Prediction of streamflow in Chalakudy River Basin, Kerala, by integrating teleconnection patterns of large-scale atmospheric circulations

Abstract

ABSTRACT In this study, the relationship between large-scale climatic drivers and streamflow of the Chalakudy River Basin, Kerala, was analysed using methods such as bivariate wavelet coherence (BWC), multiple wavelet coherence (MWC), and partial wavelet coherence (PWC) analysis. The four prominent global climate indices chosen are the Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO), along with other local climate drivers. The BWC analysis showed that streamflow and rainfall had a very strong in-phase relationship, whereas the maximum temperature and average temperature showed an anti-phase relationship. In the case of global climate drivers, ENSO has a significant impact on the streamflow of the Chalakudy River Basin. The average wavelet coherence (AWC), which quantifies the teleconnections, confirms the observation with a high coherency of 0.75 between streamflow and rainfall and 0.51 for streamflow and ENSO. Streamflow prediction models were developed using random forest (RF) and artificial neural network (ANN) techniques by considering the influence of significant global and local climatic drivers. It was observed that the RF model performed slightly better than the ANN model, with R = 0.875, NSE = 0.766, RMSE = 23.468, and RSR = 0.524.