HYDROLOGICAL ASPECTS Concerning
The GCM/RCM
Dr. William M. PutuhenaExperimental Station for Hydrology and Water Management
RESEARCH CENTER FOR WATER RESOURCESMINISTRY OF PUBLIC WORKS
Dr. William M. PutuhenaExperimental Station for Hydrology and Water Management
RESEARCH CENTER FOR WATER RESOURCESMINISTRY OF PUBLIC WORKS
INTERNATIONAL WORKSHOP THE DIGITIZATION OF HYSTORICAL CLIMATE DATA, THE NEW SACA&D DATA BASE AND CLII
IN THE ASEAN REGION02-05 APRIL, CITEKO – BOGOR, INDONESIA
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.
Increase of precipitation
Change in snow accumulation condition
Thermal expansion of sea water
Change in evapotranspiration
Melting of glaciers, ice caps and ice sheets
More intense typhoons
More frequent storm surges and coastal erosionsMore frequent storm surges and coastal erosions
Increase of river flow rate
More frequent floodsMore frequent floods More serious sediment disastersMore serious sediment disasters
Higher risk of droughtHigher risk of drought
Earlier snow melt and reduction of discharge
Change in water use pattern
More frequent heavy rains and droughts
Sea level rise
Source: Okada, 2008
Two Modeling Systems1. Climate Model (GCM/RCM)2. Hydrological Model
To provide a comprehensive understanding of the climate change impact on water resources
To provide a comprehensive understanding of the climate change impact on water resources
Global Climate Models
Hydrological Models
Target
Event to Continuous ModelLumped to Distributed ModelConceptual to Physical Model
B Model
DOWNSCALING
6
Climate Change Information
Climate Change Information
Discharge(m^3/s)
T
Current Design Rainfall
Future Design Rainfall under
Climate Change
Design HydrographDesign HydrographDesign RainfallDesign Rainfall
1
HydrologicalModel
Current Climate
Climate Change
Existing Gaps Between GCMs ability and Hydrology Need
Some Models Resolution Created By Australia
Spatial Scales Mismatch
Temporal Scales Mismatch
Temporal ScalesSeasonal
Annual
Monthly
Daily
Hourly
Minute
GCMs Hydrological Model
GCMs Ability Declines
Hydrological Importance Increases
Data Feed
Telemetry
Flood
Time
Rive
r dis
char
ge
accuration decreases
Lead Time
Detections Run-off analysis Warning Response
Flood forecasting
Present condition
NWP Satellite Radar
Vertical Scales Mismatch
GCMs
Hydrological Model
Tools for Atmosphere/ Ocean Modeling
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.
Declining return period by increasing rainfall
Rainfall amount
Return period (year)Maximum daily rainfall × 1.2
current future
100
50
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 】
r
Rainfall probability sheets
projected datacurrent data
Impact of climate change
Source: Okada, 2008
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)
Jan July Apr Oct
Wasteful discharges
Present
Rice paddy preparation
Decreasing river run-offs
Even if the rice paddy preparation season is advanced, available river run-offs in the demand season are insufficient.
Present Future Unable to store
Empty dams
Full
River run-off
Water in storage
Earlier spring flooding
Future
Changing river discharge Impact of climate change
Source: Okada, 2008
Global warming
Water temperature rise(remaining warm)
Fixed thermoclineposition
Decreasingcirculation in lakes
Increase ofE. coli
Decrease of winter ice cover(increasing light transmission)
Temperature rise
Increase of pests
Risk of infectiousdiseases
Increasing pesticide leaks withtheir increased use
Decrease of river DO Leaking hazardoussubstances
Decrease ofbottom-layer DO
Bottom sedimentationof remains
Phytoplankton proliferation
Water safety
From urban areas← increased diffusion of
nitrogen/phosphorus↓
Flux ofhazardoussubstances
Leaking iron/manganese
Landslide inrain storm
Shifts inprecipitation patterns
Soil erosion
Increasedturbidity
Use of fossilfuel, etc.
Changingnitrogen cycle inthe atmosphere
Flux into forests/soil(nitrogen saturation)
NO3-N leaking intorivers upstream
Savory water
Products of treatment
Turbidity
Smell/taste
Color
Water safety
Impact of climate change on water quality Impact of climate change
Source: Okada, 2008
Rainfall Data
SeasonalSeasonal MonsoonMonsoon StormStorm
DRY WETWET
DJFDJF MAMMAM JJAJJA SONSON
DAILY MAXDAILY MAX
TEST FOR THE TREND1916-19801981-2000
TEST FOR THE TREND1916-19801981-2000
TEST FOR THE CHANGES OF THE DISTRIBUTION
1916-19401941-19701971-2000
TEST FOR THE CHANGES OF THE DISTRIBUTION
1916-19401941-19701971-2000
TEST FOR THE TRENDTEST FOR
THE TREND
MAP (result of the test)
YearlyYearly
Identification of the Climate Change in Java IslandIdentification of the Climate Change in Java Island
Source: RCWR-MPW
TREND OF MAXIMUM DAILY RAINFALL IN JAVA ISLANDTREND OF MAXIMUM DAILY RAINFALL IN JAVA ISLAND
• Data :Seri data hujan harian maksimum tahunan dari 1600 buah pos hujan (1916 2004) yang sudah lolos uji• Metode :Non Parametrik Tau Kendall dengan tingkat kepercayaan 95 %
Catatan:
Analysis of Future Precipitation Analysis of Future Precipitation affected by Climate Change on Citarum affected by Climate Change on Citarum
River Basin, IndonesiaRiver Basin, Indonesia
ADB Intern Yutaka Araki
・ 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, IndonesiaAnalysis on Citarum, Indonesia
Analysis on Citarum, IndonesiaAnalysis on Citarum, Indonesia
Target periodTarget period
・ 50 & 80 years later(2046-2065, 2081-2100 (+1981-2000))
・ based on 2 CO2-emission-scenario
- SRES A1B & B1ToolsTools
・ 17(/25 )GCMs in CMIP3
A1 「 High economic growth 」 A1FI:enphasis on fossil fuel A1B: Balanced energy use A1T: Non fossil fuel.(Technical innovation in Energy)A2 「 Differentiated world 」 slower technological change, less emphasis on
economic, social, and cultural interactions between regions, Economic growth is uneven
B1 「 Sustainable development 」 pay increased attention to the environmental,
Technological change plays an important roleB2 「 Local self-reliance and stronger communities 」 shift toward local and regional decision-making
structures and institutions,
SRESSRES(Special Report on Emissions Scenarios)
Globalization
Regionalization
Environment-oriented
Economy-oriented
A1 B1
B2A2
Originating Group(s) Country CMIP3 I.D. 20c3m SRES A1BBeijing Climate Center China BCC-CM1 - -
Bjerknes Centre for Climate Research Norway BCCR-BCM2.0 - -
National Center for Atmospheric Research USA CCSM3 1980-19982046-2064,2080-
2098
Canadian Centre for Climate Modelling & Analysis Canada CGCM3.1(T47) 1981-19992046-2064,2081-
2099
Canadian Centre for Climate Modelling & Analysis Canada CGCM3.1(T63) 1981-19992046-2064,2081-
2099
Météo-France / Centre National de Recherches Météorologiques France CNRM-CM3 1981-20002046-2065,2081-
2100
CSIRO Atmospheric Research Australia CSIRO-Mk3.0 1981-19992046-2064,2081-
2099
CSIRO Atmospheric Research Australia CSIRO-Mk3.5 1981-19992046-2064,2081-
2099
Max Planck Institute for Meteorology Germany ECHAM5/MPI-OM 1981-20002046-2065,2081-
2100Meteorological Institute of the University of Bonn, Meteorological
Research Institute of KMA, and Model and Data group. Germany / Korea
ECHO-G 1979-19972044-2062,2078-
2096LASG / Institute of Atmospheric Physics China FGOALS-g1.0 - -
US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory
USA GFDL-CM2.0 1981-19992046-2064,2081-
2099US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics
LaboratoryUSA GFDL-CM2.1 1981-1999
2046-2064,2081-2099
NASA / Goddard Institute for Space Studies USA GISS-AOM 1981-20002046-2065,2081-
2100NASA / 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 1981-20002046-2065,2081-
2100
Institut Pierre Simon Laplace France IPSL-CM4 1981-19992046-2064,2081-
2099Center 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) 1981-20002046-2065,2081-
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)
1981-20002046-2065,2081-
2100
Meteorological Research Institute Japan MRI-CGCM2.3.2 1981-19992046-2064,2081-
2099
National Center for Atmospheric Research USA PCM 1980-19982046-2064,2080-
2098Hadley Centre for Climate Prediction and Research / Met Office UK UKMO-HadCM3 - -Hadley Centre for Climate Prediction and Research / Met Office UK UKMO-HadGEM1 - -
PCM (USA)CCSM3.0 (USA)
1 2
43 5
←←Citarum River Basin
Target AreaTarget Area
Analysis itemsAnalysis 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 No rainfall daysdays
number of model which shows increase
A1B 50years later 70% (12/17) Likely
80years later 65% (11/17) More likely than not
B1 50years later 70% (12/17) Likely
80years later 65% (11/17) More likely than not
12%UP
Heavy rainfall Heavy rainfall days days (>50mm/day)(>50mm/day)
number of model which shows increase
A1B 50years later 90% (9/10) very likely
80years later 80% (8/10) likely
B1 50years later 90% (9/10) very likely
80years later 80% (8/10) likely
Annual Annual rainfallrainfall
number of model which shows increasing rainfallA1B 50years later 53% (9/17)
80years later 59% (10/17)B1 50years later 53% (9/17)
80years later 65% (11/17)number of model which shows increasing fluctuation
(root-mean-square deviation)A1B 50years later 53% (9/17)
80years later 47% (8/17)B1 50years later 53% (9/17)
80years later 47% (8/17)
Seasonal rainfallSeasonal rainfall
number of model which shows decreasing trend (Dry season)
number of model which shows increasing trend (Rainy season)
A1B 50years later 53% (9/17) A1B 50years later 35% (6/17)
80years later 65% (11/17) 80years later 71% (12/17)
B1 50years later 59% (10/17) B1 50years later 41% (7/17)
80years later 41% (7/17) 80years later 82% (14/17)
Longest Longest consecutive consecutive no rainfall daysno rainfall days
number of model which shows increase
A1B 50years later 65% (11/17) ) More likely than not
80years later 65% (11/17) ) More likely than not
B1 50years later 60% (10/17) ) More likely than not
80years later 60% (10/17) ) More likely than not
R² = 0.958R² = 0.9775R² = 0.9685R² = 0.8955R² = 0.9564
10 100
ECHAM5/MPI-OM, Log-normal Probability Paper (Cunnane)
0.0001
99.9999
99.999
99.99
99.9
99
90
70
0.001
0.01
0.1
1
10
30
50
0.0001
99.9999
99.999
99.99
99.9
99
90
70
0.001
0.01
0.1
1
10
30
50
0.0001
99.9999
99.999
99.99
99.9
99
90
70
0.001
0.01
0.1
1
10
30
50
0.0001
99.9999
99.999
99.99
99
90
70
0.001
0.01
0.1
1
10
30
50
0.0001
99.9999
99.999
99.99
99
90
70
0.001
0.01
0.1
1
10
30
50
■20C3M 1981-2000▲ A1b 2046-2065◆ A1b 2081-2100■B1 2046-2065●B1 2081-2100
mm/day0.0001
99.9999
99.999
99.99
99
90
70
0.001
0.01
0.1
1
10
30
50
nonexceedance probability
A1BA1B B1B12046-2065 2081-2100 2046-2065 2081-2100
Number of models which show more severe distribution
than now
82%14(/17)
94%16(/17)
76%13(/17)
53%9(/17)
5-year probable rainfall 1.18 1.31 1.14 1.1810-year probable rainfall 1.20 1.35 1.15 1.2100-year probable rainfall 1.20 1.36 1.17 1.18
ProbableProbablerainfallrainfall
Incremental Ratio of Daily Probable Rainfall (10year), A1B,50years later, from 17 models
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
CGCM3.1(T47)CSIRO-Mk3.5
INM-CM3.0PCM
CNRM-CM3MRI-CGCM2.3.2
GISS-AOMECHO-G
ECHAM5/MPI-OMCSIRO-Mk3.0GFDL-CM2.1
MIROC3.2(medres)CCSM3
CGCM3.1(T63)GFDL-CM2.0
MIROC3.2(hires)IPSL-CM4
Average=1.2(from 17 models)
Flood SimulationFlood Simulation
Nanjung
Dayeuh Kolot
Majalaya
Nanjung
Dayeuh Kolot
Majalaya
・ Area Citarum Upper Basin・ Return period 10 years・ Climate Current and 50 years later(A1B)
Discharge(m^3/s)
T
Current Design Rainfall
Future Design Rainfall under
Climate Change
Design HydrographDesign HydrographDesign RainfallDesign Rainfall
1 1.2
HydrologicalModel
Current Climate
Climate Change
05
1015202530354045
0 20 40 60 80 100 120
cibeureum
Current 50 years later(A1B)
0
20
40
60
80
100
120
140
0 20 40 60 80 100 120
Ciwidey
Current 50 years later(A1B)
02468
10121416
0 20 40 60 80 100 120
Citepus
Current 50 years later(A1B)
0
50
100
150
200
0 20 40 60 80 100 120
Cisangkuy
Current 50 years later(A1B)
0
20
40
60
80
100
0 20 40 60 80 100 120
Cikapundung
Current 50 years later(A1B)
0
2
4
6
8
10
12
14
0 20 40 60 80 100 120
Cicadas
Current 50 years later(A1B)
0
5
10
15
20
25
30
0 20 40 60 80 100 120
Cidurian
Current 50 years later(A1B)
0
10
20
30
40
50
0 20 40 60 80 100 120
Cipamakolan
Current 50 years later(A1B)
0
50
100
150
200
250
0 20 40 60 80 100 120
Cikeruh
Current 50 years later(A1B)
0
50
100
150
200
0 20 40 60 80 100 120
Citarik
Current 50 years later(A1B)
0
10
20
30
40
50
60
70
80
0 20 40 60 80 100 120
Cirasea
Current 50 years later(A1B)
0
50
100
150
200
0 20 40 60 80 100 120
majalaya-Citarum Main
Current 50 years later(A1B)
Citarum Upper Basin
0
50
100
150
200
0 20 40 60 80 100 120
Cisangkuy
Current 50 years later(A1B)
Increase!
Flood SimulationFlood Simulation Orange Orange – Current Design Flood– Current Design FloodPurplePurple – Future Design Flood – Future Design Flood
Upper Citarum Basin Flood Management ProjectUCBFM
Flood Management Strategy‘No regret’ – urgent program
February 25, 2011JanJaap Brinkman, Deltares
Institutional Strengthening For Integrated Water Resources Management in the 6 CIS River Basin
Territory (Package C)
Delft Hydraulics
ADB
Understanding the basicsIs 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
0
20
40
60
80
100
120
140
1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Ex
tre
me
da
ily
ra
infa
ll (
mm
)
basin rainfall by all stations
basin rainfall by BMKG-stations
average point extremes by BMKG
Linear trend-all stations
Climate Change - Trend analysis annual rainfall in Bandung basin, Period 1879-2007
Estimate of annual rainfall in Bandung basin, Period 1879-2007
0
500
1000
1500
2000
2500
3000
3500
1860 1880 1900 1920 1940 1960 1980 2000 2020
An
nu
al
rain
fall
(m
m)
Annual basin rainfall
Period average
Climate Change - Seasonal rainfall in Citarum u/s Nanjung, Period 1879-2010
Seasonal rainfall (Jan-Mar) in Citarum basin, Period 1979-2010
0
200
400
600
800
1000
1200
1400
1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Ra
infa
ll (
mm
)
Jan- Mar 2010 1285 mm
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
0
25
50
75
100
125
150
175
200
225
250
1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Ra
infa
ll (
mm
)
5 day rainfall causing flood damage
average
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 bias-correction method should be developed for conversion of data from large climate grids to small hydrological grids.
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 bias-correction 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.
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.
SUMMARY
KARIKATUR: KOMPAS/ Sabtu 10 Februari 2007
Thank you for your kind attention !