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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 fakhruddin@rimes.int www.shmfakhruddin.com. Measuring ‘Real’ Impact

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

  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 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. 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 Probabilistic Flood Forecasting and Applications in Agriculture

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

  16. Flood risk management at community level decisions and forecast lead time requirement

  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 • Accounting for uncertainties • Final error correction • Generation of discharge forecast PDF • Critical level probability forecast Discharge data 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 9 day 10 day

  19. 2012- Brahmaputra Ensemble Forecasts

  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 DEM Distribution of H values

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

  23. Flood Risk Map

  24. Decision Support System • Community Outcomes • Delayed seedling raising, gapfilling, skipping early fertilizer application • Advance harvest of paddy ( 70-80 % 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) • 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. Sending SMS to Mobile Risk Communication for Flood Forecasts Mobile phone Flag hoisting 29

  30. Community responses to flood forecasts

  31. 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). Economic- Benefits

  32. Expansion of Areas

  33. Thank you • S.H.M. Fakhruddin • Team Leader- Hydrology • fakhruddin@rimes.int • www.shmfakhruddin.com

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