Basin-scale stream-flow forecasting using the information of large-scale atmospheric circulation phenomena

Abstract

It is well recognized that the time series of hydrologic variables, such as rainfall and streamflow are significantly influenced by various large-scale atmospheric circulation patterns. The influence of El Niño-southern oscillation (ENSO) on hydrologic variables, through hydroclimatic teleconnection, is recognized throughout the world. Indian summer monsoon rainfall (ISMR) has been proved to be significantly influenced by ENSO. Recently, it was established that the relationship between ISMR and ENSO is modulated by the influence of atmospheric circulation patterns over the Indian Ocean region. The influences of Indian Ocean dipole (IOD) mode and equatorial Indian Ocean oscillation (EQUINOO) on ISMR have been established in recent research. Thus, for the Indian subcontinent, hydrologic time series are significantly influenced by ENSO along with EQUINOO. Though the influence of these large-scale atmospheric circulations on large-scale rainfall patterns was investigated, their influence on basin-scale stream-flow is yet to be investigated. In this paper, information of ENSO from the tropical Pacific Ocean and EQUINOO from the tropical Indian Ocean is used in terms of their corresponding indices for stream-flow forecasting of the Mahanadi River in the state of Orissa, India. To model the complex non-linear relationship between basin-scale stream-flow and such large-scale atmospheric circulation information, artificial neural network (ANN) methodology has been opted for the present study. Efficient optimization of ANN architecture is obtained by using an evolutionary optimizer based on a genetic algorithm. This study proves that use of such large-scale atmospheric circulation information potentially improves the performance of monthly basin-scale stream-flow prediction which, in turn, helps in better management of water resources.