Hydrological Aspects Concerning The GCM/RCM

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Hydrological Aspects ConcerningThe GCM/RCM
INTERNATIONAL WORKSHOP THE DIGITIZATION OF
HYSTORICAL CLIMATE DATA, THE NEW SACAD 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
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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.
Change in snow accumulation condition
Thermal expansion of sea water
Change in evapotranspiration
Melting of glaciers, ice caps and ice sheets
Sea level rise
Increase of precipitation
More intense typhoons
Earlier snow melt and reduction of discharge
More frequent heavy rains and droughts

Change in water use pattern
Increase of river flow rate
More frequent storm surges and coastal erosions
More frequent floods
Higher risk of drought
More serious sediment disasters
Source Okada, 2008
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Two Modeling Systems1. Climate Model
(GCM/RCM)2. Hydrological Model

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

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Global Climate Models
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Hydrological Models
Target
Event to Continuous Model Lumped to Distributed
Model Conceptual to Physical Model
B Model
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D O W N S C A L I N G
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Climate Change Information
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Design Hydrograph
Design Rainfall
Current Design Rainfall
Future Design Rainfall under Climate Change
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Climate Change
Hydrological Model
Current Climate
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Existing Gaps Between GCMs ability and Hydrology
Need
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Some Models Resolution Created By Australia
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Spatial Scales Mismatch
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Temporal Scales Mismatch
Temporal Scales Seasonal Annual Monthly Daily
Hourly Minute
Hydrological Model
GCMs
Hydrological Importance Increases
GCMs Ability Declines
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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.
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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.

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Declining return period by increasing rainfall
Impact of climate change
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
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Rainfall probability sheets
Rainfall amount
r
Source Okada, 2008
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Changing river discharge
Impact of climate change
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
River run-off
Future
Present
Even if the rice paddy preparation season is
advanced, available river run-offs in the demand
season are insufficient.
Wasteful discharges
Jan
July
Apr
Oct
Rice paddy preparation
Water in storage
Full
Empty dams
Present
Unable to store
Future
Source Okada, 2008
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Impact of climate change on water quality
Impact of climate change
Source Okada, 2008
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Identification of the Climate Change in Java
Island
Source RCWR-MPW
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TREND OF MAXIMUM DAILY RAINFALL IN JAVA ISLAND
Catatan

• 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

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Analysis of Future Precipitation affected by
Climate Change on Citarum River Basin, Indonesia

• ADB Intern Yutaka Araki

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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
-12,000km2 basin area -3 hydroelectric
dams -1400MW -400,000ha Irrigation -80 of
Jakartas water
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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

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SRES(Special Report on Emissions Scenarios)

• A1?High economic growth?
• A1FIenphasis 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 role
• B2?Local self-reliance and stronger communities?
• shift toward local and regional
  decision-making structures and institutions,

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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 - -
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PCM (USA)
Target Area
?Citarum River Basin
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2
5
4
3
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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

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No rainfall days
12UP
number of model which shows increase number of model which shows increase 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
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Heavy rainfall days (gt50mm/day)
number of model which shows increase number of model which shows increase 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
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Annual rainfall
number of model which shows increasing rainfall number of model which shows increasing rainfall number of model which shows increasing rainfall
A1B 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) number of model which shows increasing fluctuation (root-mean-square deviation) 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)
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Seasonal rainfall
number of model which shows decreasing trend (Dry season) number of model which shows decreasing trend (Dry season) number of model which shows decreasing trend (Dry season) number of model which shows increasing trend (Rainy season) number of model which shows increasing trend (Rainy 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)
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Longest consecutive no rainfall days
number of model which shows increase number of model which shows increase 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
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Probablerainfall
A1B A1B B1 B1
2046-2065 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 1.18
10-year probable rainfall 1.20 1.35 1.15 1.2
100-year probable rainfall 1.20 1.36 1.17 1.18
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Incremental Ratio of Daily Probable Rainfall
(10year), A1B,50years later, from 17 models
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Flood Simulation
Area Citarum Upper Basin Return period
10 years Climate Current and 50
years later(A1B)
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Design Hydrograph
Design Rainfall
Current Design Rainfall
Future Design Rainfall under Climate Change
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1.2
Climate Change
Hydrological Model
Current Climate
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Citarum Upper Basin
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Flood Simulation
Orange Current Design Flood Purple Future
Design Flood
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• 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
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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

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Climate change?
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Climate Change -Trend analysis of daily point and
basin rainfall extremes
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Climate Change - Trend analysis annual rainfall
in Bandung basin, Period 1879-2007
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Climate Change - Seasonal rainfall in Citarum u/s
Nanjung, Period 1879-2010
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Rainfall characteristics

• Lessons learnt from the 2009-2010 flood season.

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Bandung basin hydrology

• Historic floods not related to basin wide
  rainfall
• Floods relate to local rainfall

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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.
SUMMARY
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.
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Thank you for your kind attention !
KARIKATUR KOMPAS/ Sabtu 10 Februari 2007