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

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: 1-6 months: STRATEGIC Intraseasonal: 20-30 days: STRATEGIC/TACTICAL Short-term: 1-10 days: TACTICAL Forecasts start May each year

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 12-13 days

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

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

And……. With data here danger level

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

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

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

Ganges ensembles and risk: 2006

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

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)

(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)

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

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

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

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

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

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)

Rank Histogram Corrections for Brahmanputra Original Adjusted ORIGINAL ADJUSTED

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

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

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

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

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

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?

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

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 2003. 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

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 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%

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.

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

Automatic Forecast Generation background processes

Automatic Forecast Generation forecast trigger process

Automatic Forecast Generation forecast trigger process (cont)

Automatic Forecast Generation forecast trigger process (cont)

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.

Livelihoods What can be done with useful forecasts?

Tomorrow

Thank You!