El-Nino/Southern Oscillation (ENSO): Recent Evolution and Possibilities for Long Range Flow Forecasting in the Brahmaputra-Jamuna River

The El Nino-Southern Oscillation is the dominant pattern of short-term climate variation, and is therefore of great importance in climate studies. Some recent studies showed the teleconnection between stream flow and the El-Nino Southern Oscillation (ENSO) of the equatorial Pacific Ocean. This paper presents an overview of the relationship between ENSO and stream flow in the Brahmaputra-Jamuna and the potential for wet season flow forecasting. This seasonal forecast of stream flow is very invaluable to the management of land and water resources, particularly in Bangladesh to improve the predictability of severe flooding. Over the years, large investments have been made to build physical infrastructure for flood protection, but it has been proved that it is not feasible, both economically and technically, to adopt solely structural mitigation approach. The choice of non-structural measures in this country focused mainly on flood forecasting because many of the nonstructural measures including flood plain zoning, compulsory acquisition of flood prone land, relocation etc have also been proved inappropriate for Bangladesh. The aim of this research is to find out an effective and long-lead flow forecasting method with lead time greater than hydrological time scale, using El Nino-Southern Oscillation index. Some studies indicate that SST can be predicted one to two years in advance using several ocean/ coupled ocean atmosphere models, therefore the ability to predict flow patterns in rivers will be highly enhanced if a strong relationship between river discharge and ENSO exists, and is quantified. With this view, to assess the strength of teleconnection between river flow and ENSO, at first correlation analyses between ENSO indices of any year and wet season flow of that year have been done. Here sea surface temperature (SST) has been used as ENSO index. This correlation analysis demonstrates a noteworthy relationship between natural variability of average flow of the months July-August-September (JAS) of the Brahmaputra-Jamuna River with SST of the corresponding months. Then discriminant prediction approach, also known as “Categoric Prediction” has been used here for the assessment of long range flood forecasting possibilities. This approach will be able to forecast the category of flow (high, average or low) using the category of predictor (predicted SST) at a sufficient lead time. In order to judge the forecast skill, a synoptic parameter “Forecasting Index” has also been used. This discriminant approach will improve the forecasting lead-time while the hydrologic forecast through rainfall-runoff modeling could provide a lead time on the order of the basin response time, which is several days or so. As the Ganges–Brahmaputra river basin is one of the most populous river basins of the world and is occupied by some developing countries of the world like Bangladesh, any reduction in the uncertainty about the flood in the Brahmaputra-Jamuna River would contribute a lot to the improvement in flow forecasting as well as to the economic development of the country.