1. Operational Short-term Flood Forecasting for Bangladesh:
An Introduction
Bangkok Training Workshop
CFAB-CFAN-ADPC-ECMWF
CARE/GT/NSF/USAID

2. The Problem: a question of scale
Bangladesh sits at the confluence of three of the largest rivers in South Asia
Each catchment region is very large and the different phases of the monsoon “feed” the river basins and the river discharge into Bangladesh
So, while Bangladesh flooding is regional, the problem encompasses large scale aspects of the South Asia monsoon circulation

3. 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

4. Background Techniques
To approach the problem of catchment precipitation forecasting, we have developed a nest of physical models that depend on:
Satellite data
Forecasts from operational centers (e.g., NCEP, ECMWF)
Statistical post-processing
Each of the modeling module is designed to be readily transportable from GT to other infrastructures

5. Examples of forecasts: 2004
We now provide examples of the forecasts for the three tiers for Ganges and Brahmaputra river discharge into Bangladesh
A by-product of the forecasting schemes are regional precipitation forecasts for the catchment basins and subregions within. Similar forecasts are possible for Bangladesh

6. Purpose of an overlapping 3-tierred forecast system
System based on provision of forecasts that are of optimal utility (Georgia Tech approach) while being a scientifically tractable
Provide overlapping forecasts that allow overlapping strategic and tactical decisions:
Seasonal: months: STRATEGIC
Intraseasonal: days: STRATEGIC/TACTICAL
Short-term: days: TACTICAL
Forecasts start May each year

7. Short-term Forecast System Developed 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 days

8. Summary of 1-10 days forecasts for 2004
danger level
With data to here

9. 1-10 days (cont)
We forecast probability of danger flood level being exceeded 10 days later!
danger level
With data here

10. And…….
With data here
danger level

11. And…….
We forecast a diminishing of the flood BUT a return to a new peak discharge and continuing flooding
danger level
With data at peak flood here

12. 1-10 days (cont)
We forecast probability of danger flood level being exceeded 10 days later!
danger level
With data here

13. Short-term 10-day Operational Forecasts for
Brahmaputra and Threshold Probabilities
danger level
danger level
Summary of forecasts and exceeding of danger level

14. Ganges ensembles and risk: 2006

15. Brahmaputra 7-10 day Forecasts
2006 Ensemble Forecasts
Brahmaputra 7-10 day Forecasts
Ganges 7-10 day Forecasts

17. 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:
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)

18. (10 influced by Bhahmaputra and Ganges flows)
Forecast Stations
Sheola
Sherpur
Moulvi Bazar
Sylhet
Sunamganj
Naogaon
Mohadevpur
Serajganj
Aricha
Goalondo
Bhairab Bazar
Tongi
Mirpur
Dhaka
Demra
Bhagyakul
Gorai Railway Bridge
Kamarkhali
Total : 18 stations
(10 influced by Bhahmaputra and Ganges flows)

19. Comparative Forecast Performance of CFAN (10-day) and FFWC(3-day) in 2006
Sherpur
Forecast Scheme
MAE (cm) R2
CFAN 12
0.945
FFWC 14
0.935
MAE: Mean Absolute Error
R2: Degree of determination or correlation
Dhaka
Forecast Scheme
MAE (cm) R2
CFAN 13
0.778
FFWC 14
0. 828
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

20. Comparison of 10-day CFAN Forecast at Serajganj with Observed Water Levels: Distance from Bahadurabad to Serajganj is 78 km
CFAN Prediction at Bahadurabad

21. 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

22. 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

24. Satellite precipitation estimates for calibration of ECMWF model precip
River discharge from two points on G and B.
Need more
Currently 1-10 day 51 ensemble data 75 km
Soon, 1-15 days 25 km

25. ECMWF Ensemble Precipitation Forecast Adjustments -- mapping forecasts from “model-” to “observational-”space
Brahmaputra Catchment-avg Forecasts
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
Discharge forecasts employ a blend of precipitation forecasts (ECMWF) and near-real-time “observations” (GPCP or CMORPH)
Precipitation “observations” used to initialize discharge model conditions (soil-moisture, lagged subcatchment discharges, etc.) before the forecast period.
GPCP and CMORPHprecipitation “observations” based on IR and microwave satellite data, but distinctly different approaches. (Note: potential systematic error in GPCP in 2003 data shown here)
Adjust ECMWFcatchment-average ensemble forecasts to satisfy similar distribution as the “observations” using the criterion of the uniformity of the verification rank; method similar to that proposed method of Hamill and Colucci, 1997

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

28. Quantile to Quantile Mapping
Model Climatology
“Observed” Climatology
Pmax
Pmax
Precipitation
Pfcst
Padj
25th
50th
75th
100th
25th
50th
75th
100th
Quantile
Quantile
The purpose of this exercise is to remove systematic in precipitation errors in the model using corrections from observations (satellite and rain gauge)

29. Rank Histogram Corrections for Brahmanputra
Original
Adjusted
ORIGINAL ADJUSTED

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

33. 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!
-- multi-model ensemble approach done on daily basis and for each forecast lead-time
-- calculate historic “simulated” discharges of each model (“data-based” and “distributed”) separately, using “observed” weather variables (precipitation, wind-speed, etc) as inputs (I.e. not using forecast data).
-- perform simple regression of the two models’ discharges against measured discharge to minimize forecast error
-- evaluate whether reduction in error residuals is significant using Akaike Information Criteria (AIC) ( which introduces a penalty function for the addition of extra regression variables)
-- If AIC minimized (smaller the better) by regressing the two models, then use the resulting regression coefficients to generate “multi-model ensemble”; if not, then use the best single model in the discharge forecast; this assures there is no penalty in introducing additional discharge models into the forecast scheme

34. Model Comparisons for the Ganges
On the left are 1 to 6 day discharge forecasts for the Ganges Basin for last year’s monsoon season for the “data-based” modeling scheme. On the right is the same results for the “distributed” model
General, discharge amplitudes appear to be better forecast for the “data-based” model. General, discharge timing appears to be better forecast for the “distributed” model

35. 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”
The data-based and distributed regression multi-model regression coefficients are shown for the Ganges (top panel) and for the Brahmaputra (bottom panel on left) at different forecast lead-times from 1 out to 10 days
Notice that the “weighting” of each model varies with both forecast lead time and for the different catchments.
Ganges is a “slower time-scale” basin, with the discharge more determined by groundwater discharge. The Brahmaputra has a “flashier” discharge response.
For the Ganges, the “distributed” model becomes more heavily weighted at longer forecast lead-times as one would expect given the “data-based” model is not as accurate in capturing basin “slow time-scale” response.
However, for the Brahmaputra, the “distributed” model weighting becomes less with longer forecast lead times. This may be indicative of modeling deficiencies

36. Multi-Model Ensemble Forecasts
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
The combined multi-model ensemble1 to 6 day forecasts for the Ganges basin are shown at the right. Overall, we see improvements in the multi-model approach, with somewhat of a compromise struck between the better “amplitude” forecasting of the “data-based” model with the better forecast “timing” of the “distributed” model
Future work:
Incorporate other hydrologic models into the framework, such as the US Geological Survey’s Modular Modeling System (MMS) / Prcipitation-Runoff Modeling System (PRMS)
Also, the “multi-model” regression set can first be selected for by using a “K-Nearest-Neighbor” non-parametric technique. This technique allows the multi-model coefficients to be derived from an historic set that is similar to the current forecast time hydrological conditions

37. Combining Precipitation (Ensemble) Probability with Model Error: Forecasting “Truer” Discharge Probabilities
Rainfall Probability
Discharge Probability
We now discuss how we “dress” the ECMWF EPS precipitation-derived discharge ensembles with estimates of model error to produce more accurate discharge probability forecasts.
In particular, we are interested in generating daily forecast exceedance probabilities of severe flood-stage discharges (approximately 60,000m^3/s and 67,000 m^3/s for the Ganges and Brahmaputra, respectively)
Rainfall [mm]
Discharge [m3/s]
Above danger level probability 36%
Greater than climatological seasonal risk?

38. A simpler hydrological approach to hydrological modeling: Isochrones?
Agudelo and Hoyos

41. 2004 “Corrected” Discharge Forecast Results
Brahmaputra Discharge Ensembles
Confidence Intervals
2 day
Critical Q black dash
Observed Q black dot
Ensemble Members in color
50%
95%
7 day
8 day
7 day
8 day
3 day
3 day
4 day
4 day
These figures show the 1 to 5 day discharge forecasts for the Brahmaputra and Ganges basins for We have randomly selected 51 new ensemble members from the combined model error/precipitation forecast uncertainty PDF discussed in the previous slide
5 day
5 day
9 day
10 day
9 day
10 day

42. 2004 Danger Level Probabilities
Brahmaputra 7-10 day Forecasts
Ganges 7-10 day Forecasts

43. 2003 Brahmaputra Flood Probability 2003 Ganges Flood Probability
1 day
2 day
1 day
2 day
3 day
4 day
3 day
4 day
Using the combined model error/precipitation forecast uncertainty PDF discussed previously, we can generate confidence intervals for our discharge forecasts. Shown here are 95% and 50% confidence intervals for the 1 to 5 day forecasts only.
Generally, we can see that observed discharge shown by the solid black line appropriately fell within the confidence interval with the frequency you would expect.
Also the black dashed lines represent the respective discharge danger-levels for the Brahmaputra and Ganges basins.
5 day
95%
5 day
95%
50%
50%

44. 2003 Danger Level Probabilities
From the combined model error/precipitation forecast uncertainty PDF discussed previously, we can also calculate exceedance probabilities for “above danger-level” discharges. Shown here by the pink line are the exceedance probabilites for 1 to 6 day forecasts for the Brahmaputra (left) and Ganges (right). The observed discharge time-series is also shown by the thin black line (its magnitude has been normalized by the specified value of the danger level discharge). The black dashed line represents an exceedance level probability of one.

45. 2006 Danger Level Probabilities
Brahmaputra 7-10 day Forecasts
Ganges 7-10 day Forecasts

46. Automatic Forecast Generation background processes

47. Automatic Forecast Generation forecast trigger process

48. Automatic Forecast Generation forecast trigger process (cont)

49. Automatic Forecast Generation forecast trigger process (cont)

50. 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.

51. Livelihoods
What can be done with useful forecasts?

52. Tomorrow

53. Thank You!