Operational Short-term Flood Forecasting for Bangladesh: An Introduction Bangkok Training Workshop...

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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 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