Flood Today, and the Water Scarcity

on the Day After Tomorrow

Open House 2007 Institute of Industrial Science

University of Tokyo

Oki and Kanae Laboratory (Hydrology and Water Resources Engineering)

Theme3 New Observations

Oki & Kanae Lab, IIS, Univ of Tokyo

Author: Shinta SETO, Hyungjun KIM 2007

Integrated water resources model uLessons by flood disaster in 2006 uA pilot water management system

Virtual Water Trade Flood in Japan

Flood Today, and the Water Scarcity on the Day After Tomorrow

Welcome to Oki and Kanae Laboratory ! Oki and Kanae Laboratory is always seeking for better scientific

understanding of water cycle and water resources and trying to deliver it to the society. By using various tools of natural and social sciences, we are struggling with many problems on different scales from global to micro. Our laboratory consists of 31 members led by Prof. Taikan Oki and Associate Prof. Shinjiro Kanae.

Flood in Thailand

uHeat island increases torrential rainfall ? uFlood risk mapping all over Japan

nNatural and anthropogenic water cycle nOptimization of crop calendar

nJapan relies on foreign water resources nWhen you eat meat, water table decreases.

What is Hydrology?

Isotope

In a word, Hydrology is a study of terrestrial water cycle. To estimate the flux and storage amount of water is an essential problem and also an ultimate goal of hydrology. Left figure shows our latest estimate of it (Oki and Kanae, 2006, Science). It represents not only natural water cycle components such as precipitation but anthropogenic water cycle components such as irrigation water intake from river. Human impact on water cycle has been getting heavier, and it became a hot topic of hydrology.

Real­time simulations uWeather forecast to flood forecast uMonitoring rainfall from the space

Impact of climate change n4 billion people will be under water stress nHeavy snowfall and global warming

lThe fifth world best high precision H2O measurement. l“Isotope flux” can separate evaporation & transpiration.

Field observations lObservation network covering various climate and land cover types.

In Japan, river levee and dams have been well established up to today. However, only such hard measures never fully secure our life. In the world, a large number of people live along rivers without levee. Hydrologists should consider soft measures such as early flood warning system.

Theme 1 Flood Theme 2 Water Scarcity Overview of Open House 2007

Theme4 Experience Hydrology Measurement of soil temperature

A miniature of global water cycle Let’s circulate global water cycle by yourself. (for children)

You can try to make an observation tool to measure soil temperature

Thank you for your interest.

Feel free to ask questions !

According to the 4 th report of IPCC, by the end of this century, global average air temperature will increase by 6.4 degrees at most. How will global warming affect the water resources? To give more reliable answer to this question, we are developing an evaluation tool of water demand/supply.

2

prediction

validation analysis

Relating Observation and Simulation

observation observation

phenomena phenomena understanding understanding

model model expression

1km 10m 100km Global

year

century

5cm

Temporal

Temporal

Spatial and temporal scale

day

hour

Portable measurem

ent

Flux Tower

Spatial Spatial

Oki­Kanae Lab. : Hydrology & Measurement Oki & Kanae Lab, IIS, Univ of Tokyo

Hydrology and Hydrological Cycle

How to Measurement the Hydrological Factors？

3.Evapotranspiration

Precipitation is very important hydrological factor incoming water on land surface.

Eddy covariance system at the tower measures evapotranspiration transferred by turbulence.

2. Precipitation 4. Soil moisture 5. Soil water depth

TDR installs in the soil and detects soil water content by difference electronic conductance.

＃TDR（Time Domain

Reflectometry） This sensor put into the water and converts water pressure into water depth. We apply at paddy to monitor irrigation water.

Micro Rain Radar

Rain Rain Gauge Gauge

EC system

Cloud Image by Meteorological Satellites

1. Satellite

Monitoring the Terrestrial Ecosystem

LAI Albedo

Experiment Design ­Which

instrument useful?

Data Application ­ How to use?

Author： Satoshi WATANABE, Jaeil CHO 2007

Through contributing a review article to SCIENCE Journal in 2006, our laboratory has represented the hydrological prospect about the water resource that is necessary for all creatures including human beings in the past, the present and the future in terms of global water circulation. It shows our interpretations about, such as, renewable freshwater resources assessed, virtual water trade, water use change in the future and impacts of climate change.

Hydrology is the science which deals with the water as a circulating resource on the earth, such as rain/snow fall, river flow, evapotranspiration, erosion and accumulation, water quality, and systems of water resources and their interactions. Furthermore, global water problems became one of the most important international tasks since United Nations Millennium Declaration in September of 2000. Considering the current situation, hydrology is in charge of not only ensuring adequate supplies of water to ecosystem and human society but also protecting societies from natural hazard. In this point of view, hydrology is the science delivering earth system science to society.

Green Water (=Invisible water flow)

Blue Water (=Visible water flow)

3

Hydro-meteorological forecasting systems at the Oki/Kanae Lab. Oki & Kanae Lab., IIS, Univ. of Tokyo

The development of hydro-meteorological forecasting and warning systems is a high priority in many countries. An accurate and early warning system can help to minimize disaster damages.

At the Oki & Kanae Lab, several hydro-meteorological early warning systems are under developing on global scale, regional scale, and local scale.

Some results are now real-time available at the website: http://hydro.iis.u-tokyo.ac.jp/LIVE/ for the three systems: Today’s Earth, Today’s Japan and Today’s Indochina.

Today’s Indochina

Today’s Earth Outputs: - Global land surface hydro/energy balance

- river discharge Spatial Resolution: 1 degree (~100 km) Time: - Run every 12 hours

- Give predictions for coming 84 hours (lead time is about 3 days)

Today’s Japan Outputs: similar to Today’s Earth

Spatial Resolution: 0.1 degree (~10 km)

Time: - Run every 6 hours - Give predictions for coming 15 hours (lead time is about 10 hours)

Outputs: - Land surface hydro-energy flux - Coming soon: river discharge, river velocity, river depth, etc.

Spatial Resolution: - 72 km for the mother domain - 24 km for the inner domain

Time: - Run every 6 hours - Give predictions for coming 72 hours (lead time is about 67 hours)

Mother domain

Inner domain

JMA-GSM Japan Meteorological Agency –

Global Spectral Model

Iso-MATSIRO Isotope- Minimal Advanced

Treatments of Surface Interaction and RunOff

TRIP Total Runoff Integrating Pathways

JMA-MSM JMA – MesoScale Model

Iso-MATSIRO

TRIP

MM5-NOAH Fifth Generation Pennsylvania State

Uni/National Center for Atmospheric Research Mesoscale Model – Ncep

Oregon state uni. Air force Hydrologic Research Lab

ATRIP Advanced TRIP

NCEP-GFS National Centers for Environmental

Prediction – Global Forecast System

Author: Thanh NGO-DUC, Kei Yoshimura 2007 4

Introduction and Study Areas

Early Flood Warning System in Japan and Thailand Oki & Kanae Lab, IIS, Univ of Tokyo

Observation and Data Report System

Ping River Basin­ Thailand

River Length：740 km

Catchment Area：36,018 km 2

＊Tributary of Chao Phraya River（162,800 km 2 ）flowing to Gulf of Thailand

Study Area

＊ Chiang Mai ­ one of the major cities along the river

＊Damaged from flood in 2005

Ping river basin with observation stations

Kariyata and Ikarashi River Basins (Niigata)

Kariyata River

River Length：53.5 km

Catchment Area：239.8 km 2

Ikarashi River

River Length：53.5 km

Catchment Area：239.8 km 2

Study Area

＊Arasawa in Niigata Prefecture in between Kariyata and Ikarashi rivers

＊Damaged from flood in 2004

Shinano river basin

Flood Prediction and Legal System for flood warning

Location

Measured Data and

organization responsible

Measurement Method

Frequency of Measurement (Normal)

Frequency of Report (Normal)

Frequency of Measuremen t (Flood)

Frequency of Report (Flood)

Transmis sion Method

Chiang Mai

Rainfall (RID) Manual 1 time/day (7 a.m.)

1 time/day (7 a.m.)

1 time/day (7 a.m.)

1 time/day (7 a.m.)

Cell Phone

Water Level (RID)

A­Automatic B­Manual

A­Continuous B­ 5 times/day (Every 3 hours after 6 a.m.)

A­1 time day B­1 time/day (Both 6 a.m.)

A­All Times B­Hourly

A­Hourly B­Hourly

Cell Phone

Niigata

Rainfall (JMA) Automatic Every 10 min.

Every 10 min.

Every 10 min.

Every 10 min.

Telemetry

Water Level (Niigata Prefectural Office)

Automatic Every 10 min.

Every 10 min.

Every 10 min.

Every 10 min.

Telemetry

RID: Royal Irrigation Department, Thailand; JMA: Japan Meteorological Agency

Chiang Mai ＊Flood prediction and warning by RID and Department of Public Disaster Prevention and Relief, Ministry of Interior. ＊ Based on water level correlation method

Niigata ＊Models are not used for flood prediction. ­First, JMA forecasts the weather and gives flood warning based on rainfall prediction ­Second, Municipality issues the warning judging on whether the resident should evacuate or not.

Flood Mitigation Measures

It can be broadly classified into two categories

­Hard measures representing structural methods

­Soft measures representing non­structural methods

Discussion and Conclusions

Niigata Flood, 2004 ＊ Flooding was due to rainfall and subsequent breakdown of the dike in the river ＊ Inundation starts at areas near the breakdown of dike ＊ The duration between critical rainfall and inundation is very short than Chiang Mai. 0.0

1.0

2.0

3.0

4.0

5.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

P67 W.L. （T­6 hrs.）m

P1 W

.L.(T

hrs

.) m

0

1

2

3

4

5

6

10/0

7/20

04

11/0

7/20

04

12/0

7/20

04

13/0

7/20

04

14/0

7/20

04

m 0

10

20

30

40

50

60

70

mm/hour

Hourly Rainfall

W.L. at Arasawa Flood Alert (Rainfall)

F lood Alert (W.L.)

Critical Situation

0

20

40

60

80

100

120

140

160

180 0

1

2

3

4

5

6

7

8/12/2005

8/13/2005

8/14/2005

8/15/2005

8/16/2005

mm/day m

Daily Rainfall

W.L. at P67

W.L. at P1

Flood Alert (Rainfall)

Flood Alert (W.L.)

Critical Situation

＊ The liberal collaboration between concerned authorities and proper dissemination of information is quite effective in Thailand.

＊The delay in Niigata was due to the absence of quantitative concrete criteria. Niigata city has developed a system in internet that provides real time information on rainfall, water level and dam.

＊Flood warning is always efficient if the lead time of prediction is high so as sufficient evacuation time can be provided to residents.

＊A real­time flood forecasting based on real time observation can be used even in big river basins like Chiang Mai.

Authors: KOIRALA Sujan, ARAI Yuko 2007

Observation and Data Report System

Measurement System of Ping River Basin Some pictures of measurement station in Ping river basin

Predicted Inundation Areas in Chiang Mai

Inundation Areas in Niigata

Figure: Chiang Mai Floods (Ping River)

Niigata Floods (Ikarashi River)

Water Level correlation between P1 and P67 stations in Ping River

Chiang Mai Flood, 2005 ＊ Flooding due to heavy overnight rainfall ＊Inundation occurs gradually from area with low elevation to areas with high elevation ＊There is 6 hours difference between the exceedance of critical level and inundation.

Oki & Kanae Lab carried out a research on issues and characteristics of early flood warning system (EFWS) in Japan and Thailand and proposed improvements on them.

5

Oki & Kanae Lab, IIS, Univ of Tokyo

Author: Naota HANASAKI, Dai YAMAZAKI 2007

IIS/NIES Global Integrated Water Resources Model What about sub­annual variation?

Global warming

Population growth

Economic growth

Change in water resources

Increase in water demand

Background

• Most of earlier global water resources assessments did not deal with sub­annual variation in water resources and use explicitly. •However, an extreme seasonality in both water resources and water use exists in some parts of the world. •Moreover, global warming is projected to alter future temperature, precipitation and snowmelt patterns, and consequently, influence not only the amount but also the timing of water resource availability and use. Annual. water withdrawal

Annual. water resources

Water resources assessment

Index=

Oki and Kanae, 2006

•Grid­based model •6 sub­models

Soi lT emp Soi lMoist

Qs

Qsb

Evap SubSnow

Snowf

Qg

Qle Qh

SWE

Rainf

Qsm

SWnet LWnet

W W max 0.75W max

E

E pot

W

Q sb 1.5

=1.5 W . W ma x

Land surface model 1

Global river model 2

Environmental flow requirement model 3 Stress

TS: Temperature Stress WS: Water Stress

Photosynthesis

Cropping period

Daily Temp. Base Temp.

Cum

ulative Tem

p.

Necessary Temp.

DOY

Daily

Temp. Cumulative Temp.

Cropping period

18 Crop type s

•Withdrawal model 6

Crop model 4

Reservoir operation model 5

demand

Inflow Storage capacity

452 reservoirs Integrated Model

Method

Integrated Water Resources Assessment

1°×1° spatial resolution Daily temporal resolution

Meteorological Input GCM MIROC 3.2 Scenario SRES A1B Period 2001­2050,

3hourly Resolution 1.125°×1.125°

Land Use, Socio­economic data • Population, GDP (SRES A1B scenario) • Industrial water demand, Domestic water demand and Irrigated area was estimated using a simple regression model • Crop type, crop calendar was estimated using an agricultural model

The Earth Simulator (Yokohama, Japan)

Population under a water­stressed condition

Estimated Water demand

Stress increase in 2050 Stress decrease in 2050

Results Two indices for assessment

Withdrawal to water resources ratio Cumulative daily withdrawal to demand ratio

Domestic

Industrial

Agricultural

2000 2025 2050

Withdraw

al [km 3 /yr]

6000

5000

4000

3000

2000

1000

0

Shiklomanov, 2000

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12

水資源量 水需要量

0 20 40 60 80

100 120 140 160

1 2 3 4 5 6 7 8 9 10 11 12

水資源量 水需要量

[mo] [mo]

Ann. withdrawal Ann. resources

Σdaily withdrawal Σdaily demand

CWD=1.0 CWD<1.0

・Conventional Index －Withdrawal to water resources ratio (WWR):

・New Index －Cumulative daily withdrawal to demand ratio (CWD):

Basin A Basin B resources demand

resources demand

WWR is a useful index, but it can not indicate the water stress caused by a gap in the sub­annual distribution of water

resources and water use. Therefore, a new index CWD was devised.

Change in index (between 2010 and 2050)

A global water resources assessment under climate change

High stress in both 2010 and 2050

Low/No stress in both 2010 and 2050 Medium stress in both 2010 and 2050

In the future, How much water will be available? How much water stress does we have?

It is estimated that… Along with global warming, water resource distribution will be change Population growth and economic growth may increase water demand

Stress is increase in Asian monsoon and Sun­ Saharan Africa, where seasonal deviation of precipitation is large.

Population [10 6

]

10000

8000

6000

4000

2000

0

2000 2025 2050

High stress

Medium stress

Low stress

Ratio of high­stressed population

Population with water­stress will increase.

River Discharge

Irrigation water demand Irrigation water supply Environmental flow requirement

Reservoir Release/Storage Evaporation/Soil moisture

Output

6

Virtual Water Virtual Water ～ ～Invisible movement of water resource Invisible movement of water resource～ ～ Oki & Kanae Lab, IIS, Univ of Tokyo Water resource per person in the Middle East is very few. However, their life is Water resource per person in the Middle East is very few. However, their life is established. established. Why? Why?

Prof. Tony Allan (Univ. of London)

They depend for most food on import. Therefore, it is not necessary to use water required in order to produce food. It is like having sold oil and buying water resources.

So to speak, import of food is import of Virtual Water Virtual Water.

Advent of concept as Virtual Water Virtual Water

Virtual Water and, life of Japanese Virtual Water and, life of Japanese Import of Virtual Water Import of Virtual Water to Japan (Virtually required water) to Japan (Virtually required water)

4.6

40.6

10.5

1.3

1.8 1.0

2.4

Others：1.4

Total import amount: 64.3 billion m 3 /yr (Yield per unit in Japan given by table of demand and supply of food in 2000)

(Trade volume given by trade statistics of ministry of finance)

Japan Jordan

3300m 3 /p/yr 150m 3 /p/yr

Population：127 million

Total amount:400km 3 /yr

Population：7 million

Total amount:1km 3 /yr

RFWR (Renewable Fresh Water Resources) / person

Let’s compare water resource between Japan and Jordan・・・

１ 22

１０．５ １０．５ tubs tubs

１１．２ １１．２ tubs tubs

２．９ ２．９ tubs tubs

４．２ ４．２ tubs tubs

０．７ ０．７ tubs tubs

Sanuki­Udon

Kake­ Udon ¥ 290­

341kcal

120L

Hamburger

Teriyaki­burger French­fried R

¥ 430­

911kcal

530L

2 Hamburgers French­fries S

¥ 268­

745kcal

2020L

Medium size ¥ 280­

710kcal

1890L

Soba

Tsukimi­ Soba ¥ 300­

377kcal

750L

How many bath tubs ? How many bath tubs ?

Beef bowl

Water consumption of fast food Water consumption of fast food

Hamburger

1.1

28.9

11.9

1.6

1.5

0.6

3.3

Others：1.1

Total import amount: 53.3 billion m 3 /yr (Yield per unit in Japan given by table of demand and supply of food in 2000)

(Trade volume given by trade statistics of ministry of finance)

Maize

Soybean

Wheat

Rice

Barley

Beef

Pork

Chicken

Virtual Water balance in 2000(m Virtual Water balance in 2000(m 3 3 /p/yr) /p/yr)

The exporting country of virtual water inclines The exporting country of virtual water inclines toward some countries, such as U.S. Canada toward some countries, such as U.S. Canada

Australia. Australia.

Export Import

Virtual Water trade in 2000 Virtual Water trade in 2000 (only main grain) (only main grain)

Caribbean Caribbean

North North America America

Central Central America America

South South America America

West West Africa Africa

Oceania Oceania

East & East & South East South East Asia Asia South South

Asia Asia

USSR USSR

North West North West Africa Africa

Western Western Europe Europe

Middle Middle East East

1~5 5~10 10~15 15~20 20~30 30~50 50< Km 3 /yr

78.5

33.5

46.2

57.5 38.8

36.4

Exporting countries base, shown only over 5 km 3 /yr

By following grain trade statistics in By following grain trade statistics in detail, movement of VW in the world is revealed. detail, movement of VW in the world is revealed.

(based on statistical data of FAO and so on in 2000)

Virtual Virtual Water Water i in n the world the world

Traditional assessment of water resources Traditional assessment of water resources

RFWR per capita in 2000

Estimation from water resources statistics and so on in the world

10 10 3 3 m m 3 3 /p/yr /p/yr

Estimation from water resources statistics and so on in the world

Extreme stress (~1) High stress (1~2) Moderate stress (2~5)

10 10 3 3 m m 3 3 /p/yr /p/yr No stress (5~10) Rich water (10~)

Assessment of water resources in the world Assessment of water resources in the world considered in Virtual Water considered in Virtual Water RFWR per capita in 2000

Poor water resources is Poor water resources is covered by import of grains. covered by import of grains.

VW VW­ ­adjusted water self adjusted water self­ ­sufficiency ratio sufficiency ratio

Really required water: amount of water resources used in exporting countries (producing country) Virtually required water: amount of water resources needed if importing countries (consumer country) will produce same amount of production in own country.

Especially, a great deal of water is used for production of meat.

Above two charts reflect the trend of trade movement after 2000. (ex., Changes due to suspension of beef import from U.S. because of BSE)

Definition of Virtual Water (Oki & Definition of Virtual Water (Oki & Kanae Kanae Lab.) Lab.)

(100 million m 3 /yr)

Amount of import

Water self­sufficiency ratio is given by

domestic quantity of water intake + green water for agriculture

domestic water intake + green water for agriculture + imported VW – exported VW

Reference: AQUASTAT P.H.Gleick, Ed., Water in Crisis(Oxford Univ. Press, 1993)

Water self Water self­ ­sufficiency ratio(WR): Top 10, Major countries, Worst 10 sufficiency ratio(WR): Top 10, Major countries, Worst 10

Green water for agriculture = Rain amount x agriculture area x α

α ： factor. Rate of evapotranspiration in rainfall, where rate is 7/11 based on estimation of Gleick (1993).

Only grain & animal product

Only grain & animal product

Extreme stress (~1) High stress (1~2) Moderate stress (2~5)

No stress (5~10) Rich water (10~)

2000

4 4 years later years later ・・・ ・・・ What kind of water ? What kind of water ?

It seems that Japan has abundant water resources. But, a great amount of food import means that Japan imports a great deal of Virtual Water. Water resources in the world is also important for Japan.

2004

0.93

18.9 1.0

0.43 1.5

Others: 0.45

6.1 Total: 29.5 billion m 3 /yr

Grain & animal product, 2004

Really required water is smaller than Virtually required water, because Really required water is estimated by producer base.

0.001

1.1

0.12

0.055

0.067

Others：0.017

Total：1.5 billion m 3 /yr 0.13

Irrigation water (river + reservoir + fossil water)

Canada：Almost green water Import of VW in each origin

Green water is very large.

Import of Virtual Water Import of Virtual Water to Japan to Japan (Really required water) (Really required water)

Wheat 53 Beef 51 Pork 4.6 Chicken 12

Barley 4.9 Maize 106 Rice 8.3 Soybean 57

Green water River Reservoir Fossil water

Author: Toshiyuki INUZUKA, Yadu POKHREL, Masashi KIGUCHI, Dai YAMAZAKI 2007

2004 Unit: billion m3/yr

about 5 % about 5 % ­ ­ ­ ­ ­ ­

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Kazakhstan

Ireland

Australia

Argentin

a Denmark

Urugu

ay

Canada

Paragu

ay

France

Thailand

U.S.A.

India

Brazil

Germany

China

Japan

Korea

Israel

Malta

Cyprus

Algeria

Kuw

ait

Jordan

U.A.E.

Cape Verde

Djib

outi

Sing

apore

0.8

no data

7

Profile of Oki and Kanae Laboratory Oki & Kanae Lab., IIS, Univ. of Tokyo

Address 153­8505 Be607, IIS, 4­6­1, Komaba, Meguro­ku, Tokyo, JAPAN Phone : 03­5452­6382 Fax : 03­5452­6383 E­mail : webmaster@hydro.iis.u­tokyo.ac.jp Web Page：http://hydro.iis.u­tokyo.ac.jp/

Professors

Professor Taikan OKI Research Topics ・Global Water Resources Assessment Considering Anthropogenic Activities ・Analysis of global water cycle by using stable isotope measurement and remote sensing ・Integrated River and Water Resources Management in Japan, Asia, and the world

CV 1987.3 Bachelor of Engineering (University of Tokyo) 1989.3 Master of Engineering (University of Tokyo) 1989.9 Research Associate (IIS, University of Tokyo) 1995.5 Assistant Professor (IIS, University of Tokyo) 1995.10 JSPS Oversea Researcher (NASA/GSFC） 1997.10 Associate Professor（IIS, University of Tokyo） 2002.4 Associate Professor(Research Institute of Humanity and Nature, also at IIS, University of Tokyo) 2003.12 Associate Professor（IIS, University of Tokyo） 2006.11 Professor（IIS, University of Tokyo)

Ph.D. Thesis 水文・水資源予測のための大気水循環過程に関する研究 (1993.9)

Research Topics ・Land­Atmosphere Interaction and Global Water Cycle Model

・Future Perspective of Water Cycle and Society in Monsoon Asia

・Climate Change and its Impact on Flood and Drought

CV 1994.3 Bachelor of Engineering (University of Tokyo) 1996.3 Master of Engineering (University of Tokyo) 1999.3 Doctor of Engineering (University of Tokyo) 1999.4 JSPS Post­Doctoral Research Fellow 1999.5 Research Associate (IIS, University of Tokyo) 2003.3 Assistant Professor （IIS, University of Tokyo） 2003.11 Associate Professor （IIS, University of Tokyo） 2003.12 Associate Professor （Research Institute of Humanity and Nature, also at IIS, University of Tokyo） 2007.4 Associate Professor (IIS, University of Tokyo)

Ph.D. Thesis Land surface hydrological processes and the variations of water resources in regional climate systems (1999.3)

(Publication and Award lists can be found in our web page)

Other Members

(Staff)

Assistant Professor

Kei YOSHIMURA Technical Associate

Masahiro KOIKE

8

Associate Professor Shinjiro KANAE

Secretaries 2

Research Fellow 10

(Students)

Doctor Course 4

Master Course 10

Undergraduate 1

(As of May 2007) Access