Bapon Fakhruddin, PhD

Medium Range Ensembles Flood Forecasts for Community Level Applications

8 July 2015|4.1 Piha Meeting Room (16) (L)

Outlines

2

• Background & Objective

• Risk & Vulnerabilities- Agriculture

• Methodology

• Analysis & Results

• Runoff Prediction & Performance

• Flood Risk Map

• Impact Outlook

• Conclusion

Background & Problem Statement

• Despite advances in forecasting, most cases warning system fails (Parker

et al., 2009)

• Generation of long lead flood forecasts is highly challenging and

uncertain. Thus there is very limited application of it. (Fakhruddin, et.al

2012)

• A decision making process is essential in a social context where roles

and responsibilities are clearly shared for appropriate response (Morss

& Wahlb 2007)

3

The major objective of the program is to increase the lead time for flood

forecasting for agriculture risk management.

Early Warning Gaps

Dissemination to at-risk communities

Observation/ monitoring

Warning formulation

Community response

Data analysis

Prediction Risk assessment

Emergency response plansPublic education/ awareness

Mitigation 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

Integration

Language Localized, relevant

Institutional mechanism, linkages

SOPs

Redundant communication systems

Reach to special groups

Data sharing among agencies, compatibility

Public awareness

Communication of forecast limitations

Lack of trainers/ facilitators Resources to respond to warning

Regulatory framework for warning Stakeholders involvement and roles

Vulnerability and Risk Assessment

Hydrology

Crop Crop -Season

Jan-March April-June Jul-Sep Oct-Dec

Robi (dry) Kharif -I Kharif II Robi (dry)

Crop

Aus S H

T. Aman S H

Boro

0

100

200

300

400

500

600

700

0

10

20

30

40

50

60

70

80

90

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rain

fall

(mm

)

Wate

r Level

(mete

r-P

WD

)

Water Level Rainfall

Project Location

Meghna Basin

82,000 sq.km

Brahmaputra Basin

552,000 sq.km

Ganges Basin

1,087,000 sq.km

B A Y O F B E N G A L

BHUTAN

I N D I A

C H I N A

BANGLADESH

I N D I A

Bangladesh rivers receive runoff from a catchment of 1.72 million sq. km, around 12 times its land area

Topography of Bangladesh

• About 50% of the country within 6-7 m of MSL.

• About 68% of the country vulnerable to flood.

• 25 to 30% areas inundated in normal flood

History of Flooding

Methodology- Hydrological Models

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 forecasts

Statistical 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). Hydrolog.

Modeling

(IV). Generation of

Probabilistic Q

(V).

Forecast

Product

Initial Data Input

Initial data comes from number of sources and is

used to either drive the forecasts (e.g.,ECMWF

EPS), correct the forecasts and provide

calibration of the basin discharge

Data is passed on for statistical rendering and to force the hydrological models(Webster et al. 2011)

• Forecast downscaled to computation grid 0.5 x 0.5 degree

• Quantile-to-quantile mapping of downscaled forecasts at

each grid point/ forecast lead time using “Kernel approach”

Statistical Rendering: Correction of EPS systematic error

QR (III)

(I) E (I) S

(Webster et al. 2011)

Vulnerability and Risk Assessment

12

Risk Scoping

Risk

Characterization

Risk Evaluation

Risk Management

Feedback

• Establish target and

criteria through

consultation with

stakeholders

• Identify possible

•Risk event

•Source of stress

•Stress receptors

•Relationship

between sources

and receptor

• Estimate for risk

event and receptor:

•Likelihood of

exposure to

stressors

•Consequences

of exposure to

stress

•Develop risk

profile

•Compare event

and total risks with

targets and criteria

•Assess existing

risk management

practices against

risk profile

•Evaluate

treatment options

•Develop strategy

based on option

(Hay, 2007)

Needs Assessment of EWS

Target groups Decisions Forecast lead time requirement

Farmers Early harvesting, delayed planting, Fertilizer management

10 days

Crop systems selection, 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

Civil Defense Resources mobilization, planning evacuation and boats 5-10 days

Arrangements for women and children 5- 10 days

Distribution of water purification tablets 7 days

Ensembles Forecasts for 1-10 Days

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

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

Forecast Validation

Forecasts Dissemination

Website

E-mail ( Nationals stakeholders, District and Union Information Centers)

SMS ( SMS to DDMC members in 12 Pilot Unions)

400 stakeholders received

flood early warning during

monsoon through SMS

120 UISCs and 100 different

stakeholders received flood

early warning through e-mail

in local language.

19

Flood Risk Map

Community Response Options

Risk Communications

Community Responses to Flood Forecasts

Economic- Benefits

• In 2008 Flood, Economic Benefits on average per household at pilot areas

• Livestock's = $485 per household

• HH assets = $270 per household

• Agriculture = $180 per household

• Fisheries = $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 (World Bank, 2012).

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.)

Appreciations

Applications of Medium Range Probabilistic Flood Forecast for Societal Benefits - Lessons Learned from Bangladesh

Thanks you

25

Bapon Fakhruddin

www.shmfakhruddin.net