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