Hydrological Aspects Concerning The Global/Regional

HYDROLOGICAL ASPECTS Concerning The GCM/RCM INTERNATIONAL WORKSHOP THE DIGITIZATION OF HYSTORICAL CLIMATE DATA, THE NEW SACA&D DATA BASE AND CLII IN THE ASEAN REGION 02-05 APRIL, CITEKO – BOGOR, INDONESIA Dr. William M. Putuhena Experimental Station for Hydrology and Water Management RESEARCH CENTER FOR WATER RESOURCES MINISTRY OF PUBLIC WORKS

Mechanism of global warming and climate change Large volumes of greenhouse gas emissions cause CO2 concentration in the air, increase heat absorption, and result in temperature rise, i.e. global warmings.

Melting of glaciers, ice caps and ice sheets

Thermal expansion of sea water

Change in evapotranspiration

Change in snow accumulation condition

Sea level rise More intense typhoons

Increase of precipitation

More frequent heavy rains and droughts

Earlier snow melt and reduction of discharge

Change in water use pattern Increase of river flow rate

More frequent storm surges and coastal erosions

More frequent floods

More serious sediment disasters

Higher risk of drought

Source: Okada, 2008

To provide a comprehensive understanding of the climate change impact on water resources

Two Modeling Systems 1. Climate Model (GCM/RCM) 2. Hydrological Model

Global Climate Models

Hydrological Models Target

Event to Continuous Model Lumped to Distributed Model Conceptual to Physical Model

B Model

D O W N S C A L I N G

Global Climate Regional

Climate

Local Climate

6

Hydrological Modeling

Data

Climate Change Information

Water resources condition in the future

Design Rainfall Current Design Rainfall

Design Hydrograph

Future Design Rainfall under Climate Change Discharge (m^3/s)

1

Hydrological Model

Climate Change

Current Climate

T

Existing Gaps Between GCMs ability and Hydrology Need

Some Models Resolution Created By Australia

Spatial Scales Mismatch

Temporal Scales Mismatch Temporal Scales

GCMs

Hydrological Model

GCMs Ability Declines

Hydrological Importance Increases

Seasonal Annual Monthly Daily Hourly Minute

Data Feed NWP

Satellite

Radar

Telemetry

accuration decreases

Lead Time

River discharge

Flood

Time

Present condition

Flood forecasting Detections

Run-off analysis

Warning

Response

Vertical Scales Mismatch GCMs Tools for Atmosphere/ Ocean Modeling

Hydrological Model Tools for Surface Earth Modeling

GCMs accuracy decreases from free tropospheric variables to surface variables, while the variables at the ground surface have direct use in water balance computations.

Working Variables Mismatch GCMs accuracy decreases from climate related variables, i.e. wind , temperature, humidity and air pressure to precipitation evapotranspiration , runoff and soil moisture, while the later variables are of key importance in hydrologic regimes.

Impact of climate change

Declining return period by increasing rainfall

Return period of flood is declining by increasing rainfall in the future. As a result, future flood safety level is estimated to decrease. 【Image of declining return period at a certain area】

Maximum daily rainfall × 1.2

Return period (year)

future

current

100 current data

projected data

50 Rainfall probability sheets

Source: Okada, 2008

r

Rainfall amount

Impact of climate change

Changing river discharge Decreasing run-offs during the peak demand season Deviation from traditional water use patterns will be required State of river run-offs after global warming (estimated) Earlier spring flooding

Decreasing river run-offs Future

River run-off

Wasteful discharges Jan

Source: Okada, 2008

Even if the rice paddy preparation season is advanced, available river run-offs in the demand season are insufficient.

Apr Full

Water in storage

Present

July Rice paddy preparation

Oct

Empty dams Unable to store

Future

Present

Impact of climate change

Impact of climate change on water quality

Use of fossil fuel, etc.

Global warming

Water temperature rise (remaining warm)

Temperature rise Increase of E. coli

Increase of pests

Fixed thermocline position

Shifts in precipitation patterns

Decrease of winter ice cover (increasing light transmission) From urban areas ←　increased diffusion of nitrogen/phosphorus↓

Decreasing circulation in lakes

Landslide in rain storm

Soil erosion Bottom sedimentation of remains Increased turbidity Decrease of bottom-layer DO Leaking iron/ manganese

Water safety Decrease of river DO

Leaking hazardous substances

Turbidity Smell/ taste Color

Flux of hazardous substances Water safety

Source: Okada, 2008

Savory water

Products of treatment

Increasing pesticide leaks with their increased use

Flux into forests/soil (nitrogen saturation) NO3-N leaking into rivers upstream

Phytoplankton proliferation

Risk of infectious diseases

Changing nitrogen cycle in the atmosphere

Identification of the Climate Change in Java Island Rainfall Data Yearly

Seasonal DRY

Monsoon

Storm

WET

DAILY MAX DJF

TEST FOR THE TREND 1916-1980 1981-2000

MAM

JJA

TEST FOR THE CHANGES OF THE DISTRIBUTION 1916-1940 1941-1970 1971-2000

SON TEST FOR THE TREND

MAP (result of the test) Source: RCWR-MPW

TREND OF MAXIMUM DAILY RAINFALL IN JAVA ISLAND

Catatan: • Data • Metode

:Seri data hujan harian maksimum tahunan dari 1600 buah pos hujan (1916 2004) yang sudah lolos uji :Non Parametrik Tau Kendall dengan tingkat kepercayaan 95 %

Analysis of Future Precipitation affected by Climate Change on Citarum River Basin, Indonesia ADB Intern Yutaka Araki

Analysis on Citarum, Indonesia ・Most strategic river basin ・Climate Change could lead to more severe and frequent flooding, and raise sea level in the river mouth

-12,000km^2 basin area -3 hydroelectric dams -1400MW -400,000ha Irrigation -80% of Jakarta’s water

Analysis on Citarum, Indonesia Target period ・50 & 80 years later (2046-2065, 2081-2100 (+1981-2000))

・based on 2 CO2-emission-scenario - SRES A1B & B1

Tools ・17(/25 )GCMs in CMIP3

ＳＲＥＳ (Special Report on Emissions Scenarios) A1｢High economic growth｣ Globalization A1FI:enphasis on fossil fuel A1B: Balanced energy use A1T: Non fossil fuel.(Technical innovation in Energy) A1 B1 A2｢Differentiated world｣ slower technological change, less emphasis on economic, social, and cultural interactions between EnvironmentEconomyregions, Economic growth is uneven oriented oriented B1｢Sustainable development｣ pay increased attention to the environmental, Technological change plays an important role A2 B2 B2｢Local self-reliance and stronger communities｣ shift toward local and regional decision-making structures and institutions,

Regionalization

Originating Group(s)

Country

CMIP3 I.D.

20c3m

SRES A1B

Beijing Climate Center

China

BCC-CM1

-

-

Bjerknes Centre for Climate Research

Norway

BCCR-BCM2.0

-

-

National Center for Atmospheric Research

USA

CCSM3

1980-1998

2046-2064,2080-2098

Canadian Centre for Climate Modelling & Analysis

Canada

CGCM3.1(T47)

1981-1999

2046-2064,2081-2099

Canadian Centre for Climate Modelling & Analysis

Canada

CGCM3.1(T63)

1981-1999

2046-2064,2081-2099

Météo-France / Centre National de Recherches Météorologiques

France

CNRM-CM3

1981-2000

2046-2065,2081-2100

CSIRO Atmospheric Research

Australia

CSIRO-Mk3.0

1981-1999

2046-2064,2081-2099

CSIRO Atmospheric Research

Australia

CSIRO-Mk3.5

1981-1999

2046-2064,2081-2099

Max Planck Institute for Meteorology

Germany

ECHAM5/MPI-OM

1981-2000

2046-2065,2081-2100

Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group.

Germany / Korea

ECHO-G

1979-1997

2044-2062,2078-2096

LASG / Institute of Atmospheric Physics

China

FGOALS-g1.0

-

-

US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory

USA

GFDL-CM2.0

1981-1999

2046-2064,2081-2099

US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory

USA

GFDL-CM2.1

1981-1999

2046-2064,2081-2099

NASA / Goddard Institute for Space Studies

USA

GISS-AOM

1981-2000

2046-2065,2081-2100

NASA / Goddard Institute for Space Studies

USA

GISS-EH

-

-

NASA / Goddard Institute for Space Studies

USA

GISS-ER

-

-

Instituto Nazionale di Geofisica e Vulcanologia

Italy

INGV-SXG

-

-

Institute for Numerical Mathematics

Russia

INM-CM3.0

1981-2000

2046-2065,2081-2100

Institut Pierre Simon Laplace

France

IPSL-CM4

1981-1999

2046-2064,2081-2099

Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC)

Japan

MIROC3.2(hires)

1981-2000

2046-2065,2081-2100

Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC)

Japan

MIROC3.2(medres)

1981-2000

2046-2065,2081-2100

Meteorological Research Institute

Japan

MRI-CGCM2.3.2

1981-1999

2046-2064,2081-2099

National Center for Atmospheric Research

USA

PCM

1980-1998

2046-2064,2080-2098

Hadley Centre for Climate Prediction and Research / Met Office

UK

UKMO-HadCM3

-

-

Hadley Centre for Climate Prediction and Research / Met Office

UK

UKMO-HadGEM1

-

-

Target Area

←Citarum River Basin

1

2

3

4

CCSM3.0 (USA)

5

PCM (USA)

Analysis items • • • • •

Rainfall days over 50,10 mm/day No rainfall days / consecutive no rainfall days Annual rainfall Seasonal rainfall (dry and rainy) Probable daily rainfall (5,10,100 years return) - Flood/City drainage - Irrigation/Drought management - Water Management

No rainfall days

no rainfall days 1.2 1.15 1.1 1.05

12%UP

A1B B1

1 0.95

0.9 1981-2000

2046-2065

number of model which shows increase A1B

80years later

B1

70% (12/17) Likely

50years later

65%

70%

50years later 80years later

(11/17) More likely than not

65%

(12/17) Likely

(11/17) More likely than not

2081-2100

Heavy rainfall days (>50mm/day)

Heavy rainfall days (>50mm/day) 3.5 3 2.5 2

A1B

1.5

B1

1 0.5 0 1981-2000

2046-2065

number of model which shows increase

A1B

50years later 80years later

B1

90%

(9/10) very likely

80%

(8/10) likely

50years later

90% (9/10) very likely

80years later

80% (8/10) likely

2081-2100

Annual rainfall

Annual rainfall 1.1

A1B

1

B1

0.9 1981-2000

A1B B1

A1B B1

2046-2065

number of model which shows increasing rainfall 50years later 53% (9/17) 80years later 59% (10/17) 50years later 53% (9/17) 80years later 65% (11/17) number of model which shows increasing fluctuation (root-mean-square deviation) 50years later 53% (9/17) 80years later 47% (8/17) 50years later 53% (9/17) 80years later 47% (8/17)

2081-2100

Seasonal rainfall Dry season

Rainy season

1.1 1.08 1.06 1.04 1.02 1 0.98 0.96 0.94

1.1 1.08 1.06 1.04 1.02 1 0.98 0.96 0.94 1981-2000

2046-2065

2081-2100

number of model which shows decreasing trend (Dry season) A1B

B1

50years later

53% (9/17)

80years later

65% (11/17)

50years later

59% (10/17)

80years later

41% (7/17)

A1B B1

1981-2000

2046-2065

2081-2100

number of model which shows increasing trend (Rainy season) A1B

B1

50years later

35% (6/17)

80years later

71% (12/17)

50years later

41% (7/17)

80years later

82% (14/17)

Longest consecutive no rainfall days

Longest consecutive no rainfall days 1.4 1.2 1 0.8

A1B

0.6

B1

0.4 0.2 0 1981-2000

2046-2065

number of model which shows increase

A1B

B1

50years later

65% (11/17) ) More likely than not

80years later

65% (11/17) ) More likely than not

50years later

60%

(10/17) ) More likely than not

80years later

60%

(10/17) ) More likely than not

2081-2100

Probable rainfall

nonexceedance probability 99.9999

ECHAM5/MPI-OM, Log-normal Probability Paper (Cunnane)

99.999 99.99 99.9

99 ■20C3M 1981-2000 ▲A1b 2046-2065 ◆A1b 2081-2100 ■B1 2046-2065 ●B1 2081-2100

90 70 50 30

R² = 0.958 R² = 0.9775 R² = 0.9685 R² = 0.8955 R² = 0.9564

10

1 0.1 0.01 0.001 0.0001 10

100

mm/day

A1B

Number of models which show more severe distribution than now

5-year probable rainfall 10-year probable rainfall 100-year probable rainfall

B1

2046-2065

2081-2100

2046-2065

2081-2100

82%

94%

76%

14(/17)

16(/17)

13(/17)

53% 9(/17)

1.18

1.31 1.35 1.36

1.14 1.15 1.17

1.18 1.2 1.18

1.20 1.20

Incremental Ratio of Daily Probable Rainfall (10year), A1B,50years later, from 17 models IPSL-CM4 MIROC3.2(hires) GFDL-CM2.0 CGCM3.1(T63) CCSM3 MIROC3.2(medres) GFDL-CM2.1 CSIRO-Mk3.0 ECHAM5/MPI-OM ECHO-G GISS-AOM MRI-CGCM2.3.2 CNRM-CM3 PCM INM-CM3.0 CSIRO-Mk3.5 CGCM3.1(T47)

Average=1.2 (from 17 models)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Flood Simulation ・Area Citarum Upper Basin ・Return period 10 years ・Climate Current and 50 years later(A1B)

Nanjung Nanjung Dayeuh Dayeuh Kolot Kolot

Majalaya Majalaya

Design Rainfall Current Design Rainfall

1

Design Hydrograph

Future Design Rainfall under Climate Change Discharge (m^3/s)

1.2

Hydrological Model

Climate Change

Current Climate

T

Citarum Upper Basin cibeureum 45 40 35 30 25 20 15 10 5 0

Citepus

Cipamakolan

16

50

14 12

10

40

8

0

20

40

60

80

100

120

Cikapundung

6 2

50 years later(A1B)

30

100

4

Current

20

80

0 0

20

40 Current

60

80

100

120

60

10

Cikeruh

40

50 years later(A1B)

0

20

250

0

0 0

20

40

60

Current

80

100

20

40

120

60

Current

80

100

120

200 150

50 years later(A1B)

100

50 years later(A1B)

50 0 0

20

40

60

Current

80

100

120

50 years later(A1B)

Cidurian 30 25 20

15 10 5 0 0

20

40 Current

60

200

Ciwidey

120

Cisangkuy

Citarik 200 150

50

Increase!

120 100 80

150

40

100

100

140

60

80

50 years later(A1B)

20

0 0

20

40 Current

60

80

100

120

50 years later(A1B)

Cicadas

0 0

20

40

60

80

100

Cisangkuy

120

14 12

200 Current

50 years later(A1B)

10

100

150

8 6

100

4 2

50

0 0

0 0

20

40 Current

60

80

50 years later(A1B)

100

120

20

50

40 Current

60

80

100

120

50 years later(A1B)

Cirasea 80 70 60

50 40 30

0

20 10 0

0

20

40 Current

0

60

20

40 Current

60

80

80

100

120

100

50 years later(A1B)

50 years later(A1B)

120

majalaya-Citarum Main 200 150

100 50 0 0

20

40 Current

60

80

50 years later(A1B)

100

120

Flood Simulation

Orange – Current Design Flood Purple – Future Design Flood

ADB Delft Hydraulics

Institutional Strengthening For Integrated Water Resources Management in the 6 CIS River Basin Territory (Package C)

Upper Citarum Basin Flood Management Project UCBFM Flood Management Strategy ‘No regret’ – urgent program February 25, 2011 JanJaap Brinkman, Deltares

Understanding the basics Is there any change? • Land-use change? – Yes, urbanization • Climate change increasing floods? – No, not yet • Topography change? – Yes, subsidence • River change? – Yes, maintenance and ‘controlled’ river normalization • Flood management change? – Yes, urgently required – ‘space for water management’

Climate change?

Climate Change -Trend analysis of daily point and basin rainfall extremes Annual maximum point and basin daily rainfall extremes in Bandung basin 140 basin rainfall by all stations basin rainfall by BMKG-stations average point extremes by BMKG Linear trend-all stations

Extreme daily rainfall (mm)

120

100

80

60

40

20

0 1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

Climate Change - Trend analysis annual rainfall in Bandung basin, Period 1879-2007 Estimate of annual rainfall in Bandung basin, Period 1879-2007 3500

3000

Annual rainfall (mm)

2500

2000

1500 Annual basin rainfall Period average

1000

500

0 1860

1880

1900

1920

1940

1960

1980

2000

2020

Climate Change - Seasonal rainfall in Citarum u/s Nanjung, Period 1879-2010 Seasonal rainfall (Jan-Mar) in Citarum basin, Period 1979-2010 1400 Jan- Mar 2010 1285 mm 1200

Rainfall (mm)

1000

800

600

400

200

0 1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

Rainfall characteristics Lessons learnt from the 2009-2010 flood season.

Bandung basin – hydrology • Historic floods not related to basin wide rainfall – Floods relate to local rainfall 5-Day rainfall extremes in basin u/s of Dayeuh Kolot with occurrence of 5-day rain-flood damages 250 5 day rainfall causing flood damage

225 200 175

Rainfall (mm)

average 150 125 100 75 50 25 0 1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

SUMMARY Advanced GCM, RCM, and the hydrological model and also methodologies for comprehensive modeling have been developed. The two modeling systems have recently been used for quantification of the hydrological impacts of future climate change. However, the research on hydrological change is still in its infancy both with respect to model accuracy and uncertainty. Traditionally, based on the output of global or regional climate models, hydrological models have been run as stand alone models. This means that the feedbacks to the atmosphere are neglected which has an unknown impact on the predictions of the climate change, particularly at the local scale. New model should be developed by combining the regional climate model and the hydrological model. As part of the integrated model a statistical downscaling and biascorrection method should be developed for conversion of data from large climate grids to small hydrological grids. New methodologies and tools should be developed to enable easier and more accurate use of regional scale climate and hydrological models to address local scale water resources problems.

Thank you for your kind attention !

KARIKATUR: KOMPAS/ Sabtu 10 Februari 2007