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

Abstract

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.