HR: 12:05h
AN: H52A-07    [Abstracts]
TI: ACCOUNTING FOR UNCERTAINTIES IN GENERATING RELIABLE PROBABILISTIC FLOOD FORECASTS FOR BANGLADESH
AU: * Hopson, T M
EM: hopson@ucar.edu
AF: Advanced Study Program and Research Applications Laboratory, National Center for Atmospheric Research P.O. Box 3000, Boulder, CO 80307-3000, United States
AU: Webster, P J
EM: pjw@eas.gatech.edu
AF: School of Earth and Atmospheric Sciences, Georgia Institute of Technology 311 Ferst Avenue, Atlanta, GA 30332-0340, United States
AB: 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 and of this year) 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 probabilistic 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 uncertainty estimator module to our 1- to 10-day in-advance automated real-time operational multi-model flood forecast scheme for the upper basins of the Ganges and Brahmaputra rivers. These forecasts are based on an application of the European Centre for Medium-Range Weather Forecasts (ECMWF) 51-member ensemble weather forecasts, 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. The uncertainty estimator module estimates multi-model hydrologic error utilizing daily-updated hindcasts, which are separate from the forecasted weather variable uncertainty. Such a separation of error sources is done to maximize the sharpness of the final forecast probability distribution function (PDF), as well as to enhance the utility of the ensemble spread as an indicator of ensemble skill; for this latter feature of ensemble forecasts, we also present a new measure to test the spread-skill utility. In the final step of the uncertainty module, we merge these two sources of uncertainty together while at the same time providing an additional forecast error correction. This last step utilizes a relatively- unused statistical tool that ensures reliability in the PDF while ensuring skill no worse than a climatological forecast or persistence.
UR: http://cfab.eas.gatech.edu/shortterm/home.html
DE: 1821 Floods
DE: 1860 Streamflow
DE: 1872 Time series analysis (3270, 4277, 4475)
DE: 1873 Uncertainty assessment (3275)
DE: 1874 Ungaged basins
SC: Hydrology [H]
MN: 2007 Fall Meeting