climate change adaptation planning with disaster risk ... · icss asia, hanoi 2-4 march 2011...

07.03.2011 Seite 1ICSS ASIA, Hanoi 2-4 March 2011

Climate Change Adaptation Planning with Disaster Risk

Reduction Approach : Indonesian Experiences

Tri Wahyu [email protected]

Center for Climate Change

Bandung Institute of Technology


Contribution of materials:

Djoko S.A. Suroso (Policy &

Planning)

Hamzah Latief (Coastal

Vulnerability)

Ibnu Sofian (Ocean Dynamics)

Oman Abdurrahman

(Hydrogeology)

Budhi Setiawan (Civil

Hydrologist)

ACKNOWLEDGEMENT

Financial support :

Deutsche Gesellschaft für

Internazionale Zussamenarbeit

(GIZ; formerly GTZ) in

Collaboration with : (i)The

Indonesian Ministry of

Environment (KLH), (ii)Agency

for National Development

Planning (BAPPENAS),

(iii)Australian-AID


DEVELOPMENT

Environmental impacts

Environmental change

Uncontrollable

environmental

degradation

Human

activities

Environmental Impact Assessment

1st principle : avoid the unmanageable

NATURAL

ENVIRONMENT Natural/human induced disasters

Controllable

/desireable

impacts (+)

(-)Natural resources

Natural

hazards

Sustainable Development : A Simple View

Growth of

prosperity

Disaster Risk Assessment

2nd principle : manage the unavoidable

Human

induced

hazards

?

?


Mainstreaming of CC Issue in Development

Long-term plan

Mid-term plan Short-term plan

The interlinkage of Climate Change Roadmap and

Development Planning in Indonesia


Problems in CC Adaptation Planning

Mal-adaptation

Over-adaptation

Under-adaptation

Potential failure


Adaptation Planning with DRR Framework

Understand the

climatic hazard Assess Risks Reduce Risks

Transfer Risks

uncertainty

past proxy data

• Hazard Assessments

• Vulnerability Assessments

• Risk Maps

• Potential Impact Assess.

Macro-scale :

• National scale

• Policy & Laws

• Long-term planning

Meso-scale :

• Province & Municipality

• Policy

• Mid-term Planning

Micro-scale :

• Municipality

• Spatial planning

• Adaptation action

• Reduce Hazard Level

• Reduce Vulnerability Level

• Structural

• Technological

• Socio-cultural

• etc. measures

• Financial instruments

present obs. data

future climate

model

• To save human lives

• To save investments

Climate scientists Climate scientists, engineers,

planner, economic & policy

analysts

Decision makers

• Reduce economic loss

• Accelerate recovery


Risk Assessment Approach

Climate stimuli

• Temperature

• Rainfall

• Sea level

Surface

condition :

• topography

• land cover

• etc

Projected

changes in :

• mean

• variability

• extremes

CC Hazards (sectors)

• Water resources

• availability ()

• flood & drought ()

• Agricultural

• production ()

planting failure

harvest failure

lower productivity

• Health

• incidence rate ()

DBD

Malaria

Diarrhea

• Coastal

• inundated area ()

SLR

Extreme eventsH = F(f,M,p)

Physical

• Houses

• Cultivated area

• etc

Vulnerability

Components

• (E)xposure

• (S)ensitivity

• (A)daptive

(C)apacity

(R) isk = H×V

Elements of Built Environment

Social

• Population density

• Vulnerable group

• etc

Economic

• # Assets

• GDP

• etc

AC

SEV

Additional modeling

IPCC

AR4

Note : pseudo mathematics!


Case 1 : Macro-scale (i)

Projection Model : IPCC AR-4 GCM ( NCCSM,

GFDL 2.0&2.1, MIMR, HADCM3, MPEH5,

MRIGCM2) Ouput

Downscaling : No

Model Validation : GPCC Rainfall Data

Model Correction :

Selective Ensemble, best 4 models based

on correlation of climatological mean annual

pattern

Correlation map GFDL2.0 vs GPCC Selective ensemble

Effective working period :

3 months


Case 1 : Macro-scale (ii)

Projected changes of rainfall and temperature over Java Island

(area averaged) relative to base line 19961-1990

2020-2049 2070-2099


Case 1 : Macro-scale (iii)

Water sector ( drought )

Hazard Analysis :

Water Balance Model

Coastal ( inundation )

Hazard Analysis :

SLR + DEM (SRTM)

2020-2050


Case 2 : Meso-scale (i)

Projection Model : IPCC AR-4 (ECHAM5,

GFDL2.0, GFDL2.1, MRI ) Ouput

Downscaling : Statistical (scaling)

Model Validation : Local Rainfall Data

Model Correction :

Ensemble best fit to climatological annual

pattern

2001 2002 2003 2004 2005 2006 2007 2008

24.5

25

25.5

26

26.5

27

27.5

28

28.5

29

Time

Te

mp

era

ture

( C

)

Obs. (2001-2007)

SRES B1

SRES A1B

SRES A2

2001 2002 2003 2004 2005 2006 2007 20080

50

100

150

200

250

300

350

400

450

500

Time

Ra

infa

ll (

mm

)

Obs. (2001-2007)

SRES A2

(a)


Case 2 : Meso-scale (ii)

Water sector ( drought )

Hazard Analysis :

Water Balance Model

Vulnerability Analysis :

Seamless (grid) :

population density, land

use, water demands,

slope, irrigation

infrastructure, etc.

Risk Level :

increase of probability of

surface water deficit

Coastal ( inundation )

Hazard Analysis :

SLR + DEM

Vulnerability Analysis :

Seamless (grid) :

population,

infrastructure, etc.

2020-2050


Case 3 : Micro-scale (ongoing)

Projection Model : IPCC AR-4 GCM ( NCCSM,

GFDL 2.0&2.1, MIMR, HADCM3, MPEH5,

MRIGCM2) ouput

Downscaling :

Statistical (bias&phase lag correction)

Model Validation : Local rainfall data

Model Correction :

Ensemble best fit to observations


Problems of Data Availability & Quality

A

C

B

D

0 100 200 300 400 500 600 700 8000

100

200

300

400

500

600

700

800

BMKG Montlhy Rainfall 1982-2003 (mm)

GH

CN

Mo

nth

ly R

ain

fall 1

91

1-1

99

1 (

mm

)

APHRODITE information : http://www.chikyu.ac.jp/precip/


Impacts of Data on Understanding The Tropical Climate

1923 - 1942

1923 - 1932

No DJF peak?

(J. Voȗte, 1948)

Aldrian and Susanto (2003)

Bosscha Astronomical

Observatory)


Interdecadal Variability of Rainfall

Disappearing decadal scale

variablity not represented by

GCM output

Empirical Mode Decomposition (EMD) Analysis


Interdecadal Variability of Rainfall

Several studies have shown that rainfall variations are teleconnected with global climate indices (PDO, NAO, SOI) (eg. Wei et al., 2008)

Simple regression model of interdecadalcomponents of PDO and NAO with (lagged) rainfall at Tarakan


Climate Proofing Development Processes ?

Tra

nsp

ort

ation

Fo

rec

as

t L

ea

d T

ime

Warnings & Alert

Coordination

Watches

Forecasts

Threats

Assessments

Guidance

Outlook

Pro

tectio

n o

f

Life

& P

rop

ert

y

Sp

ace

Op

era

tio

n

Re

cre

atio

n

Eco

syste

m

Sta

te/L

ocal

Pla

nn

ing

En

viro

nm

ent

Flo

od

Mitig

atio

n

& N

avig

atio

n

Agricu

ltu

re

Re

se

rvo

ir

Co

ntr

ol

En

erg

y

Co

mm

erc

e

BenefitsH

yd

rop

ow

er

Fire

Weath

er

Hea

lth

Forecast

Uncertainty

Minutes

Hours

Days

1 Week

2 Week

Months

Seasons

Years

Initial Conditions

Boundary

Conditions

Current

range of

skillful

forecasts

Range of

forecasts

with

developing

skill

Adopted from Lord et al. (Symposium on 50th Anniversary of Operational Numerical Weather

Prediction, University of Maryland College Park, July 15, 2004)


Problems of “Climate Literacy”

The danger of

deterministic view

in climate forecast

or ….

this could be worse?


Intelligent Use of Climate Information


We cannot do it alone…

Prof. Shigeo Yoden (2008)


Concluding Remarks

We have attempted to apply DRR approach to climate change risk assessments in Indonesia, and contributed the results to development planning at national, provincial, and local levels

Because of demands to produce quick (project based) results, we have only used simple statistical downscaling or statistical data correction to interpolated IPCC AR-4 model outputs long-term sustainable research on downscaling of GCM outputs is needed

The main problems with statistical downscaling is availability and quality of observational data. Availability of good quality data is also crucial for our understanding of regional and local climate in the tropics.

We need a more credible and reputable “climate science authority” in this region to deal with the problems of “climate illiteracy”


Thank You

climate change adaptation planning with disaster risk ... · icss asia, hanoi 2-4 march 2011...

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