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The El Niño southern oscillation and long-range forecasting of flows in the Ganges

✍ Scribed by Douglas W. Whitaker; Saleh A. Wasimi; Shafiqul Islam


Publisher
John Wiley and Sons
Year
2001
Tongue
English
Weight
200 KB
Volume
21
Category
Article
ISSN
0899-8418

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✦ Synopsis


Abstract

Several recent studies have shown that the El Niño–Southern Oscillation (ENSO) index has a significant influence on various climatic and hydrologic signals across the globe. This study attempts to identify the nature and strength of possible teleconnections between the Ganges River flow and ENSO, and to develop a model which can capture, at least in part, the natural variability of flow, and provide a large forecasting lead‐time. The motivation came from the fact that, in the past, hydrologic forecasts of the basin through rainfall‐runoff modelling could provide a lead‐time on the order of the basin response time, which is several days or so. Such a short forecasting lead‐time is not adequate to hedge against extreme events (flood or drought) in large river basins. This is, perhaps, the first attempt to relate flows in the Ganges with ENSO.

Our analysis suggests that a significant relationship exists between the natural variability of the Ganges annual flow and ENSO index. Through further investigation, we show that the rate of change of ENSO index is also statistically related to the Ganges flow. A statistical model that combines all these indicators to forecast annual flow in the Ganges is proposed. This model uses current flow data, predicted ENSO data and its gradient to forecast flow in the Ganges with a forecasting lead‐time of 1 year. The model also provides a quantitative measure of forecasting uncertainty. A key advantage of this model is that it does not require rainfall and stream flow information from upstream areas and countries. We have used 45 years of data for model development and calibration, and 15 years of data for validation. It is encouraging to note that all four of the validation forecasts during the El Niño and La Niña events are within the 95% confidence intervals. These results demonstrate the strength of the proposed approach and suggest further exploration of this long‐range forecasting methodology for other major rivers in the world. Copyright © 2001 Royal Meteorological Society


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