Climate Change and Extreme Weather Events

Climate Forecast Applications for Disaster Mitigation in Southeast Asia

S.H.M. Fakhruddin

fakhruddin@adpc.net

Senior Technical Specialist, Climate Risk Management Division

Discussion Topics

• Extreme Climate Events Program

• Climate Forecast Application (CFA)

for Disaster Mitigation in Philippines

and Indonesia

• Climate Forecast Application in

Bangladesh (CFAB) for Flood Risk

Management

CFA Program Overview

• Trigger: Developed in response to the severe impacts Niño 1997 98• Period:

– 1998-2003:Extreme Climate Events Program (documentation of impacts, analysis of institutional responses, identification of opportunities for forecast applications)

– 2003-2008: Climate Forecast Applications for Disaster Mitigation Program (tools development, capacity building, demonstration projects)

• Geographical coverage: Indonesia & Philippines (2003-2008) and Timor-Leste (2007-2009)

• Supported by: Office of Foreign Disaster Assistance of the United States Agency for International Development (OFDA-USAID)

• ADPC works with local, national, and international partners

CFA Demonstration SitesCFA Demonstration SitesAngatAngat

Nusa Tenggara Timur

IndramayuIndramayu

Dumangas, Iloilo

Indonesia 93% of drought years in Indonesia are linked to El Niño years

Severe drought reduced rice yields, requiring to import 5.1 million tonnes of rice

Economic crisis devalued rupiah by about 80%, pushing up price of imported rice to four times pre-crisis levels

Forest fires - out of control in Sumatra and Kalimantan

Philippines El Niño and peso depreciation collide and magnify impacts

60% depreciation of peso

Per capita GNP declined by 2.7%

Agriculture contracted by 7% and industry by 1.7%

Food and basic commodity prices increased rapidly

Socio-economic Impacts of ENSO 1997-98 in Indonesia & Philippines

El Niño impacts on rice production in the Philippines

5000

6000

7000

8000

9000

10000

11000

12000

13000

82 84 86 88 90 92 94 96 98

Year

Pal

ay P

rod

uct

ion

( x

100

0 M

T)

El Nino

El Nino

El Nino

El Nino

Year Area Impacted (Ha) % of Total1988 92,488 1.191989 38,259 0.491990 63,646 0.821991 1,060,344 13.591992 49,676 0.641993 91,407 1.171994 705,566 9.051995 33,194 0.431996 72,042 0.921997 592,488 7.601998 216,145 2.771999 52,253 0.672000 55,638 0.712001 71,368 0.912002 176964 2.27

El Niño onset yearsEl Niño onset years

Impacts El Niño on paddy production in Indonesia

Impacted = lost over 25% of yield relative to average yield

Countries needed timely, usable climate information to manage resources effectively & reduce disaster risks. However, localized & usable climate information was not available to resource managers

Reasons why climate information was not available

• Absence of participatory mechanism for identifying user forecast requirements

• Available climate information not tailored to users’ needs and requirements

• Weak forecast producer-user communication channel

• Users have difficulty understanding forecast language

• Community-level dissemination is weak

• Users have no mechanism for processing climate information once it is received

• Feedback channel from forecast user to producer is weak or nonexistent in most cases

These are the gaps the CFA program aims to address

Need/ capacity assessments

Assessment of available technology

Capacity building through partnerships

Institutionalization of end-to-end system: pilot

demonstrations, replication

Apply information to enable pro-active decision making

Monitor and evaluate applicability of information

CFA Methodology:

Six step process

Focused Intervention:

Global climate

information providers

National institutions

End-users

End-to-end climate information generation and application system

Providing climate outlook

Interpreting global climate outlook into local outlook

Translating local climate outlook into impact scenarios

Communication of response options/ feedback

Major Achievements of CFAMajor Achievements of CFA

Indonesia & Philippines

The CFA program is instrumental in establishinginstitutional mechanisms that connect hydro-meteorological

communities, risk management institutions, & societies.

Farmers in Liquiça district learning basic rainfall observation at the Climate Field School for Farmers.

BMG

IPB

Universities

Agriculture Office at District Level

Marine and Fishery

Forestry

Health

TourismAviation - Maritime

Directorate of Plant ProtectionDept. of Agriculture

Related institutions

BMG

Translation of Climate Outlook Scientific Language Operational Language

(“Below Normal”) = (Lack of water)

Provision of Climate OutlookIn “meteorological language”

Conversion of Operational Language into e.g. Crop Management Strategies

Dissemination of Information to Farmers and evaluation of Farmers

Response

Change Crop Pattern ! Change Planting Time ! Change Crop Variety !

Institutional mechanism: Indonesia

Directorate of Plant Protection

IPB

Provision of climate outlook

BMG

Translation of climate outlook into

impact outlook

Indramayu Agriculture Office

Conversion of impact outlook into crop management

strategies

Dissemination of information to farmers and evaluation of

farmers response

SUB BAGIAN TATA USAHA

Drs. Agung Wradsongk o, MP

SUBDIT ANALI SIS & MITIGAS

I DAMPAK IKLIM

Ir. Jatmiko

SUBDIT PENGELO LAAN PHT

Ir. Sarsito WGS,MM

SEKSI KELEMBAGAAN

Ir. B. IndriastutiK

SEKSI PEMASYARAKATAN

Ir. Dyah Mutiawari

SUBDIT PENGE NDALIAN OPT

Ir. Hari Utomo

SEKSI OPT SEREALIA Drs. Ruswandi

, MM

SEKSI OPT KAC- ANG 2AN DAN

-UMBI 2AN Ir. Ety Purwan

ti

KELOMPOK JABATAN FUNGSIONAL

BALAI PENGUJI M UTU PRODUK TAN

AMAN Ir. Yayah Roliyah,

M Si

SEKSI ANALISIIKLIM

Ir. Irwan Kamal

SEKSI MITIGAS I DAMPAK IKLI

M Ir. Endang Titi

P. MM

DIREKTUR PERLINDUNGAN

TANAMANIr. Ati Wasiati

SEKSI PELAYANAN

TEKNIKDrs. Oscar Rulli

SEKSI PENGELOLAAN

SAMPLEDra. Tantri Indrianti

SUB BAGAIN TATA USAHA

Suparjo

SUB DIT PENGELOLAHA

N DATA OPTIr. Fatra Widjaya,

M Si

SEKSI INFORMASI &

DOKUMENTASIIr. Yarmiati

Munaf

SEKSI MONITORING

Drs. Tigor Sagala

*Subdivision on Climate Analysis

and Mitigation

Institutional innovation at Indonesian Ministry of Agriculture

Institutional mechanism: Philippines

GLOBAL CLIMATE PREDICTION CENTERS

Sea Surface Temperature AnomaliesRainfall Anomalies

Temperature Anomalies

CAB/ PAGASA/ DOST

Analysis / Downscaling techniques / Impact assessment procedures /

Forum Discussion

Tailored Local Climate Forecasts / Advisories (I loilo)

CLIMATE PATTERNS TRENDS

Near-real-time Data

HISTORICAL DATA AND INFORMATION

Episodic Events Documents

Dumangas Agromet Station

TWG CLIMATE FORUM

Prov’l Agricultural Officer Municipal Agricultural Officers

NIA Region VI Dumangas Agromet Station Officer

Farmers Reps. Local Irrigation System Office

IMPACTS / DECISION OUTLOOKS / ADVISORIES

FARMERS

Translation of Global Climate Forecasts into Impacts / Decision Translation of Global Climate Forecasts into Impacts / Decision Outlooks Outlooks and Advisories for the Endand Advisories for the End--User Farmers of Dumangas and Tigbauan, IloiloUser Farmers of Dumangas and Tigbauan, Iloilo

8” Standard rain gauge

Tipping bucket rain gauge

Automatic Weather Station raingauge

A pool of meteorologists has been formed & trained to provide tailored climate information for risk management in program countries. Through their interactions with institutions & communities under the Program, they understand that meteorologists should not just produce information but have to relate the information to the user context.

BMG

Kel-1 : bag sel Haurgelis/ Gabuswetan/

Bangodua

Kel-2 : bag.utara Indramayu

Kel-3 : bag.utara Anjatan/Sukra

Kel-4 : Krangkeng /Karangampel

Juntinyuat/ Sliyeg/Kertasemaya/

Jatibarang/Widasari/Sindang/

Lohbener/ bag.Utara Bangodua

Kel-5 : Kandanghaur/Bongas/bag.utara

Gabuswetan/bag.timur

Anjatan/Lohsarang

Kel-6 : Cikedung /bag.sel.Gabuswetan

/bag.utara Haurgelis/ Lelea

Climate ForecastsUpdated every month:

day 2125

Forecast delivery:day 2630

Collection of daily dataFrom day 20 (M-1)

day 20

Dumangas Climate Field School

A farmer shows his

revisedcroppingcalendar

Institutional and community-level dissemination channels indemonstration sites have been strengthened. Community capacity to use climate information have been built primarily through Climate Field Schools, climate forum, & community-level workshops

Indramayu Climate Field SchoolWomen farmers participating inthe Climate Field Schoolwork with extensionworkers in understanding rainfall graphs

Farmers in Losarang subdistrict Indramayushowing rainfallgraphs

Farmers in Losarang subdistrict Indramayushowing rainfallgraphs

F A R M E R S

Farmer Groups

P1-1

P1-2P1-2 P1-2

F A R M E R S

Farmer Groups

P1-1P1-1

P1-2P1-2 P1-2P1-2P1-2 P1-2P1-2P1-2 P1-2

Stage 1: training of agricultural extension specialists (district level)

Stage 2: training of agricultural extension workers (sub-district)

Stage 3: training of heads of farmers groups

Stage 4: dialogue with farmers

Climate field school: implementation process

Instrument areaStation building

Forecast applications for disaster mitigation isnow internalized and owned by local governmentsinvolved in the program

Municipal legislation assures local government support to the operations & maintenance of Dumangas Agro-Met Station

Raingauges in Indramayu Regency

Implementation experience:Indramayu, West Java, Indonesia, Dry season 2006-2007

WMO El Niño advisory, 2006

Preparing program for supporting the action plans and socialization

20072006Translation

Communication FlowCommunication Flow

BMG

Jun

e/July

A

ug

ust

S

ept/O

ct

Octo

ber

1st1st 2nd2nd

Janu

ary

F

ebru

ary

Mar/A

pr

A

pril

Dumangas community-based flood early warningsystem

The CFA program has been instrumental in demonstrating that use of weather & climate information can save lives and produce tangible economic benefits

El Nino 2002-2003El Nino 2002-2003As reported by the Iloilo Provincial

Agriculturist, due to early dissemination of the El Niño forecast, farmers in the province were able to mitigate its adverse impacts by switching to alternative crops (e.g. rice to watermelon)

El Niño damage - 64.00 M

Production of other

crops - 732.02 M

Difference Php 688.02 M

Key coping strategy: crop Key coping strategy: crop substitution substitution

Climate Forecast Climate Forecast Application in BangladeshApplication in Bangladesh

Objectives

Project Objective 1: Flood Forecast technology tested and Forecast technology tested and transferredtransferred, and capacities developed to operationalise:

A. 1-10 days forecastsB. 20-25 days forecastsC. 3-6 months forecasts

Project Objective 2: Application of flood information through pilot projects at selected sites, showing measurable improvements.

Project Objective 3: Flash flood forecast technology developed and tested on experimentalon experimental basis for North East Bangladesh

Implementation Plan

2006 2007 2008 2009

Pilot Testing the model, validation

& capacity building

Technology Transferred to

FFWC, physical supports, capacity

building

Refinement, validation, advisory support & bridging

Pilot Testing the model, validation

& capacity building

2000-2005 Model Development and Experimental Forecasts

Supported by USAID OFDA, ADPC

Supported by USAID through CARE Bangladesh

Institutional Collaboration For Sustainable End-to-end Flood Forecasts System

BMD

Climate (rainfall and discharge) forecasting technology

(EAS)/ADPC

Agro met translation

FFWC Discharge translation

IWM

DMB, DAE

Interpretation

Communication

End users

ADPC, CARE ,CEGIS ADPC

ADPC, CARE,CEGIS ADPC

Climate forecast ADPC GCEGI

S //GECIS

G

CFAB Model Area

Bahadurabad

Hardinge Bridge

Discharge Forecast Schemes

ECMWF Operational ensemble forecast

NOAA and NASA (i.e.CMORPH and GPCP)

satellite precipitation & GTS rain gauge data

Hydrologic model parameters

Discharge data

Downscaling of forecastsStatistical correction

Hydrological Model

• Lumped• Distributed• Multi-Model

Discharge Forecasting

• Accounting for uncertainties

• Final error correction

• Generation of discharge forecast PDF

• Critical level probability forecast

(I). Initial Data Input

(II). Statistical Rendering

(III). Hydrological Modeling

(IV). Generation of Probabilistic Q

(V). Forecast Product

Brahmaputra Discharge Forecasts 20081-10 day flood forecasts using ECMWF precipitation forecasts

Brahmaputra Discharge Forecasts 20071-10 day flood forecasts using ECMWF precipitation forecasts

2007 Brahmaputra Ensemble Forecasts and

Danger Level Probabilities

7-10 day Ensemble Forecasts 7-10 day Danger Levels

7 day 8 day

9 day 10 day

7 day 8 day

9 day 10 day

Plumes and probability pies for the first Brahmaputra flood July 28-August 6, 2007

High probabilities of exceedance of the danger level by the Brahmaputra at the India-Bangladesh border

Plumes and probability pies for the second Brahmaputra flood September 8-16, 2007

For the second flooding, short-term forecasting, successful in providing high probabilities of exceedance of the danger level by the Brahmaputra

1-10 Days Forecasts at Bahadurabad 2009

1-10 Days Forecasts in the FFWC Website

http://www.ffwc.gov.bd/http://www.ffwc.gov.bd/

Forecast updates from 72 hrs to 10 days

Traditional 3 days forecasts Forecast extended to 10 days

20-25 days and Seasonal Forecasts

• 20-25 days and Seasonal Forecast still in experiment. Not shared to public

Pilot AreasPilot

Areas

Target groups Decisions Forecast lead time requirement

Farmers Early harvesting of B.Aman, delayed planting of T.Aman 10 days

Crop systems selection, area of T. Aman and subsequent crops

Seasonal

Selling cattle, goats and poultry (extreme) Seasonal

Household Storage of dry food, safe drinking water, food grains, fire wood

10 days

Collecting vegetables, banana 1 week

With draw money from micro-financing institutions 1 week

Fisherman Protecting fishing nets 1 week

Harvesting fresh water fish from small ponds 10 days

DMCs Planning evacuation routs and boats 20 – 25 days

Arrangements for women and children 20 – 25 days

Distribution of water purification tablets 1 week

Char households Storage of dry food, drinking water, deciding on temporary accommodation

1 week

Flood risk management at community level decisions and forecast lead time requirement (Eg. Rajpur Union,

Lalmunirhat district)

USER MATRIX on Disasters, Impacts and Management Plan for Crop, Livestock and Fisheries sector

Disasters Crop Stages Season/month

Impacts Time of flood forecast

Alternative management plans

Early flood T.Aman Seedling andVegetative stage

Kharif IIJun – Jul

Damage seedlingsDamage early planted T.AmanDelay plantingSoil erosion

Early June

Delayed seedling raising,Gapfilling, skipping early fertilizer application

T.Aus Harvesting

Kharif IJun – Jul

Damage to the matured crop

Early June

Advance harvest

Jute Near maturity

June-July Yield lossPoor quality

May end Early harvest

S.Vegetables

Harvesting

June-July Damage yield lossPoor quality

Mar - Apr

Pot culture (homestead)Use resistant variety

High flood T. Aman Tillering Kharif - IIJuly-Aug

Total crop damage Early June

Late varietiesDirect seedingLate planting

Late flood T. Aman Booting Kharif IIAug-Sep

Yield loss and crop damage

Early July

Use of late varietiesDirect seedingEarly winter vegetablesMustard or pulses

Flood (early, high and late)

Cattle - Jun-Sep Crisis of food and shelter. Diseases like cholera, worm infestation

Early June

Food storage, flood shelter, vaccination de-warming

Flood Nursery table fishBrood fish

- June to Aug

Inundation of fish farmsDamage to the pond embankmentsInfestation of diseasesLoss of standing crops

Apr - May

Pre-flood harvesting,Net fencing/bana,Fingerlings stocked in flood free pond, High stock density

Risk Communication of flood forecasts 2007- 2008

54

Sending SMS to Mobile

Risk Communication for Flood Forecasts 2007-2008

Mobile phone

Flag hoisting

Institutional and community responses on 2007 flood forecast: ADPC Field Team

Flood forecast issued for two

boundary locations

Incorporated into customized local model

21 Jul

22 Jul

23 Jul

Communication to project partners

24 Jul

Communication to stakeholders and local DMC members

25 Jul

Communication to Disaster Emergency Group

26 Jul

Discussion of options with local communities, CBOs, local working group members,

networks

30 Jul

2 Aug

Information to relief agencies about the extent of flooding

Local institutions prepared response and relief plans

Community in low lands reserved their food, drinking water, fodder requirements

Local Disaster Management Committee and Volunteers prepared for rescue

Aid agencies arranged logistics and begin dialogue with district administration

Low lying areas are flooded on 29th July

Relief distribution started in affected locations

Flood water exceeded danger level on 28th July

Institutional and community responses on 2008 flood forecast- Report from ADPC field Team

Flood forecast issued for two

boundary locations

Incorporated into customized local model

27 Aug 28 Aug

29 Aug

Communication to project partners

30 Aug

Communication to stakeholders and local DMC members

Communication to Disaster Emergency Group, DAE, etc

31 Aug

04 Sep 10 Sep

The flood water likely increase

Low lying areas are flooded

Flood level could further recede and come down below danger level likely

Flood water exceeded damaging level on 1 Sep

Field team visit and observe the local

situation

Community responses to flood forecasts

Community responses to flood forecasts

Economic Beneifits

• In 2008 Flood, Economic Benefits on average per household at pilot areas– Livestock's = TK. 33,000 per

household – HH assets = TK. 18,500 per

household – Agriculture = TK 12,500 per

household– Fisheries = TK. 8,800 per

households

• Experiment showed that every USD 1 invested, a return of USD 40.85 in benefits over a ten-year period may be realized (WB).

Average Amount of Saving per Household

0 5000 10000 15000 20000 25000 30000 35000

Save agriculture

Save HH assets

Save Livestock

Save Fishereis

Amount (TK.)

Improvement Due to Long Lead Forecasts System

• FFWC able to increase the lead time from 72 hrs to 10 days

• This model performs consistently well and correctly predicted 2007 and

2008 floods

• The flood forecasts provides onset of flood, duration and dates of receding

of floods

• 1-10 days long lead forecasts provides enough lead time to interpret,

translate forecast information to users through established communication

channels

• The pilot testing of this long lead forecast information at high risk location

revels tangible benefits to the at risk communities

Key Lessons Learned: 2007- 2008 Flood

• 10-days forecast system availability is crucial for live and livelihoods preparedness and local agency response.

• Response to forecasts in low lying areas related to saving lives and small household assets (dry food, drinking water, fire wood, animal fodder, barrowing credit from micro-financing institutions)

• Response to flood forecasts in high lands are mostly related to preparedness activities like reserving seedlings for double planting, protecting fisheries, early harvesting, abandoning early planting, protecting livestock and preserving fodder

• Local institutions during 2007-08 in pilot unions are well informed and prepared for floods in advance

• The probabilistic nature of 1-10 days forecasts need to be communicated to the users at all level by innovative risk communication tools

• Capacity to interpret, translate and communicate probabilistic forecast information with impact outlooks with response options at various levels are crucial

• Long Lead forecasts are one of the best tools to enhance our adaptation to climate change associated risks at present and in future

• Agency level preparedness SoPs based on probabilistic forecast needs to be developed for livelihoods risk reduction

Key Lessons Learned: 2007- 2008 Flood

Flash Flood Forecasting

• Provided experimental 3 days rainfall forecasting for the NE region of Bangladesh in2008 and 2009. The model is integrated with a horizontal resolution of 9 KM X 9KM.  The forecasted information showed good result since 2008

• Meso-scale rainfall forecast models effectively linked to the existing discharge forecast capability of the FFWC

THANK YOU