Operational Short-term Flood Forecasting for Bangladesh:

An Introduction

Bangkok Training Workshop

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

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

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 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

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

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: TACTICALPurpose 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 Forecasts 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 cycleIncorporated to drive Bangladesh routing model (MIKE)Extends 2-3 day Bangladesh operational forecasts to 12-13 days

danger levelWith data to hereSummary of 1-10 days forecasts for 2004

danger levelWith data hereWe forecast probability of danger flood level being exceeded 10 days later!1-10 days (cont)

danger levelWith data hereAnd.

danger levelWith data at peak flood hereAnd.We forecast a diminishing of the flood BUT a return to a new peak discharge and continuing flooding

danger levelWith data hereWe forecast probability of danger flood level being exceeded 10 days later!1-10 days (cont)

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

Ganges ensembles and risk: 2006

2006 Ensemble ForecastsBrahmaputra 7-10 day ForecastsGanges 7-10 day Forecasts

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:

Forecast StationsTotal : 18 stations(10 influced by Bhahmaputra and Ganges flows)

SerajganjArichaGoalondo

Gorai Railway BridgeKamarkhali

TongiMirpurDhakaDemraBhagyakul

SheolaSherpurMoulvi BazarSylhetSunamganj

NaogaonMohadevpur

Comparative Forecast Performance of CFAN (10-day) and FFWC(3-day) in 2006MAE: Mean Absolute Error

R2: Degree of determination or correlation

SerajganjForecast SchemeMAE (cm)R2CFAN190.879FFWC180.896

KamarkhaliForecast SchemeMAE (cm)R2CFAN190.965FFWC170.976

DhakaForecast SchemeMAE (cm)R2CFAN130.778FFWC140. 828

SherpurForecast SchemeMAE (cm)R2CFAN120.945FFWC140.935

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

Distance from Bahadurabad to Serajganj is 78 km

CFAN Prediction at Bahadurabad

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 kmSatellite precipitation estimates for calibration of ECMWF model precipRiver 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-spaceBrahmaputra Catchment-avg ForecastsECMWF 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

Pmax25th50th75th100thPfcstPrecipitationQuantilePmax25th50th75th100thPadjQuantileQuantile to Quantile MappingModel ClimatologyObserved ClimatologyThe purpose of this exercise is to remove systematic in precipitation errors in the model using corrections from observations (satellite and rain gauge)

OriginalAdjustedRank Histogram Corrections for BrahmanputraORIGINAL 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-varianceMean-Square-Error of the Ensemble-Mean shows skill out to 7-8 days

Discharge Multi-Model ForecastMulti-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 variationCoefficients vary with the forecast lead-timeRepresentative of the each basins 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 ProbabilitiesRainfall ProbabilityRainfall [mm]Discharge ProbabilityDischarge [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 Ensembles3 day4 day5 day2 day3 day4 day5 dayConfidence Intervals2004 Corrected Discharge Forecast ResultsObserved Q black dotEnsemble Members in color7 day8 day9 day10 day7 day8 day9 day10 day

50%95%Critical Q black dash

2004 Danger Level ProbabilitiesBrahmaputra 7-10 day ForecastsGanges 7-10 day Forecasts

2003 Brahmaputra Flood Probability2003 Ganges Flood Probability1 day2 day3 day4 day5 day1 day2 day3 day4 day5 day95%50%95%50%

2003 Danger Level Probabilities

2006 Danger Level ProbabilitiesBrahmaputra 7-10 day ForecastsGanges 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

Merge 3 data products together

Process data files

Temporally/spatially upscale/downscale to computation grid

Download new GTS/CMORPH/GPCP files

Process observed Precipitation

If errors reduced, copy new parameters into oper