Operational Short-term Flood Forecasting for Bangladesh:

An Introduction

Bangkok Training Workshop

CFAB-CFAN-ADPC-ECMWF CARE/GT/NSF/USAID

The Problem: a The Problem: a question of scalequestion of scale

Bangladesh sits at the confluence of three of Bangladesh sits at the confluence of three of the largest rivers in South Asiathe largest rivers in South Asia

Each catchment region is very large and the Each catchment region is very large and the different phases of the monsoon “feed” the river different phases of the monsoon “feed” the river basins and the river discharge into Bangladeshbasins and the river discharge into Bangladesh

So, while Bangladesh flooding is regional, the So, while Bangladesh flooding is regional, the problem encompasses large scale aspects of the problem encompasses large scale aspects of the South Asia monsoon circulation South Asia monsoon circulation

Grand challenge:

No upstream data is available to Bangladesh for either the Ganges and the Brahmaputra from India.

Only hydrological data available is river flow measured at boundaries of India and Bangladesh

Forecast schemes have to assume that the Ganges and Brahmaputra are ungauged river basin

Background TechniquesBackground Techniques

To approach the problem of To approach the problem of catchment precipitation forecasting, we catchment precipitation forecasting, we have developed a nest of physical have developed a nest of physical models that depend on:models that depend on:

• Satellite dataSatellite data

• Forecasts from operational centers Forecasts from operational centers (e.g., NCEP, ECMWF)(e.g., NCEP, ECMWF)

• Statistical post-processingStatistical post-processing Each of the modeling module is designed to be Each of the modeling module is designed to be readily transportable from GT to other readily transportable from GT to other infrastructures infrastructures

Examples of forecasts: 2004 Examples of forecasts: 2004

We now provide examples of the We now provide examples of the forecasts for the three tiers for Ganges forecasts for the three tiers for Ganges and Brahmaputra river discharge into and Brahmaputra river discharge into BangladeshBangladesh

A by-product of the forecasting A by-product of the forecasting schemes are regional precipitation schemes are regional precipitation forecasts for the catchment basins and forecasts for the catchment basins and subregions within. Similar forecasts are subregions within. Similar forecasts are possible for Bangladeshpossible for Bangladesh

Provide overlapping forecasts that allow overlapping strategic and tactical decisions:

Seasonal: 1-6 months: STRATEGIC

Intraseasonal: 20-30 days: STRATEGIC/TACTICAL

Short-term: 1-10 days: TACTICAL

Purpose of an overlapping 3-Purpose of an overlapping 3-tierred forecast systemtierred forecast system

System based on provision of forecasts that System based on provision of forecasts that are of optimal utility (Georgia Tech approach) are of optimal utility (Georgia Tech approach) while being a scientifically tractable while being a scientifically tractable

Forecasts start May each yearForecasts start May each year

Short-term Forecast SystemDeveloped for Bangladesh

Forecast of rainfall and precipitation in probabilistic form updated every day. Skillful out 7-10 days.

Provide probability of flood level exceedance at the entry point of the Ganges & Brahmaputra. Useful for emergency planning, and selective planting or harvesting to reduce potential crop losses at the beginning or end of the cropping cycle

Incorporated to drive Bangladesh routing model (MIKE) Extends 2-3 day Bangladesh operational forecasts to

12-13 days

danger leveldanger level

With data to hereWith data to here

Summary of Summary of 1-10 days 1-10 days

forecasts for forecasts for 20042004

danger leveldanger level

With data hereWith data here

We forecast probability of We forecast probability of danger flood level being danger flood level being exceeded 10 days later!exceeded 10 days later!

1-10 days (cont)1-10 days (cont)

danger leveldanger level

With data hereWith data here

And…….And…….

danger leveldanger level

With data at peak With data at peak flood hereflood here

And…….And…….

We forecast a diminishing We forecast a diminishing of the flood BUT a return of the flood BUT a return to a new peak discharge to a new peak discharge and continuing floodingand continuing flooding

danger leveldanger level

With data hereWith data here

We forecast probability of We forecast probability of danger flood level being danger flood level being exceeded 10 days later!exceeded 10 days later!

1-10 days (cont)1-10 days (cont)

Short-term 10-day Operational Forecasts for Brahmaputra and Threshold Probabilities

Summary of forecasts and exceeding of danger levelSummary of forecasts and exceeding of danger level

danger leveldanger level

danger leveldanger level

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.Ganges ensembles and risk: 2006

2006 Ensemble Forecasts

Brahmaputra 7-10 day Forecasts Ganges 7-10 day Forecasts

Bahadurabad

Hardinge-Bridge

• Ensemble Mean

• 16% and 84% quantiles respectively for -1 Standard deviation and +1 Standard deviation (roughly 68% of the time the forecasts fall within these bounds)

• 97.5% and 2.5% quantiles (upper and lower limits of 95% confidence limits)

Originally, CFAN generated 51 sets of ensemble forecasts at Bahadurabad and Hardinge-Bridge. However, the following selective forecast simulations were carried out from operational viewpoint:

Serajganj

Aricha

Goalondo

Gorai Railway Bridge

Kamarkhali

Tongi

Mirpur

Dhaka

Demra

Bhagyakul

Sheola

Sherpur

Moulvi Bazar

Sylhet

Sunamganj

Bhairab Bazar

Naogaon

Mohadevpur

Forecast Stations

Total : 18 stations

(10 influced by Bhahmaputra and Ganges flows)

Serajganj

Forecast Scheme

MAE (cm)R2

CFAN 19 0.879

FFWC 18 0.896

Kamarkhali

Forecast Scheme

MAE (cm)R2

CFAN 19 0.965

FFWC 17 0.976

Dhaka

Forecast Scheme

MAE (cm)R2

CFAN 13 0.778

FFWC 14 0. 828

Sherpur

Forecast Scheme MAE (cm) R2

CFAN 12 0.945

FFWC 14 0.935

Comparative Forecast Performance of CFAN (10-day) and FFWC(3-day) in 2006

MAE: Mean Absolute Error

R2: Degree of determination or correlation

Serajganj: Comparison between Observed and Forecast (10-day) Water Levels

9.50

10.00

10.50

11.00

11.50

12.00

12.50

13.00

13.50

15-Jun-06 05-Jul-06 25-Jul-06 14-Aug-06 03-Sep-06 23-Sep-06 13-Oct-06 02-Nov-06

Discharge (m

3/s)

Forecast (Ensemble-Mean) Observed

Discharge at Bahadurabad (10-day forecast in 2006)

5000

15000

25000

35000

45000

55000

15-Jun 05-Jul 25-Jul 14-Aug 03-Sep 23-Sep 13-Oct 02-Nov

Discharge (m

3/s)

Ensemble-Mean Rated Observed

Comparison of 10-day CFAN Forecast at Serajganj with Observed Water Levels:

Distance from Bahadurabad to Serajganj

is 78 km

Comparison of 10-day CFAN Forecast at Serajganj with Observed Water Levels:

Distance from Bahadurabad to Serajganj

is 78 km

CFAN Prediction at Bahadurabad

Kamarkhali: Comparison between Observed and Forecast (10-day) Water Levels

2.00

3.00

4.00

5.00

6.00

7.00

8.00

15-Jun-06 05-Jul-06 25-Jul-06 14-Aug-06 03-Sep-06 23-Sep-06 13-Oct-06 02-Nov-06

Discharge (m

3/s)

Forecast (Ensemble-Mean) Observed

Discharge at Hardinge-Bridge (10-day forecast in 2006)

0

10000

20000

30000

40000

50000

60000

15-Jun 05-Jul 25-Jul 14-Aug 03-Sep 23-Sep 13-Oct 02-Nov

Discharge (m

3/s)

Ensemble-Mean Rated Observed

Comparison of 10-day Forecast at Kamarkhali with

Observed Water Levels:

Distance from Hardinge-Bridge to Kamarkhali is

97 km

Comparison of 10-day Forecast at Kamarkhali with

Observed Water Levels:

Distance from Hardinge-Bridge to Kamarkhali is

97 km

CFAN Prediction at Hardinge-Bridge

The Scheme

• The short-term prediction scheme depends on the ECMWF daily ensemble forecasts of rainfall, and thermodynamical variables over the Indian Ocean, Asia and the Western Pacific Ocean

• Forecasts are corrected statistically to reduce systematic error

• Rainfall introduced into a suite of hydrological models which allow calculation of G&B discharge into Bangladesh

• Statistical probabilities are then generated

Currently 1-10 day 51 ensemble data 75 km

Soon, 1-15 days 25 km

Satellite precipitation estimates for calibration of ECMWF model precip

River discharge from two points on G and B.

Need more

Read basin wide ECMWF precip estimates.

Approach: calculate historical NWP-climatology PDF and observation-climatology PDF for each grid using a “kernel” method

For each forecast ensemble, determine its quantile in model-space and extract equivalent quantile in observation-space

Brahmaputra Catchment-avg Forecasts

ECMWF Ensemble Precipitation Forecast Adjustments -- mapping forecasts from “model-” to “observational-”space

All models contain systematic errors. Here we use past observations (our approximation of truth) and past predictions to apply to the forecasts

Pmax

25th 50th 75th 100th

Pfcst

Pre

cipi

tatio

n

Quantile

Pmax

25th 50th 75th 100th

Padj

Quantile

Quantile to Quantile Mapping

Model Climatology “Observed” Climatology

The purpose of this exercise is to remove systematic in precipitation errors in the model using corrections from observations (satellite and rain gauge)

Original AdjustedRank Histogram Corrections for Brahmanputra

ORIGINAL ADJUSTED

ECMWF Ensemble Precipitation Forecast Adjustments -- mapping forecasts from “model-” to “observational-”space Brahmaputra Adjusted Forecasts •Benefits:

--Gridded “realistic” forecast values--spatial- and temporal covariances preserved

•Drawbacks:--limited sample set for model-space PDF (2 yrs)--rank histograms show “under-variance”

Mean-Square-Error of the Ensemble-Mean shows skill out to 7-8 days

Discharge Multi-Model Forecast

Multi-Model-Ensemble Approach:

• Rank models based on historic residual error using current model calibration and “observed” precipitation

•Regress models’ historic discharges to minimize historic residuals with observed discharge

•To avoid over-calibration, evaluate resultant residuals using Akaike Information Criteria (AIC)

•If AIC minimized, use regression coefficients to generate “multi-model” forecast; otherwise use highest-ranked model => “win-win” situation!

Model Comparisons for the Ganges

Multi-Model Forecast Regression Coefficients

- Lumped model (red)- Distributed model (blue)

• Significant catchment variation

• Coefficients vary with the forecast lead-time

• Representative of the each basin’s hydrology

-- Ganges slower time-scale response

-- Brahmaputra “flashier”

Results: --show improvements--compromise between timing (distributed)

with amplitude (lumped) => use of different error measure in

selection process

Future:-- structure allows incorporating other

models-- KNN technique to select based on current precipitation/discharge conditions

Multi-Model Ensemble Forecasts

Combining Precipitation (Ensemble) Probability with Model Error:

Forecasting “Truer” Discharge Probabilities

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 1 2 3 4 5 6

Rainfall Probability

Rainfall [mm]

Discharge Probability

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

10,000 30,000 50,000 70,000 90,000

Discharge [m3/s]

Above danger level probability 36%Greater than climatological seasonal risk?

A simpler hydrological approach to hydrological modeling: Isochrones?

Agudelo and Hoyos

Brahmaputra Discharge Ensembles

3 day 4 day

5 day

2 day

3 day 4 day

5 day

Confidence Intervals

2004 “Corrected” Discharge Forecast Results

Observed Q black dotEnsemble Members in color

7 day 8 day

9 day 10 day

7 day 8 day

9 day 10 day

50% 95%Critical Q black dash

2004 Danger Level Probabilities

Brahmaputra 7-10 day Forecasts Ganges 7-10 day Forecasts

2003 Brahmaputra Flood Probability 2003 Ganges Flood Probability

1 day 2 day

3 day 4 day

5 day

1 day 2 day

3 day 4 day

5 day95%

50%

95%

50%

2003 Danger Level Probabilities

2006 Danger Level Probabilities

Brahmaputra 7-10 day Forecasts Ganges 7-10 day Forecasts

Automatic Forecast Generationbackground processes

Update Observed Discharge Data

Download FFWC web page and extract

new stage heights

Convert stage heights to discharge using

rating curves

Update operational discharge files

Process “observed” Precipitation

Download new GTS/CMORPH/GPCP

files

Process data files Temporally/spatially upscale/downscale to

computation grid

Merge 3 data products together

Calibrate Distributed Model

Sample new parameter spaces using

“simulated annealing” technique

If errors reduced, copy new parameters into

operational parameter file

Automatic Forecast Generationforecast trigger process

Weather forecast processing and dischage forecast generation

Files arrived? Then process (510) GRIB-format files and Temporally/spatially upscale/downscale to computation grid

Process data files Temporally/spatially upscale/downscale to

computation grid

Generate “observed” catchment-averaged

precipitation for lumped

hydrologic model

Generate forecast catchment-averaged

precipitation for lumped hydrologic

model

Generate “observed”

precipitation files for

distributed hydrologic

model

Generate forecast

precipitation files for

distributed hydrologic

model

“observed” precipitation

input

“observed” precipitation

input

Automatic Forecast Generationforecast trigger process (cont)

Calibrate lumped

model and generate hindcasts

Update soil moisture and in-stream flows and

generate hindcasts

Done for each forecast lead-time

and each catchment

Incorporate updated

discharge file

Incorporate new calibration

parameter file

Calibrate auto-regressive error correction model and generate

corrected hindcasts

Calibrate multi-model forecasts

Calibrate auto-regressive error correction model and generate

corrected hindcasts

Automatic Forecast Generationforecast trigger process (cont)

Generate multi-model hindcasts using “observed” precipitation

Generate above critical level cumulative probabilities

Generate multi-model ensemble discharge forecasts using

ensemble precipitation forecasts

Generate model error ensembles using KNN technique

Merge ensemble discharge forecasts with model error ensembles

Transfer data to web site for Bangladesh download

Five Pilot Sites chosen in 2006 consultation workshops based on biophysical, social criteria:

Rajpur Union -- 16 sq km-- 16,000 pop.

Uria Union-- 23 sq km-- 14,000 pop.

Kaijuri Union-- 45 sq km-- 53,000 pop.

Gazirtek Union-- 32 sq km-- 23,000 pop.

Bhekra Union-- 11 sq km-- 9,000 pop.

A v e r a g e D a m a g e ( T k . ) p e r H o u s e h o l d i n P i l o t U n i o n

7 , 2 5 5

2 8 , 7 4 5

6 0 , 9 9 3

6 4 , 0 0 0

4 0 5 8

0

1 0 , 0 0 0

2 0 , 0 0 0

3 0 , 0 0 0

4 0 , 0 0 0

5 0 , 0 0 0

6 0 , 0 0 0

7 0 , 0 0 0

U r i a G a z i r t e k K a i j u r i R a j p u r B e k r a

U n i o n

Average Damage (Tk) per

Household

Livelihood groups Rajpur Uria Kaijuri Gazirtek Bhekra Farmers/share cropper 55 48 55 52 71 Agriculture labour 15 30 5 14 13 Non-agriculture labour

20 9 15 8 4

Fisherman 2 4 1 2 2 Services 4 1 1 2 4 Business 2 3 10 2 4 Loom/transport/catage 1 4 12 2 1 Others 1 1 1 18 1

Vulnerable Sectors

Short range (1 – 10 days)

Medium range (20 – 25 days)

Long range (1-4 months)

Agriculture 1. Harvesting of crops

2. stocking of seeds for emergency period

3. delaying of seed bed preparation

4. abstaining from planting crops

5. awareness to the people through miking, postering and drumming regarding forthcoming flood

6. Staggered crop harvesting

7. preparation to rescue the assets and life from flood

8. arrangement of seed bed on high land

9. Crop/variety choice based on duration

Livelihoods

What can be done with useful forecasts?

Tomorrow

Thank You!