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PROJ ECTI ON OF TH E IMPACT OFCLIM AT E CHAN GE ONLI MA TE CHA NGE ON

THE HY DROLOGIC REGIM E AND WAT ER RESOURCESOF

A GEOGRAPHI CAL REGIONGEOGRAPHICAL REGION:A DYN AM ICAL APPROACH

The views expressed in this paper/presentation are the views of the author and do not necessarily reflect the views or policies of the

M.L. K avv as , Z.Q.Chen, N.Oha r a, E.TanCal i fo r n ia Hydro log ic Researc h Labora t o r y (CHRL)

, , .accuracy of the data included in this paper and accepts no responsibility for any consequence of their use. Terminology used may notnecessarily be consistent with ADB official terms.

Cal i fo r n ia Hydro log ic Researc h Labora t o r y (CHRL)A .J a m a l l u dd i n B in Sh a ab a n, M .Z a k i M .A m i n

Na t iona l Hydrau l i c Resea rc h In s t i t u t e o f Ma lays i a (NAHRIM)

Introduction

• Weather - day to day fluctuations in thestate of the atmos here

• Climate - atmospheric state averaged over a

• Climate Change - change in averaged

natural and anthropogenically induced)

Modeling Climate Change

The main tools for simulating the global climate evolution in time andspace are the coupled Atmosphere-Ocean Global Circulation Models

Confidence in AOGCMs is due to the physical basis of these models in,skills in simulating the observed historical climate and past climatechanges.

At large spatial scales there is confidence that AOGCMs provide crediblequantitative estimates of the change in the future climate.

nce m - s s s ar e s mu a ng

gradually changing climate conditions under variousgreen ouse em ss on scenar os.

SRES (Special Report on Emissions Scenarios) (2001)

•Rapid economic growth.•A global population that reaches 9 billion in 2050 and then gradually declines.•The quick spread of new and efficient technologies.•A convergent world - income and way of life converge between regions. Extensive social andcultural interactions worldwide.

There are subsets to the A1 family based on their technological emphasis:•A1FI - An emphasis on fossil-fuels (fossil fuel intensive).•A1B - A balanced emphasis on all energy sources (balanced).• - mp as s on non- oss energy sources pre om nant y non oss ue .

A2•A world of independently operating, self-reliant nations.•Continuously increasing population.

.•Slower and more fragmented technological changes and improvements to per capita income.

B1•Rapid economic growth as in A1, but with rapid changes towards a service and informationeconom .•Population rising to 9 billion in 2050 and then declining as in A1.•Reductions in material intensity and the introduction of clean and resource efficient technologies.•An emphasis on global solutions to economic, social and environmental stability.

B2•Continuously increasing population, but at a slower rate than in A2.•Emphasis on local rather than global solutions to economic, social and environmental stability.

•Intermediate levels of economic development.•Less rapid and more fragmented technological change than in B1 and A1.

SRES (Special Report on Emissions Scenarios)

Climate Change Projection Methodology shall be

emons ra e

by a completed study on the

p e e e e es u es

Peninsular Malaysia

–at 9km spatial grid resolution and 1hr time intervals.

In 2001 t e on y pu ic y avai a e mu ti-rea ization g o a c imatechange AOGCM simulation data (3 realizations) was from

Canadian Climate Center.

Due to its well-documented validation with the historical observations,

due to its use of the most realistic climate change scenario (IS92a),as o , ,

in its climate change simulation studies

CGCM1 (Canadian Global Climate Model 1) climate change simulationresults were selected

CGCM1-modeled mean DJF surface air temperature. b. Difference between modeled DJF surface airtemperature and NCAR DJF climatology c. As in a but for JJA. d. as in b but for JJA. Contour interval is5 ° C. Hatching indicates positive differences greater than 5 ° C, whereas shading indicates negativedifferences less than ) 5 ° C

a. CGCM1 modeled mean DJF precipitation. b. Observed DJF precipitation climatology (Xieand Arkin 1996, 1997). c. As in a. but for JJA.. d. as in b but for JJA.. Contour interval is 2mm/ d (from Flato et al.2000)

Simulations of CGCM1 during the period 1850 – 2100 thatwere used for the Peninsular Malaysia climate change study:

They consist of a control simulation (in order to compare the modelperformance against historical observations), and the ensemble averageof three independent simulations with the same greenhouse gas andaerosol changes.

These simulations spanned from the “preindustrial” 1850 conditions

to the end of twenty-first century.

A representation of the historical change in greenhouse gas (GHG) andaerosol (A) forcing from 1850 to the present (1993) was specified

in terms of equiva ent CO 2 concentration an aeroso oa ing c anges.

The ro ected forcin chan e from the resent 1993 to 2100essentially follows IPCC IS92a scenario.

Global-scale AOGCM Climate Change Projections areunreliable at regional (country) and watershed scales

• AOGCM spatial grid resolutions are too coarse for theescr pt on o t e ne eatures o oca c mate spat a

grid resolution is on the order of 410 km over PeninsularMala sia)

• At Regional and watershed scales– more refined topographic & land surface characteristics have

profound impact on regional climate

Malaysia during 2049. Letters indicate the locations of the CGCM1 grids.

As such, at Regional and Watershed scalesit is necessary to refine the coarse-grid information from

by downscaling such information to a

a muc ner gr networ over t e mo e e reg on .

Downscaling AOGCM Climate Change Simulation Resultso eg ona ca e

• Statistical Downscaling– Statistical relations, developed among regional climate

var a es an arge sca e a mosp er c s a e var a es, are enused to downscale AOGCM results to regional scale;

• Numerical or Nested Grid Downscaling ( Dynamical approach ):– AOGCM results are used as initial and boundary conditions

which are nested into AOGCMs.

Reliability Problems with Statistical Downscaling

• arge gr reso u on n s a s ca ownsca ng s oo coarse o accoun or eeffect of steep topography on local climate;

• Runoff estimates are fundamentally determined by precipitation projections;• Future projections of precipitation by AOGCMs, when downscaled to regional

,the neglect of the fundamental influence of topography on regional climate;

•climate are not accounted for.

Numerical Downscalingof AOGCM Climate Change Simulation Results by

Regional Hydroclimate Models (RegHCM)(Dynamical Approach)

• AOGCM climate simulation results form initial andboundary conditions for regional hydroclimate model

• Regional model has substantially more refinedtopographic and land surface characteristics data (grid

resolution <10km)

Consequently,

A Regional Hydroclimate Model of Peninsular Malaysia (RegHCM-PM)

was developed in order to downscale dynamically

the climate change simulations of the Canadian GCM (CGCM1)

at coarse spatial resolution (~410km)

to t e region of Peninsu ar Ma aysia at fine spatia reso ution (9 m)

the effect of the topography and land surface conditions

on its local hydroclimate .

=the atmos heric com onent of

MM5 (Fifth Generation Mesoscale Model)

or ofWRF (Weather Research and Forecasting Model)

the land surface process module of IRSHAM (Integrated.

F θ σ 1 ( u * , θ * , θ 1 , θ 2 ) F q σ 1 ( u * , θ * , q 1 , q 2 )

θ = θ 1 q = q 1F i r s t A t m o s p h e r ic L a y e r( σ = 0 . 9 9 5 , z ≈ 5 0 m )

θ ( u * , θ * , T s u r f ; z ) q ( E ; u * ; L ( u * , θ * ( T s u r f ) ; z )

C o n s t a n t f l u x

R o u g h n e s sR N H s ( u * , θ * ) E ( w s , u * , θ * , q )

θ s o ( u * , θ * , T s u r f ) q s o ( w s )

S o il s u r f a c e T s u r f ( R N , E , H s , G ) w s ( E , P )

P u r e D i f f u s i o n Z o n e

S o i l w a t e rc o n t e n t w

S o i l V a d o s e Z o n e

s u r , s o

A schematic description of regional hydroclimate model RegHCMas the model for the interactive evolution of atmospheric processes aloft,

atmospheric planetary boundary layer, and land surface processes(From Kavvas et al. (1998), Journal of Hydrological Sciences, IAHS)

MM5 Atmos heric Model

NCAR (US National Center for AtmosphericResearch and Penn State Universit

• Nonhydrostatic 3-D dynamic simulation of

• Downscaling and upscaling capabilities;

• Many modeling options for various atmosphericprocesses .

MM5 has a wide ran e of h sical rocess routinessuch as those

handling advection, diffusion, radiation, the boundary layer,

surface slab model, cumulus parameterization and moisture .

MM5 can be downscaled even to 0.5km spatial grid resolution,

which makes it very desirable for downscaling climate study results

even to t e sca e o sma waters e s, an e a e to capturethe impact of steep topography of the modeled region

on the local climatic conditions.

In the horizontal directions MM5 has two-way nesting capability.

Each nested domain takes information from its parent domain at every time-step, and

runs three time steps for each parent step before feeding back informationto the parent domain on the coincident interior points.

e ee ac s ngu s es wo-way nes ng rom one-way nes ng,

and allows nests to affect the coarse mesh solution,

usually leading to better behavior at outflow boundaries.

Weather Research and Forecasting Model (WRF)

• WRF is a supported community regional climate model which is a free andshared resource with distributed development and centralized support.

• Its development is led by US NCAR, NOAA/GSD and NOAA/NCEP/EMCwith partnerships at AFWA, FAA, NRL, and collaborations with universitiesand other government agencies in the US and overseas.

• WRF has two different dynamical cores: The Advanced Research WRF(ARW) and Nonhydrostatic Mesoscale Model (NMM).

• Dynamical cores include mostly advection, pressure-gradients, Coriolis,buoyancy, filters, diffusion, and time stepping..

• ARW is supported by NCAR/MMM whereas NMM development is centeredat NCEP/EMC with the support of NCAR/DTC

The hydrologic and atmospheric processes that take place at the interfacebetween the earth surface and atmosphere, such as infiltration and

,“land surface processes”.

These land surface processes have scales much smaller than thehorizontal resolution of mesoscale atmospheric models.

For exam le, the rid size of the Re HCM for Peninsular Mala sia is9 km for the inner domain, while the actual hydrologic processes take

place at a scale of much less than 1 km (~10s of meters).

It is impossible to resolve the land surface processesat their actual spatial resolution

over t e computationa gri s of a regiona y roc imate mo e .Numerical schemes that model the areal-average behavior of

mesoscale atmospheric model are called“land surface parameterization”.

MM5/WRF only have one-way interaction in the vertical direction.Their land surface model component options are all based on

- -fundamental role land heterogeneity plays in the modeled land surfaceprocesses as they vary with the scale of the model horizontal gridresolution.

What is necessary is a model of land surface fluxes and landhydrologic processes that will scale with the horizontal grid

Our Regiona Hy roc imate Mo e (RegHCM) is ase upon sucscalable land surface hydrologic conservation equations (Kavvas etal. 1998, Journal of H drolo ical Sciences

and is fully coupled to MM5 in a two-way interaction in the verticaldirection .

Regional Land Surface Module of RegHCMfrom land surface com onent of IRSHAM Kavvas et al. 1998

Areally averagedsoil water flow and

Rain Sun Rain Rain SunSun

so eat ow

equationsComputes

Interceptionby vegetation

Direct Evaporationof Intercepted Water Transpiration of

Root Zone Water Interceptionby vegetation

Direct Evaporationof Intercepted Water

Direct Evaporationof Intercepted Water Transpiration of

Root Zone Water Transpiration of

Root Zone Water

interception,evapotranspiration,

infiltration, exfiltration,

Through Fall

Rainfall excess

Bare Soil Evaporation

Through Fall

Rainfall excess

Bare Soil Evaporation

soil water contentprofile, soil waterstorage, direct runoff

Infiltration Root Zone

Unsaturated Zone

Groundwater Rechar e

Infiltration

Infiltration Root Zone Root Zone

Unsaturated Zone

Groundwater Rechar e

Infiltration,temperature asareally-averaged

Groundwater TableGroundwater Table model grid area

Necessary data for RegHCM

Land Use/Land Cover: Global Land CoverCharacterization (GLCC) by USGS

Aerodynamic Roughness Length and Surface Albedo:oo up a e w c re a es e aero ynam c roug ness

length and albedo with GLCC for winter and summer

respectively. (developed by NCAR)Soil Data: Digital Soil Map of the World (DSMW) byFAO

Sea Surface Temperature: ICOADS or AOGCM

Peninsular Malaysia and FAO soil survey dataset separately.

Saturated hydraulic conductivity estimated using both the soilsurvey dataset of Peninsular Malaysia and FAO soil survey dataset together.

Mean saturated hydraulic conductivity [unit: cm/hr] overRegHCM-PM’s inner domain grids.

Standard deviation of saturated hydraulic conductivity [unit: cm/hr] over

RegHCM-PM’s inner domain grids.

Aerodynamic roughness in winter over Peninsular Malaysia

Albedo in winter over Peninsular Malaysia

Stream channel routing model

The land hydrology component of RegHCM-PM, described above,

computes

the flow from neighboring lands

to the stream network of a watershed.

In order to compute the flow at the outlet of a watershed,

s necessary o accoun or e s orage an rans a on processes w n

the stream network of a watershed.

For this purpose Muskingum Flow Routing algorithm

P ET P ET

I is river inflow, O is outflow from the watershed, ET is evapotranspiration,s prec p a on, an s e c anne s orage w n e wa ers e .

The river inflow I , evapotranspiration ET , and precipitation P are computed bythe land hydrology and atmospheric components of RegHCM-PM.

The Muskingum flow routing model routes the streamflows within the stream

channel network toward watershed outlet.

InflowInflow

Release

Outflow

Generator

Irrigation

AdditionalIrrigation

water Outflow

Generato r

Irrigation

AdditionalIrrigation

water wa er

Irrigated land

Schematic description of the operation of a reservoir

involves computation of crop potential evapotranspiration (PET), andthen balancing this PET by rain that falls over the region and

y so wa er s orage.

But the standard method for computing soil water storage is by theThornthwaite-Mather water balance method of 1950s which does not

accoun or e curren y roc ma e mo e ng ec no ogy a prov esmuch more reliable soil water balances and estimates.

Estimation of Crop Irrigation demand :

After the actual evapotranspiration rate is calculated byRegional Hydroclimate Model RegHCM

Accounting forCro t e cro season state of soil water stora e and state of

atmospheric boundary layer

Crop Irrigation Demand = PET – Actual ET

Global ScaleAtmospheric TopographyBoundary

Atmospheric&

OceanData

Topography&

LandcoverUSGS

BoundaryConditions

Initial

MM5ModelOuter

CGCM, NCEP Soil (FAO)Fieldsoma n

BoundaryConditions

InitialFields

Model2ndDomain Model

BoundaryConditions

Fields InnerDomain

Watershed ScaleHydro-climate

Out ut

IRSHAMModel

Domain

,Landcover

(NAHRIM)

NCEP: stands for United States National Center for Environmental Prediction;USGS: United States Geological Survey;

FAO: Food and Agriculture Organization of the United Nations;

Nested Domains and Configuration of RegHCM-PM

g gThe RegHCM-PM was nested into the First Generation Coupled General Circulation

figure. The CGCM1 provides the initial fields and boundary conditions to theRegHCM-PM, and then the CGCM1 simulation results are downscaled to the regionof Peninsular Malaysia through several nesting procedures.

The grid layout for the 1st domain of the RegHCM-PM under Mercatorprojection. GTOPO30 DEM of the region is overlaid on the outer domain grids.

este gr s o t e nner 3 r an t e outer 1 s oma ns o eg - un erMercator projection. The boundaries of the Peninsular Malaysia and nearby islands

are overlaid on the grids.

VALIDATION OF REGHCM-PM OVER SUBREGIONS OF PENINSULAR MALAYSIA

Selected subregions in Peninsular Malaysia for hydroclimate comparisons

between the RegHCM-PM modeled values and observations

n ( m m

)Obs Sim

5. Kelantan

c i p i t a t i o

0Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92

Month-Year

1000 m

)Obs Sim

6. Pahang

Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92

P r e c i p i t a t i o n

ont - ear

c i p i t a t i o n ( m m

)Obs Sim

7. Perak

Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92

Month-Year

1000 )

Obs Sim8. Kedah

400600

- - - - - - - - - -

P r e c i p i t a t i o n

Month-Year

Observed and simulated monthly precipitation over the subregions in Malaysian Peninsuladuring the validation period

202530

a t u r e

( o C )

Obs Sim2. Klang

a t u r e

( o C )

O bs S im1. West Coast

t e m p e r a

0Jan-91 Jan- 92 Jan-93

Month-Year

30 o C

Obs S im3. Selangor

O bs S im4. Terengganu

0Jan-91 Jan-92 Jan-93

M onth-Year

Jan-91 Jan-92 Jan-93

A i r t e

m p e r a

t u r e (

A i r t e m p e r a

t u r e

o n - e ar ont - ear

p e r a

t u r e

( o C )

Obs S im5. Kelantan

15202530

p e r a

t u r e

( o C )

Obs Sim6. Pahang

Month-Year

A i r t e

Ja n-9 1 Ja n-9 2 Ja n-9 3

Month-Year

A i r t e

30 )Obs Sim. Perak

Obs S im8. Kedah

101520

A i r t e m p e r a t u

r e ( o

1015202530

A i r t e m p e r a t u

r e ( o C

Month-Year Month-Year

Observed and simulated monthly mean air temperature over the subregions in Malaysian Peninsuladuring the validation period.

Locations of the selected stream gauging stations and watersheds.

Sim Obs

F l o w ( M

J a n -

n - 9 1

J a n -

Observed monthly mean flow and simulated monthly mean flowat Jam. Guillemard, Kelantan (region no. 5)

Sim ObsMissing data

- 8 - 8 - 8 - 8 - 8 - 8 - 9 - 9 - 9 - 9

F l o w

J a J a J a J a J a J a J a J a J a J a

Observed monthly mean flow and simulated monthly mean flowat Jam. Iskandar, Perak

OVER PENINSULAR MALAYSIA

West Coast Klang Selangor Terengganu

Kelantan Pahang Perak KedahJohor Southern Peninsula N.East Coast

r e ( o C )

t e m p e r a

4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 1 2 2 4 5 6 7 8 9 0

Historical Period Simulated Future Period

West Coarst Klang Selangor TerengganuKelantan Pahang Perack Kedah

Kelantan Pahang Perack Kedah

45005000

o or out ern en nsu a . ast oast

30003500

i p i t a t i o

15002000

n n u a

l P r e c

SimulatedSimulated Future Period

stor ca er o

1 1 1 1 1 1 1 1 1 1

West Coast Klang Selangor TerengganuKelantan Pahang Perak KedahJohor Southern Peninsula N East Coast

Johor Southern Peninsula N.East Coast

18002000

t i o n

( m m )

8001000

t r a n s p

200400

A n n u a

l e v Simulated

Historical Period Simulated Future Period

1 9 8 4

1 9 8 5

1 9 8 6

1 9 8 7

1 9 8 8

1 9 8 9

1 9 9 0

1 9 9 1

1 9 9 2

1 9 9 3

2 0 4 1

2 0 4 2

2 0 4 3

2 0 4 4

2 0 4 5

2 0 4 6

2 0 4 7

2 0 4 8

2 0 4 9

2 0 5 0

West Coast Klang Selangor Terengganu

Kelantan Pahan Perak Kedah

180Johor Southern Peninsula N.East Coast

SimulatedHistorical Period

Simulated Future Period

100120

o r a g e

o i l w a

t e r s

1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Locations of the selected stream gauging stations and watersheds.

SimulatedHi i l P i d

Simulated Future Period

Historical Period

f l o w

( c m s

t h l y m e a

4 5 6 7 8 9 0 1 2 3 5 6 7 8 9 0 1 2 3 4 5 1 2 2 4 5 6 7 8 9 0 1 9 8

Simulated monthly river flows during the historical (1984-1993) and future (2025-2034 and 2041-2050) periods at Jambatan. Guillemard, Kelantan (region no. 5)

ummary o s mu a e ows ur ng e s or ca an u ure per o s a eselected watersheds of Peninsular Malaysia

Maximum monthlyflow cms

Mean monthly flowcms

Minimum monthlyflow cms

Historical Future Historical Future Historical FutureKlang 31.2 45.8 14.4 13.3 2.6 3.5

Selangor 107.9 108.5 40.7 37.5 7.1 0.5

Teren anu 398.4 569.5 93.4 98.3 13.1 10.8

re ion name

Kelantan 1535.1 1950.7 535.9 601.7 158.4 125.8Pahang 1697.4 2176.6 669.6 718.1 156.3 122.7Perak 523.7 578.2 286.4 299.7 183.6 139.2

. . . . . .Johor 82.7 94.0 32.7 31.8 9.8 6.8

now available, clearly show strong trendsin climate variablesin the future.

How to make inferences on the future water balancetrends over a selected eo ra hical re ion?

With this ensemble averaging approach, one can then filter out

the natural variability of the hydrologic variablein order to discern the real signal that should describe

the impact of the climate change on the hydrologic variable.

Wit in t e framewor of t is ensem e of rea izations

one can also compute the strength of the real signal

at two time points to the estimated ensemble standard deviation of

o a str ut on o t e s mu ate sur ace temperature yGCM control run at 0:00 January 1, 1997

o a str ut on o t e s mu ate sur ace temperature yGCM (SRES A1B) run 1 at 0:00 January 1, 2050

o a str ut on o t e s mu ate sur ace temperature y

An ensemble of 14 90-year realizations from these GCMs’ global climate

simulations for the 2010-2100 future eriod ma be downscaled to a

region of interest by means of RegHCM at 9km grid resolution and

hourly intervals.

The control climate runs of these three GCMs were also obtained, and

can e ownsca e o e reg on o n eres y means o eg or a

historical period for comparison with the future climate projections in

on the future water resources of the region

In this manner it is possible to construct an ensemble of 14

climatic variable over the region of interest at 9km grid resolution

un e year .

Based upon this ensemble it is then possible to construct

behavior of the specified hydrologic (eg. watershed runoff

throughout their transient, trending evolution during

the future period until the year 2100.

The simulated ensemble of future climatic and hydrologic conditions

can then be compared against the counterpart historical simulationsof RegHCM over the specified region, based upon its downscalingof 3 control simulations over the re ion from ECHAM5 MRI- CGCM2.3.2, and GFDL-CM2.1 GCMs during a 30-yearcalibration/validation period.

These comparisons can be performed by graphs, by statistics(monthly means and standard deviations of streamflows, maximuman m n mum stream ows ur ng t e stor ca an uture t meperiods), and by statistical tests (tests for the equality of the meansand standard deviations of historical and future streamflows,confidences bands for ensemble averages).

:With the available ensemble of climate simulations from

various AOGCMs,

With today’s regional hydroclimate modeling technology,

t s poss e to ma e sc ent ca y soun n erences

on the impact of future climate change

on the state of the water resources

o a es gna e geograp ca reg on.

Com arisons of the monthl reci itation that were downscaled b

RegHCM-SS from the coarse-resolution ECHAM5 historical control rundata, against the Willmott data at 12 selected locations

200300400500600700800900

i p i t a t i o n

)Obs MM5Willmott(65)

200300400500600700800

i p i t a t i o n

) Obs MM5Willmott(110)

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

600700

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

700800900

0100200300400

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

P r e c

i p i t a t i o n

0100200300400500600

a n - 9

l - 9 3

a n - 9

l - 9 4

a n - 9

l - 9 5

a n - 9

l - 9 6

a n - 9

l - 9 7

a n - 9

l - 9 8

a n - 9

l - 9 9

P r e c

i p i t a t i o n (

400600800

10001200

i p i t a t i o n

J J J J J J J

200300400500600700800900

i p i t a t i o n

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

Com arisons of the monthl reci itation that were downscaled b

RegHCM-SS from the coarse-resolution NCAR reanalysis data, againstthe Willmott data at 12 selected locations

P r e c

i p i t a t i o n

100200300400500600700

P r e c

i p i t a t i o n

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

n ( m m

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

600700800900

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

P r e c

i p i t a t i o

0100200300400500

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

P r e c

i p i t a t i o

P r e c

i p i t a t i o n ( m

m ) Obs MM5Willmott(23)

200300400500600700800900

P r e c

i p i t a t i o n

m ) Obs MM5Willmott(59)

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

J a n - 9

l - 9 3

J a n - 9

l - 9 4

J a n - 9

l - 9 5

J a n - 9

l - 9 6

J a n - 9

l - 9 7

J a n - 9

l - 9 8

J a n - 9

l - 9 9

Climate is an evolving, transient process.

As such, the best way to make inferences on

h f i i i

the future extreme precipitation eventsunder future changed climate conditions is

to dow nscale many sim ulated climate realizations

from different GCMsfor a specified

atmospheric greenhouse gases and sulfate aerosol emissions scenario

Regional Hydroclimate Model (RegHCM )

over the specified watershedfrom which one can then select future extreme reci itation events.

One can then compare the historical MP estimate

aga nsthe future precipitation events

a are ownsca e rom e en e arge sca e ex reme

atmospheric conditionso e sca e o e spec e wa ers e or

by means of the RegHCM over the specified watershed

in order to verify that the

stor ca est mate

is indeed the maximum precipitation event

t at can occur over t e spec e waters e .

EH5-A1B Surface Air

Y 2016 R li i

Year 2016 Realizations

3 Realizations of Surface air tem erature simulations retrieved from

an EH5 GCM Grid (1.85x1.85degree) over California

3 Realizations of 6 hourly precipitation simulation data retrieved from

an EH5 GCM Grid (1.85x1.85 degree) over California

Precipitation during a potential flooding event

3 Realizations of 6 hourly precipitation simulation data obtained from

an EH5 GCM Grid (1.85x1.85degree) over California

Based upon this ensemble it will then be possible

to construct

an upper limit realization for the future precipitation series

There are also a limited number of GCM simulations

extending to the year 2300

that can be used to examine further the future extreme

precipitation/flood eventsb downscalin the GCM simulations to the s ecified watershed

by means of the combined RegHCM-WEHY atmospheric-hydrologic model

DEM of Peninsular Malaysia at a grid resolution of 3 arc-second based onthe Shuttle Radar Topography Mission. (SRTM-3 DEM, approximate horizontal grid

resolution of 90 meters)

Regional Hydroclimate Model (RegHCM) of PeninsularMalaysia

was run rst w t ts n t a an oun ary con t ons prov e rom

CGCM1 global historical atmospheric simulation data

at 1st domain at 81km grid resolution with 23 x 24 grids ,

cover ng e w o e en nsu ar a ays a reg on

and the surrounding areas,

and to be called “1 st domain”,

The longitudes of the outer domain span from 91 o E to 114 o E

and its latitudes span from 5 o S to 15 o N.

The 2 nd domain with 34 x 37 grids and a grid resolution of 27 km,

which covers a region of 918 km x 999 km,

is nested within the center of the 1st

domain.

The 3rd domain is the inner domain of the updated RegHCM-PM , which

encompasses the entire Peninsular Malaysiaan covers a par o a an n e nor , ngapore n e sou , an a

part of Indonesia in the southeast.The inner domain is nested within the center of the 2 nd domain.

Below figure shows the whole inner domain grid layout relative to thePeninsular Malaysia under Mercator projection.

a region of 576 km x 684 km. In the figure, the boundary of PeninsularMalaysia is shown with black lines, the grids of the inner domain are shownas small blue squares, and the grids of the outer domains are indicated by

the large blue squares.

Twice-daily atmosphere-ocean data from the CGCM1 climate change

simulations for the desired 1984-1993, 2025-2034 and 2041-2050

study periods are available from the Canadian Center for Climate

Anal sis and Modelin CCCma onl for the avera e of the

ensemble of three realizations.

The atmospheric data are provided at the 10 vertical levels with a 12

hour time interval, and the surface data are given with a daily time

interval. Three time periods of the GHG+A1 IPCC IS92a Scenario

Run, 1984-1993 for historical conditions, and 2025-2034, 2041-2050

for future global climate change conditions, were used in this study.

These data were used as initial and boundary conditions for MM5

(the atmospheric component of RegHCM-PM) simulations of the

regional climate conditions over Peninsular Malaysia.

30( c m

25 f l o w

( Klang

i c m o n

t h l y

0Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

P e r i o

Periodic means of simulated monthly flows during the historical (1984-1993) and future

(2025-2034 and 2041-2050) periods, and the 95% confidence band aroundthe future flows at Jam. Sulaiman, Klang

80 ( c m s

405060

n t h l y f l o w

e r i o

d i c m

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMonth

Periodic means of simulated monthly flows during the historical (1984-1993) and future(2025-2034 and 2041-2050) periods and the 95% confidence band aroundthe future flows at Rantau Panjang, Selangor

250 w ( c

Terengganu

t h l y f l

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec P e r i o

Periodic means of simulated monthly flows during the historical (1984-1993) and future(2025-2034 and 2041-2050) periods and the 95% confidence band around

t h l y

f l o w

P e r i o

d i c m

Periodic means of simulated monthly flows during the historical (1984-1993) and future(2025-2034 and 2041-2050) periods and the 95% confidence band aroundthe future flows at Temerloh Pahan

500 m s

t h l y f l o w

( cPerak

r i o d i c m o n

Periodic means of simulated monthly flows during the historical (1984-1993) andfuture (2025-2034 and 2041-2050) periods and the 95% confidence band around thefuture flows at Jambatan. Iskandar Perak

250 c m s

n t h l y f l o w

e r i o

d i c m

Periodic means of simulated monthly flows during the historical (1984-1993) and future(2025-2034 and 2041-2050) periods and the 95% confidence band around

the future flows at Jambatan. Syed Omar, Muda, Kedah

70 m s )

60 ( cJohor

304050

o n t h l y f l o

P e r i o

Periodic means of simulated monthly flows during the historical (1984-1993) and future

(2025-2034 and 2041-2050) periods and the 95% confidence band aroundthe future flows at Rantau Panjang, Johor

Land cover classification of Peninsular Malaysia. Nested grids of theinner and the outer domains of RegHCM-PM are shown in the background.

o a str ut on o t e s mu ate sur ace temperature y -CGCM2.3.2 GCM (SRES A1B) at 0:00 January 1, 2050

0 60.8

0.6 t i o

0.20.4

l t o n o

i s e r

-1-0.8-0.6-0.4

S i g n

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Signal to noise ratio based on simulated flow data in1984-1993, 2025-2034, and 2041-2050 in Kelantan

Monthly Leaf Area Index [x10] for RegHCM-PM’s inner domain grid areas

Monthly green fraction [%] for RegHCM-PM’s inner domain grid areas

As such, it is necessary to developa regional hydrologic-atmospheric model of

the studied region (eg. Peninsular Malaysia)to be called as “ Regional Hydroclimate Model of (modeled region)”

atmospheric dynamics and hydrologic dynamics)

the coarse- rid-resolution lobal climate chan e simulation datafrom the specified AOGCM (eg. ~ 410km grid-resolution

Canadian CGCM1 current and future climate simulation data forPM Climate Change Study)

tothe studied region (eg. Peninsular Malaysia)

at fine spatial grid resolution (<10km grid resolution).

As such, the best way to make statistical inferences about climate changeis to obtain several simulated climatic realizations from different

aerosol emissions scenarioso that one can form an ensemble of several members at any given

y gspecified time for a specified hydrologic (eg. watershed runoff) or

climatic (eg. rainfall) variable.

en a a ns an o me, one can ma e n erences ase upon eensemble average value of the variable of interest, with the ensemblestandard deviation providing a measure of the uncertainty. Ensemble

averages of the same variable, estimated at different time points wouldthen provide a measure of the change in the variable of interest.

It is also possible to develop a confidence band around the ensembleaverage n or er o ma e n erences concern ng e s gn cance o

deficits or of the severity of hydrologic extremes, as function of evolving

time toward the future.

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