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Seasonal Flood Potential- Bangladesh -a Scheme for an Advance Warning and Response Plan for Hazard Mitigation

Md. Rashed CHOWDHURY
Pacific ENSO Applications Center, University of Hawaii, USA (rashed@hawaii.edu)

M. Neil WARD
International Research Institute for Climate and Society, Columbia University, USA.

Abstract/ Executive Summary:

The primary objectives of this paper was to study the variability and predictability of seasonal flooding in Bangladesh, as revealed by large-scale predictors of the climate across the watersheds. To explore the source of predictability, accessible Bangladesh hydrological indicators are related to large-scale oceanic variability like the El Niño Southern Oscillation (ENSO) climate cycle, and to the precipitation patterns of the General Circulation Models (GCM). Findings have demonstrated that the seasonal flood prediction in Bangladesh is possible from (i) the basin-wide rainfall run-off relation, (ii) the year-to-year ENSO climate cycle, (iii) the unusually warm or cold sea-surface temperature (SST) in parts of the tropics, and (iv) the precipitation as predicted by the GCM. This predictability can be enhanced with the information from downstream stream-flows and upstream rainfall conditions. Finally, with experience from other regions, this study concluded that the success of seasonal forecasts, in contrast to short-term forecast, depends on consensus among the participating institutions. A general consensus in producing seasonal forecasts can thus be achieved by enhancing the existing ‘Flood Forecasting and Warning Response System’ (FFWRS). Therefore, in conclusion, the need for a FFWRS had been emphasized for a comprehensive and participatory approach to seasonal flood hazard management in Bangladesh.

Keywords: Flood, ENSO, GCM, Seasonal Prediction, Bangladesh.

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Start Date:

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Program Area: Risk Management in Agriculture

Non-commodity: Flood

Objectives:

This study was conducted with the aimed at providing a basis for the development of a seasonal outlook for the extent of flooding in Bangladesh.

Methodology:

Data accumulation was a major task in this study. It was possible to get some readily available information (i.e., SST1 and atmospheric circulation 2 data), while access to some other local information was extremely difficult (i.e., rainfall and discharge data). In that case, it had to explore Hulme3 Global Precipitation data. This was a huge task in this research and comprehensive discussions on these hydro-climatic data (flood area, rainfall, and stream flow) were reported in Chowdhury, 2003; Chowdhury and Ward, 2004; and Chowdhury and Ward, 2006. It is important to note here that in those studies the 5-strong El Niño and La Niña years identified were 1951, 1958, 1972, 1982, and 1997 (El Niño), and 1964, 1973, 1975, 1988, and 1998 (La Niña). Based on BWDB source, the flood-affected area (FAA) data were primarily used to identify the extreme events like the wettest and driest years. The 5-driest (drought) and wettest (flooded) years identified from this source were 1982, 1989, 1990, 1992, and 1994 (driest), and 1955, 1974, 1987, 1988, and 1998 (wettest). However, to ensure robustness of the results, other hydro-climate data like the rainfall and stream-flows were also examined.

Source of Information: International Conference on Water & Flood Management (ICWFM-2007), 12 - 14 March 2007, Dhaka, Bangladesh

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Achievement/Findings:

Major findings from the above four approaches for seasonal flood predictions are summarized:
(i) The rainfall-run-off relation in the greater GBM basins is quantifiable and month-to-seasonal (1-3 months in advance) stream-flow forecast in Bangladesh is possible by simply employing correlation with the rainfall data from upper India on a real time and continuous basis.
(ii) Bangladesh climate has particularly a strong relation when SOI extremes indicate negative SOI (i.e., El Niño years) for dry and positive SOI (i.e., La Niña years) for wet. In the case of a moderate anomaly in the SOI, the index-climate relation appeared to be contradictory. Although, in particular, Bangladesh has been found to be wet in some of the moderate El Niño years (1987), but any conclusive statement is difficult at this stage; more research is needed.

(iii) There is skill to predict FAA from SST – prediction skill increases when rainfall and stream-flows from Bangladesh are added to the model. The maximum variance that could be explained in the statistical model is 49.0 percent with leading SST PCs.
(iv) ECHAM4 GCM prediction fields also provide opportunities to predict FAA in Bangladesh. As before, here prediction skill also increases when rainfall and stream-flows are added to the model. The maximum variance that could be explained here is 40.9 percent with leading ECHAM PCs.

Knowledge Management: Report/Proceedings