1. Team Leader- Hydrology
The Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES)
Ensembles Probabilistic Long Lead Flood Forecasts For Community Level Applications
S.H.M. Fakhruddin
Team Leader- Hydrology
Measuring ‘Real’ Impact
Monday 25th June 2012
UK CDS, Wellcome Trust, 215 Euston Road, London NW1 2BE
Free Powerpoint Templates

2. Discussion Topics About RIMES & Key Activities End to End EWS
Case Study on Flood forecasting and Agriculture Risk Management

3. RIMES Member States

4. 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

5. Governance
Council
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

6. Organizational Chart Chief Scientist Tsunami Early Warning
Warning Coordination Scientist
Seismologist
Oceanographer
Telecommunications Specialist
System Analysts (2)
Decision-support Tool Development Specialist
Seconded Scientists (6)
Team Leader
Early Warning
Tsunami Hazard and Risk Assessment Expert
Climate Risk Management
Earthquake Hazard and Risk Assessment Expert
Project Teams
Climate Impact Assessment Team
GIS and Survey Specialist
Secretariat
Council
Director, Program Unit
Program Management
Tsunami Watch Provision
Support to Hydro-Met Services
Societal Applications
Capacity Building Specialist
Chief
Finance Officer
Human Resource and Administration Officer
ICKM Specialist
Climate Forecast Application Team
Climate Change
Seasonal Forecasting
Severe Weather
Synoptician
Hydrologist
System Analyst
Seconded Scientists (2)

7. Key services
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
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

8. Key services continued
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

9. EW System

10. EW System Structure Detection Subsystem Management Subsystem
Monitoring, detection, data Assessment, data analysis, prediction
Management Subsystem
Risk Assessment, interpretation, communication
Response Subsystem
Interpretation, confirmation and response

11. Reasons for Warning Failure?

12. Gaps Regulatory framework for warning
Stakeholders involvement and roles
Observation/ monitoring
Aging and insufficient observation and data communication facilities
Data analysis
Data sharing among agencies
Numerical prediction capability
Skilled human resource
Capacity to make use of new generation forecasts
Prediction
Risk assessment
Potential impact assessment
Local level potential impact assessment not done
Language
Localized, relevant
Warning formulation
Preparation of response options
Institutional mechanism, linkages
SOPs
Redundant communication systems
Reach to special groups
Dissemination to at-risk communities
Public awareness
Communication of forecast limitations
Lack of trainers/ facilitators
Resources to respond to warning
Emergency response plans
Public education/ awareness
Mitigation programs
Community response

13. A Case Study- Bangladesh

14. 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

15. Institutional Collaboration For Sustainable End-to-end Flood Forecasts System
Climate (rainfall and di
scharge)
forecasting technology
RIMES- CFAN
BMD
RIMES
Flood forecast
RIMES
FFWC
Discharge translation
Agro met
translation
Interpretation
DMB, DAE
RIMES, Local Partners
Communication
RIMES, Local Partners
End users

16. Forecast lead time requirement
Flood risk management at community level decisions and forecast lead time requirement
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)
Household
Storage of dry food, safe drinking water, food grains, fire wood
Collecting vegetables, banana
1 week
With draw money from micro-financing institutions
Fisherman
Protecting fishing nets
Harvesting fresh water fish from small ponds
DMCs
Planning evacuation routs and boats
20 – 25 days
Arrangements for women and children
Distribution of water purification tablets
Char households
Storage of dry food, drinking water, deciding on temporary accommodation

17. Discharge Forecast Schemes
(I). Initial Data Input
(II). Statistical Rendering
(III). Hydrological Modeling
(IV). Generation of Probabilistic Q
(V). Forecast Product
Hydrological Model
Lumped
Distributed
Multi-Model Discharge Forecasting
Discharge data
Accounting for uncertainties
Final error correction
Generation of discharge forecast PDF
Critical level probability forecast
Hydrologic model parameters
NOAA and NASA (i.e.CMORPH and GPCP) satellite precipitation & GTS rain gauge data
ECMWF Operational ensemble forecast
Downscaling of forecasts
Statistical correction

18. 2007 Flood- Brahmaputra Ensemble Forecasts and
Danger Level Probabilities
7-10 day Ensemble Forecasts
7-10 day Danger Levels
7 day
8 day
7 day
8 day
9 day
10 day
2005 (not shown) and 2006 (shown here) discharges for both Brahmaputra (left) and Ganges were well below severe flooding levels, as forecasted.
9 day
10 day 18

19. 2012- Brahmaputra Ensemble Forecasts
2005 (not shown) and 2006 (shown here) discharges for both Brahmaputra (left) and Ganges were well below severe flooding levels, as forecasted. 19

20. 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

21. Distribution of H combined with DEM --> probabilities of flood classes
Distribution of H values

22. Vulnerability & Flood Risk Assessment
Development of flood risk map which will include:
low probability
medium probability
high probability

23. Flood Risk Map

24. Decision Support System
High flood J F M A S O N D
T.Aman 1 3
T.Aus 2
Jute
S.Vegetables 4
Cattle 5 …
Community Outcomes
Delayed seedling raising, gapfilling, skipping early fertilizer application
Advance harvest of paddy ( % mature)
Early harvest of jute for rotten in water
Pot culture (homestead), Use resistant variety
Food storage, flood shelter, vaccination de-warming

25. Decision Support System (DSS)
High flood J F M A S O N D
T.Aman 1 3
T.Aus 2
Jute
S.Vegetables 4
Cattle 5 …
Recommendations
Delayed seedling raising, gapfilling, skipping early fertilizer application
Advance harvest
Early harvest
Pot culture (homestead), Use resistant variety
Food storage, flood shelter, vaccination de-warming

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

27. Decision Tree

28. Risk Communication of flood forecasts

29. Risk Communication for Flood Forecasts
Mobile phone
Sending SMS to Mobile
Flag
hoisting 29

30. Community responses to flood forecasts

31. 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, ( $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 in benefits over a ten-year period may be realized (WB).

32. Expansion of Areas

33. Team Leader- Hydrology
Thank you
S.H.M. Fakhruddin
Team Leader- Hydrology