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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!