Thursday, 18 January 2007: 3:30 PM
Seamless Daily to Seasonal Ensemble Flood Forecasting for Bangladesh
211 (Henry B. Gonzalez Convention Center)
The country of Bangladesh experiences life-threatening floods in the basins of the Ganges and Brahmaputra rivers flowing through the country with tragic regularity. These floods result in loss of life on a scale that often greatly eclipses the deaths due to natural disasters in developed countries. Flooding in these basins can occur on weekly time scales (as occurred during the severe Brahmaputra floods of 2004) to seasonal time scales (as occurred during the disastrous floods of 1998). Beginning in 2003, the Climate Forecasting Applications for Bangladesh (CFAB) project began issuing operational flood forecasts to the country of Bangladesh over a wide-range of time scales to provide advanced warning of severe flood-stage discharges in the catchments of the Ganges and Brahmaputra basins. In this paper we discuss the real-time operational multi-model flood forecast schemes for the upper basins of the Ganges and Brahmaputra rivers based on a seamless application of the current European Centre for Medium-Range Weather Forecasts (ECMWF) 51-member ensemble weather forecasts and 41-member climate forecasts. Currently, CFAB produces separate precipitation and discharge forecasts from an enhanced model at intra-seasonal time-scales. Although in the near future we anticipate a statistical merger of these multi-time scale forecasts, here we focus on the results of applying the current operational ECMWF weather and seasonal ensemble forecasts to probabilistic discharge forecasting for Bangladesh.
In addition to the ECMWF products, the discharge forecasts utilize near-real-time GPCP and CMORPH satellite and NOAA CPC rain gauge precipitation estimates and near-real-time discharge estimates from the Bangladesh Flood Forecasting and Warning Centre. In order to generate fully automated probabilistic river discharge forecasts from 1-day out to 6-months in advance, these schemes utilize statistical dressing and a downscaling technique to merge the ECMWF weather and seasonal forecasts. These techniques also ensure reliability in both the weather and discharge forecasts and skill no worse than a climatological forecast or persistence.