al. (2007)

IOCCG 2000

TN from Jinzu River

Deepen understanding on
Chl-a variability In Toyama bay
Clariﬁed boundaries of inﬂuence ofland-based sources nutrients from rivers

Chl-a es5ma5on fails in turbid water

Applica5on of beker Chl-a es5ma5on
algorithm for turbid water (Siswanto et al. 2011)

116

Sub-area A
Sub-area B
Sub-area C

Detec5on of Global Forest Change at 30 m resolu5on

5. Introduc5on to Google Earth Engine

Hansen et al. 2013This is the ﬁrst map of forest change that is globally consistent and locally
relevant. What would have taken a single computer 15 years to perform wascompleted in a maker of days using Google Earth Engine compu5ng.
- Professor Ma4 Hansen, University of Maryland

hkps://earthengine.google.com/

Analysis of surface water changes globally at 30m resolu5on

Land reclama5on in Isahaya Bay, Japan

Donchyts et al. 2015

Land to water
Water to Land

#1. Create image
for your area of interest

Hands on• Analysis of 5me series Chl-a using WIM Sofware• References– C:\Program Files\WimSof\Course\3• Tutorial_5me_series_of_areal_sta5sics.pdf• Input– Projec5on: Linear– Pixel in size 9,000 meters– Input la5tude and longitude
for your area of interest– Apply coastal line overlay

hkp://wimsof.com• Output– Save output as hdf ﬁle117

#2. Create mask images
for your area of interest#3. Create 5me series chart• References• References– C:\Program Files\WimSof\Course\3– C:\Program
Files\WimSof\Course\3• Tutorial_5me_series_of_areal_sta5sics.pdf• Input• Tutorial_5me_series_of_areal_sta
5sics.pdf– Remapped merged L3 Monthly Chl_9 images with WIM liner
projec5on by wam_series (save the ﬁles in “cut” directory)– List of monthly Chl-a image under C:\Sat\Merged\L3\Monthly\CHL_9– Your created mask image for 5me series• Input– Draw masks to be used for 5meseries with Edit-Draw func5on– Replace coast line value 255
with 0 by Transf-Replace Values• Output– Run wam_sta5s for monthly 5meseries and save output as .csv
ﬁle and then create 5me series chart with MS excel• Output– Save output as hdf ﬁleSave your output in Z (share drive)• #1. Crea5ng directory with your name• #2. Save your output in MS power point andput in the directory118E2: Cruise Data Analysis
Joji Ishizaka(Institute for Space-Earth Environmental Research, Nagoya University)
Abstract
Ise Bay is one of heavily used coastal environments in Japan.

Surrounded by the largecity of Nagoya and Tokai industrial regions, many cargo ships transported large amount ofmaterials through the bay.

Because of the anthropogenic activities, both atmosphere andmarine environments had been heavily polluted during 1970/80s, and hypoxia condition of
the bottom water has been still often observed during summer, even the loading of thepollutant has been reduced.

On the other hand, Ise Bay and the surrounded area have been

also known as high fisheries production areas, and some of fish/clam catches, such as
Manila clam, is top level in Japan.
Using the T/V Seisui-Maru in Mie University, we will observe marine environments ofIse Bay including water mass structure, phytoplankton abundance, and optical properties.
We also demonstrate to take plankton samples by net and sediment with grab.
Observations will be conducted in the western Ise Bay (narrow definition of Ise Bay) and in
the eastern Ise Bay (Mikawa Bay).

It is possible to see how people are using the bay andsurrounded areas.
During the excise of cruise data analysis, we are planning to plot environmental data,such as temperature, salinity, chlorophyll-a, from the cruise during the training course andfrom cruises during different seasons to check the special and temporal changes of theenvironments.
by NOAA.

We are going to use Ocean Data View, which is a free software developed

We are also planning to verify the chlorophyll-a concentrations estimated by

optical measurements and satellite using the observed data during the cruise.

119

120

E3: Coastal model output analysis
Hidenori Aiki
(Institute for Space-Earth Environmental Research, Nagoya University)
Abstract
Ocean models have been used for understanding the three-dimensional structure and thecontinuous time evolution of coastal ocean circulations that are affected by the seasonal andinterannual variations of general ocean circulations, such as the Kuroshio Current. Recent
advances in atmosphere-ocean modeling and in-situ and satellite observations have enabled
to take into account both the realistic structure of the Kuroshio Current and thehigh-resolution variations of atmospheric and tidal forcing for ocean circulations. The firstpart of this exercise gives a short lecture on basic understanding for (i) ocean models, (ii)classification of model experiments, and (iii) available model outputs and forcing. Then thesecond part of this exercise illustrates how to use a visualization tool suitable for analyzing
model outputs and explain useful metrics for understanding the dynamics of coastal ocean
circulation.

121

122

Exercise 3, December 7 (Wednesday), 13:30-17:00

26th IHP TC, 2016


Governing Equation System for an Ocean Model�

u x + v y + wz = 0

Coastal Model Output Analysis tendencyadvecOon

Coriolisuvw

Incompressiblepressure
gradientρ0 ⎡⎣ut + (uu)x + (vu)y + (wu)z − fv ⎤⎦ = − px + mixing

E3.1 Ocean models(13:30- 20min)
E3.2 ClassiﬁcaOon of model experiments (13:50- 20min)
E3.3 Available model outputs and forcing (14:10- 20min)- break E3.4 VisualizaOon(14:50- 30min)
E3.5 Useful metrics(15:10- 30min)- break E3.6 How to make animaOon(15:50- 40min)
E3.7 PresentaOon by students(16:40- 20min)ρ0 ⎡⎣ vt + (uv)x + (vv)y + (wv)z + fu ⎤⎦ = − py + mixinghydrostaOc

[kg / m 3 ]ρ = ρ(T,S,p)[kg / m 3 ]salinity
density
Coriolis
gravityρ ≈ 999.8 − 0.0752T + 0.8244S[kg / m 3 ]ρ = ρ(T,S,p)[kg / m 3 ]

z=0m

T=0

S=0 g/kg

999.8

999.7

998.2

995.6

S=30 g/kg

1024.0

1023.0

1020.9

1017.9

S=35 g/kg

1028.1

1026.9

1024.7

1021.7

S=40 g/kg

1032.1

1030.8

1028.5

1025.4z=-4000m

T=0

T=10

T=20

T=30 

S=0 g/kg

1001.8

1001.6

1000.0

997.4

S=30 g/kg

1025.9

1024.8

1022.7

1019.6

S=35 g/kg

1030.0

1028.7

1026.5

1023.4

S=40 g/kg

1034.0

1032.6

1030.3

1027.1





T=10





T=20





T=30 



^3

US
ROMS (Regional Ocean Modeling System)
hZp://www.myroms.org/
POM (Princeton Ocean Model)
hZp://www.ccpo.odu.edu/POMWEB/FVCOM (Finite-Volume, primiOve equaOon Community Ocean Model)

l

-50m

hZp://fvcom.smast.umassd.edu/

HYCOM (Hybrid Coordinate Ocean Model)
hZps://hycom.org/
MITgcm (MIT general circulaOon model)
hZp://mitgcm.org/
MOM (Modular Ocean Model) hZps://www.gfdl.noaa.gov/mom-ocean-model/

Surface layer
thermoclinetemperature

E3.1 Ocean Models

m

m
^3

10
27
kg
/

0m

10
25

kg
/

oceanic mixed layer

turbulent viscosity

equaOon of state

Exact version

hZp://co2.hyarc.nagoya-u.ac.jp//labhp/member/aiki/density.html

zsea surface

Simpliﬁed versionρ ≈ 999.8 − 0.0752T + 0.8244S

VerOcal structureof the oceanηDensity of sea waterSimpliﬁed versionExact version

z

T
S
ρ
f
g

St + (uS)x + (vS)y + (wS)z = mixingDensity of sea waterp = ∫ g ρ dz
heightTt + (uT )x + (vT )y + (wT )z = mixingρ = ρ (T ,S, p)

verOcalη

0 = − pz − g ρbuoyancytendencyadvecOon

meridional

turbulentviscosityHidenori Aiki, ISEE, Nagoya Univ.

Boussinesqzonal-1000mEuropeNEMO (Nucleus for European Modelling of the Ocean)

hZp://www.nemo-ocean.eu/

Japan
MRI.COM (Meteorological Research InsOtute Community Ocean Model)

Deep layer

hZp://www.mri-jma.go.jp/

COCO (CCSR Ocean Component model)

hZp://ccsr.aori.u-tokyo.ac.jp/~hasumi/COCO/

Ocean modelling acOviOes in the East Asia Region:
hZp://www.clivar.org/sites/default/ﬁles/documents/wgomd/tsujino_report.pdf

-4000m123

A state-of-the-art coastal ocean cir-nents needed in scientific or manage-

coastal regions characterized by complex

culation model system requires: (1) grid
flexibility to resolve complex coastline

ment applications; and (5) the ability
to use a wide variety of input data, es-coastlines and bathymetry and diverse
forcing. Used widely in computational

Lecture and8 engineering, theand bathymetry; (2) accurate numerical 3 pecially as more real-time atmospheric CEE262c
fluid mechanics
methods that conserve mass, momenand coastal ocean measurements become
finite-volume method used in this modeltum, heat, and salt; (3) proper paramavailable for assimilation. This model
combines the advantage of finite-eleeterization of vertical and horizontal
mixing; (4) modular design to facilitate

system should be robust, have a flexible
user interface, and be an “open” commu-

ment methods for geometric flexibility
and finite-difference methods for simple

selection of the essential model compo-

E3.1 Ocean Models

nity model, supported by an expandingbase of users that continue to improve it.

discrete computation (Figure 1). Verified
through comparisons with analytical so-

Changsheng Chen (c1chen@umassd.edu)
is Professor, School for Marine Science andTechnology, University of Massachusetts-

A major step towards such a system
has been taken recently by a team ofUniversity of Massachusetts-Dartmouthlutions and numerical simulations madewith POM and other popular finite-difference models in idealized test cases

ment of Physical Oceanography, Woods

et al., 2004) who have developed a new(Chen et al., submitted), FVCOM has
been successfully applied in a number of
estuarine, continental shelf, and region-

Hole Oceanographic Institution, Woods Hole,
MA, USA. Geoffrey Cowles is Research

prognostic, unstructured-grid, FiniteVolume, free-surface, three-dimensional

al/open ocean studies involving realistic
model domains (for more information

Scientist, School for Marine Science andTechnology, University of Massachusetts-

primitive equation Coastal Ocean circulation Model (FVCOM) for physical andsee http://codfish.smast.umassd.edu).
For hindcast and forecast applications,

Dartmouth, New Bedford, MA, USA.

coupled physical/biological studies in

an integrated coastal ocean model sys-

VerOcal Grid Arrangement
CEE262c Lecture 8
New Bedford, MA, USA. Robert Sigma
and Woods coordinates
Hole Oceanographic InstituZ-levelDartmouth,
coordinates
C. Beardsley is Scientist Emeritus, Departtion researchers (Chen et al., 2003; Chen

Horizontal Grid Arrangement
Structured Grid

∂τxx ∂τxy
+
,
∂x
∂y

τxx = 2AM∂U,∂x

τxy = AM

!

∂
Fx =
∂x

!
"
#
!
"$
∂U
∂
∂U
∂V
2AM
+
AM
+
.
∂x
∂y
∂y
∂x

∂U
∂V
+
∂y
∂x

"







hZp://apdrc.soest.hawaii.edu

Density coordinates

3

where ∇ = ∂x e1 + Grid∂y e2 and V = U e1 + V e2 , and where the horizontal diffusion is given by
Unstructured∂τxx ∂τxy
+
,
∂x
∂y

Model Coastline

Fx =Real Coastline

τxx = 2AM

∂U
,
∂x

!

∂U
∂V
Figure 2: Common vertical coordinate systems used inτ ocean= A models. +,E3.2 Available Model Outputs and Forcing

Figure 2: Common vertical coordinate systems used in ocean models.

2: Common vertical coordinate systems used in ocean models.

y e2 and V = U e1 + V e2 , and where the horizontal diffusion is given by

Fx =

3

xy

M

∂y

∂x

"

,

where ∇ = ∂x e1 + ∂y e2 and V = U e1 + V e2 , and where the horizontal diffusion is given by
!
"
#
!
"$
∂
∂U
∂
∂U
∂V
∂τxx ∂τxy
Fx =
2AM
+
AM
+
.
Fx =
+
,
∂x
∂x
∂y
∂y
∂x
∂x
∂y

Ocean
Note that in the Navier-Stokes equations in their incompressible form, the viscous term is
by The true coastline is shown in black, the !
Figure 1. An example of fitting a structured grid (left) and an unstructured grid (right) to a∂Usimple coastalgiven
embayment.
"!
",xx = 2A
M the model∂∂ui ∂uj∂ 2 ui∂τij∂∂ui ∂uj∂ 2 ucoastline
modelin red. Note how the unstructured triangular grid can beτadjusted
so thatcoastline follows the true coastline, while ∂τ
theijstructured
i
=
ν
+
=ν
,
=
ν
+
= νgrid coastline, is jagged—which can result in unrealistic flow disturbance close to the coast.∂x
∂xj
∂xj
∂xj
∂xi
∂xj ∂xj
∂xj
∂xj
∂xj
∂xi
∂xj ∂xj!
" for i = 1, if ν "=constant,soonstant,
∂U
∂V
!
"
!
"
!
"
τxy = AM
+
,
!
"
!
"
!
"
∂
∂u ∂u
∂
∂u ∂v
∂
∂u ∂w
∂τij
∂y
∂x
∂
∂u ∂u
∂
∂u ∂v
∂
∂u ∂w
=
ν
+
+
ν
+
+ ν
+
.
80+ Oceanography
ν
+
+
ν
+
ν
+ Vol. 19, No.
. 1, Mar. 2006
∂xj
∂x
∂x ∂x
∂y
∂y ∂x
∂z
∂z
∂x
∂x
∂x ∂x
∂y
∂y ∂x
∂z
∂z
∂x
!
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#
!
"$
∂
∂U
∂
∂U
∂V
Fx =
2AM
+
AM
+
.
∂x
∂x
∂y
∂y
∂x

avier-Stokes equations in their incompressible form, the viscous term is

Note that in the Navier-Stokes equations in their incompressible form, the viscous term isgiven by!
"∂τij∂∂ui ∂uj∂ 2 ui
=
ν
+
=ν
,
∂xj
∂xj
∂xj
∂xi
∂xj ∂xj
so for i = 1, if ν "=constant,
!
"
!
"
!
"
∂
∂u ∂u
∂
∂u ∂v
∂
∂u ∂w
∂τij
=
ν
+
+
ν
+
+ ν
+
.
∂xj
∂x
∂x ∂x
∂y
∂y ∂x
∂z
∂z
∂x



E3.2 Available Model Outputs and Forcing



hZp://apdrc.soest.hawaii.edu

AtmosphericForcing
Momentum

Heat &
Water ﬂux

SST

OCEAN
MODEL
IniOal and lateral boundary condiOons for OCEAN MODEL w/o Odes

E3.2 Available Model Outputs and Forcing







hZp://volkov.oce.orst.edu/Odes/global.html

Lombok Strait
Gordon et al. (2005)

124


South
(Indian Ocean)



North
(Java Sea)

y
x

z
x



Forcing checkpoints

E3.3 Classiﬁca?on of model experiments

Atmospheric forcing
source: Satellite remote sensing (dx=20km) or
Atmospheric model outputs(global: dx=200km, regional: dx=5km)
Ome-evoluOon: Climatology, Monthly, Daily-mean,
3-hour interval, Hourly 

Ocean Model• Hindcast experiments w/o Odes
• Reanalysis (data assimilaOon) w/o Odes
• Nowcast experiments w/o Odes
• Forecast experiments w/o Odes

River discharge
included or not included

Coupled Atmosphere-Ocean Model• Hindcast experiments• Reanalysis (data assimilaOon)• Nowcast experiments• Forecast experiments

Tidal forcing
included or not included
consOtuents: M2, S2, M1, O1 …

 

Earth Simulator III

JCOPE (Japan Coastal Ocean Predictability Experiment)
reanalysis / forecast system

JCOPE (Japan Coastal Ocean Predictability Experiment)
reanalysis / forecast system

Earth Simulator III

Reanalysis(1993-, dx=1/12 deg, 108-180E, 10.5-62N)

125

Miyazawa and Yamagata (2003)
Miyazwa et al. (2009)
Varlamov et al. (2015)

 

 

AtmosphericForcing

CPU1

CPU2

CPU3

Atmosphere
Model

Nagoya Univ. (Tsuboki and Sakakibara, 2003)
DX = 4km
IniOal and lateral B.C.: RANAL from NPD/JMA

OCEAN
MODEL

NonHydrostaOc Ocean model for the Earth Simulator(NHOES)

JAMSTEC (Aiki and Yamgata, 2004)
DX = 4km, DZ = 2 m for top 100 m
IniOal and lateral B.C.: JCOPE2 reanalysis:
realisOc 3D structure of Kuroshio Current / SST in western North Paciﬁc

IniOal and lateral boundary condiOons for OCEAN MODEL w/o Odes

CPU2

CPU3

CPU4

CPU5

CPU6

CPU7





CPU8




Cloud Resolving Storm Simulator(CReSS)

AtmosphericForcing

Nagoya Univ. (Tsuboki and Sakakibara, 2003)
DX = 4km
IniOal and lateral B.C.: RANAL from NPD/JMA

Surface Wave Model

CPU8


CPU7

Momentum

SST

OCEAN
MODEL

CPU1

CPU6

Heat &
Water ﬂux

Heat &
Water ﬂux

SST

CPU5

Cloud Resolving Storm Simulator(CReSS)

Momentum
Momentum

CPU4

Atmosphere
Model
Momentum

Momentum

Miami Univ. / USA (Donelan etet al, 2012)
Vectorelized for ES
DX = 4km

OCEAN
MODEL

(NHOES)

JAMSTEC (Aiki and Yamgata, 2004)
DX = 4km, DZ = 2 m for top 100 m
IniOal and lateral B.C.: JCOPE2 reanalysis:
realisOc 3D structure of Kuroshio Current / SST in western North Paciﬁc

SURFACE WAVE MODEL
SST

SST

NonHydrostaOc Ocean model for the Earth Simulator

Heat &
Water ﬂux

Momentum

OCEAN
MODEL

IniOal and lateral boundary condiOons for OCEAN MODEL w/o Odes

126

Heat &
Water ﬂux

Sullivan & McWilliams (2010)

Surface Wave Model Donelan et al. 2012 / Miami Univ.

E ( x, y,! , " )distributed in 4-dimensional space:high computational cost

E
θ
σ

 

Wave energy
Wave direction [rad]24 grid

Wave frequency [1/s] 36 gridSpectral distribution of wave energy
at a fixed point southwest ofa hurricane translating north-westward(green arrow)
Wave direction (contour) is not
the same as wind direction (red arrow) 

Simulation with wave

Observed Current Speed

T0505 Haitang(2005.07.14-)

 

Before typhoon

10-m depth

After typhoonColor: Significant Wave Height (m)Contour: Wind Speed

Observed Temperature

Color: SST
Contour: Sea Surface Pressure

Morimoto et al. (2009) 

E3.4 Visualiza?on

Simulation
with wave

Open-source Sooware

GrADS (Grid Analysis and Display System)

T1013 Megi
(2010.10.14-)

Supported ﬁle formats: binary (stream or sequenOal), GRIB, NetCDF etc.
Supported remote-access protocol: OPeNDAPColor: SST
Contour:
Sea Surface Pressure

hZp://cola.gmu.edu/grads/

Minimum startdata ﬁle + control ﬁletype each command
Medium startdata ﬁle + control ﬁleexec command-set ﬁle (no loop)
Experienced startdata ﬁle + control ﬁlerun program ﬁle (with loop)

Color:
Significant Wave Height (m)Contour: Wind Speed

Smart start
remote access to data
without control ﬁle

127



T1013 Megi (2010.10.14-)

Color: 6-day Change of SST
Contour: Sea Surface Pressure
Simulation without wave
hZp://apdrc.soest.hawaii.edu

Simulation with wave

Satellite observation





E3.6 Useful metrics

Smart start
remote access to data
without control ﬁleWind velocity (QuickSCAT, Atmosphere Model)
Signiﬁcant wave height (WAVEWATCHIII)
understand wind wave
Sea surface temperature (OISST, HadSST, NGSST, Ocean Model)
Sea surface height (TOPEX/ERS/Jason 1, Aviso, Ocean Model)
understand geostrophic velocity
Ekman transport (QuickSCAT, Atmosphere Model)
understand coastal upwelling/downwellinguh mix = τ xwind / f
vh mix = −τ ywind / f

E3.6 Useful metrics (con?nued)

'reinit''open cmodel_result.ctl’'set display color white’
t=37
while ( t<150 )'c’'set parea 0.4 5.4 0.5 8.0 ’
Grads'set t 't
command:'set gxout shaded'prinOm'set lev -1000''set clevs -5 -4 -3 -2 -1 0 1 2 3 4 5''d vel_wp*1000’'prinOm tmp.gif x1600 y1200’
if (t < 100)'!mv tmp.gif im-0't
else'!mv tmp.gif im-'t
endif
t=t+3
endwhile'!convert -loop 0 -delay 20 -colors 11 im-* anime.gif''!rm im-*’

High-pass ﬁltered ocean current velocity (Ocean Model)
understand inerOal oscillaOon, Odal internal wave
Mixed layer depth (Argo, Ocean Model)
deﬁniOon: ΔT=0.5 or Δρ=0.03 kg/Heat content (Argo, Ocean Model)
understand tropical cyclone0HC ≡ ρ0Cp ∫ (T − T26 )dzz 26

Cp = 4178

 

E3.7 How to make anima?on

[JK −1kg −1 ]BoZom topography (ETOPO, GEBCO)

128

Unix command: convert