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Ensembles Probabilistic Long Lead Flood Forecasts For Community Level Applications

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Title: Ensembles Probabilistic Long Lead Flood Forecasts For Community Level Applications


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_at_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
Organizational Chart
7
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
  1. 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
  1. 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
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

Flood forecast

RIMES
RIMES

FFWC

Discharge translation
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
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
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 --gt
probabilities of flood classes
DEM
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 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
  • 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)
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
  • 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
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. 18,500 ( 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).

32
Expansion of Areas
33
Thank you
  • S.H.M. Fakhruddin
  • Team Leader- Hydrology
  • fakhruddin_at_rimes.int
  • www.shmfakhruddin.com
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