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The Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES)

S.H.M. FakhruddinTeam Leader- Hydrology

[email protected]

Ensembles Probabilistic Long Lead Flood Forecasts For Community Level Applications

Measuring ‘Real’ Impact

Monday 25th June 2012 UK CDS, Wellcome Trust, 215 Euston Road, London NW1 2BE

http://www.powerpointstyles.com/


mailto:[email protected]


Discussion Topics

About RIMES & Key Activities

End to End EWS

Case Study on Flood forecasting and Agriculture Risk Management



RIMES Member States



Purpose and objectives

Purpose: Provide early warning services for enhanced preparedness, response, and mitigation of natural hazards, according to differing needs and demands of its Member States

Objectives:

Facilitate establishment and maintenance of core regional observation and monitoring networks and ensure data availability for early warning

Provide regional tsunami watch within the framework of UNESCO Intergovernmental Oceanographic Commission (IOC)

Support National Meteorological and Hydrological services for providing localized hydro-meteorological risk information within the framework of the World Meteorological Organization (WMO)

Enhance warning response capacities at all levels (national to community) within each national early warning framework



GovernanceCouncil

Heads of NMHSs/ national scientific and technical agencies generating multi-hazard early warning information, empowered to make policy decisions, on behalf of governments, concerning regional early warning arrangements for enhanced preparedness for, response to, and mitigation of natural hazards

Chair: Government of India

Secretariat

Carries out the decisions and tasks assigned by the Council, and provides support to the Program Unit in managing the regional early warning center

Government of Maldives (Presidential Task Force led by the H.E. Vice President of Maldives) serves as Administrative Secretariat; Government of Mongolia (Ministry of Foreign Affairs) as Program Secretariat

Program Unit

Responsible for the day-to-day operation and management of the regional early warning center and the implementation of programs and activities

Co-located with the RIMES regional early warning Center



Organizational Chart

Chief ScientistTsunami Early Warning

Warning Coordination Scientist

Seismologist

Oceanographer

Telecommunications Specialist

System Analysts (2)

Decision-support Tool Development Specialist

Seconded Scientists (6)

Team LeaderEarly Warning

Tsunami Hazard and Risk Assessment Expert

Team LeaderClimate Risk Management

Earthquake Hazard and Risk Assessment Expert

Project Teams

Climate Impact Assessment Team

GIS and Survey Specialist

Secretariat

Council

Director, Program Unit

Program ManagementTsunami Watch Provision Support to Hydro-Met Services Societal Applications

Capacity Building Specialist

ChiefProgram Management

Finance Officer

Human Resource and Administration Officer

ICKM Specialist

Climate Forecast Application Team

Project Teams

Chief ScientistClimate Change

Chief ScientistSeasonal Forecasting

Chief ScientistSevere Weather

Synoptician

Hydrologist

System Analyst

Seconded Scientists (2)



Key services

1) Tsunami Watch Provision to National Tsunami Warning Centers

Seismic and sea level monitoring and data exchange Provision of earthquake alerts and regional tsunami bulletins Tsunami hazard and risk assessment tools for local coastal inundation

forecasting

2) Support to National Meteorological and Hydrological Services

Customization of climate and weather forecasting models for generation of more reliable, location-specific severe weather and short- and medium-term weather forecasts, and seasonal climate outlook, having longer lead times

Downscaling of global climate models for generating high-resolution climate change information for national and local level planning

Development of decision-support tools

Translation of products of research into operational forecast products and testing these for local level application



Key services continued

3) Capacity Building on End-to-End Early Warning

Early warning system audits

Assistance in establishing and maintaining observation and monitoring stations of regional benefit

Training of scientists (in-country and RIMES secondment program)

Development of decision-support tools

Strengthening national early warning provider and user interface

Application of tailored risk information at different time scales in decision-making

Enhancing community responses to early warning



EW System



EW System Structure

Detection Subsystem Detection Subsystem

Management Subsystem Management Subsystem

Response Subsystem Response Subsystem

Monitoring, detection, data Assessment, data analysis, prediction

Risk Assessment, interpretation, communication

Interpretation, confirmation and response



Reasons for Warning Failure

?



Gaps

Dissemination to at-risk communities

Observation/ monitoring

Warning formulation

Community response

Data analysis

Prediction Risk assessment

Emergency response plansPublic education/ awarenessMitigation programs

Potential impact assessment

Preparation of response options

Aging and insufficient observation and data communication facilities

Numerical prediction capability Skilled human resource Capacity to make use of new generation forecasts

Local level potential impact assessment not done

Language Localized, relevant

Institutional mechanism, linkages SOPs Redundant communication systems Reach to special groups

Data sharing among agencies

Public awareness Communication of forecast limitations Lack of trainers/ facilitators Resources to respond to warning

Regulatory framework for warning Stakeholders involvement and roles



A Case Study- Bangladesh



Probabilistic Flood Forecasting and Applications in Agriculture

• Research Project initiated since 2000 and completed in 2007

• GoB requested RIMES to continue to support

• RIMES provides 10 days lead time flood forecast to GoB and build capacity



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

BMD

Climate (rainfall and discharge) forecasting technology

RIMES- CFAN

Agro met translation

FFWC Discharge translation

RIMES

DMB, DAE

Interpretation

Communication

End users

RIMES, Local Partners

RIMES, Local Partners

Flood forecast RIMES



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



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



2007 Flood- 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



2012- Brahmaputra Ensemble Forecasts



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

Model able to meet three fundamental information needs of communities at risk



Distribution of H combined with DEM --> probabilities of flood classes

DEM Distribution of H values



Vulnerability & Flood Risk Assessment

• Development of flood risk map which will include: – low probability – medium probability – high probability

Social Map

Flood Vulnerable area Map

Flood Risk Map



Flood Risk Map



Decision Support System

Community Outcomes 1. Delayed seedling raising, gapfilling, skipping early fertilizer application2. Advance harvest of paddy ( 70-80 % mature)3. Early harvest of jute for rotten in water4. Pot culture (homestead), Use resistant variety5. Food storage, flood shelter, vaccination de-warming

High flood J F M A M J J A S O N D

T.Aman 1 1 3

T.Aus 2 2 2 3

Jute 3

S.Vegetables 4 4 4

Cattle 5 5 5 5 5 5 5 5 5 5 5 5

…



High flood J F M A M J J A S O N D

T.Aman 1 1 3

T.Aus 2 2 2 3

Jute 3

S.Vegetables 4 4 4

Cattle 5 5 5 5 5 5 5 5 5 5 5 5

…

Decision Support System (DSS)

Recommendations1. Delayed seedling raising, gapfilling, skipping early fertilizer application2. Advance harvest3. Early harvest4. Pot culture (homestead), Use resistant variety5. Food storage, flood shelter, vaccination de-warming



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



Decision Tree

Harvesting decision

Harvest in early July

Wait until mid July

No flood

Flood

No flood

Flood

Quality lost (I)

Crop and input up to now saved (II)

Gain 100% yield and quality (III)

Entire crop and input upto now lost (IV)

Harvesting decision

Harvest in early July

Wait until mid July

No flood

Flood

No flood

Flood

Quality lost (I)

Crop and input up to now saved (II)

Gain 100% yield and quality (III)

Entire crop and input upto now lost (IV)



Risk Communication of flood forecasts



29

Sending SMS to Mobile

Risk Communication for Flood Forecasts

Mobile phone

Flag hoisting



Community responses to flood forecasts



Economic- Benefits

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

($485) per household – HH assets = TK. 18,500

( $270) per household – Agriculture = TK 12,500

($180) per household– Fisheries = TK.

8,800 ( $120) 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.)



Expansion of Areas



Thank you

S.H.M. FakhruddinTeam Leader- Hydrology

[email protected]


mailto:[email protected]

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Documents

early warning services

free powerpoint templates

warning response capacities

regional tsunami watch

rimes member states

asia rimes

levels national

rimes key activities