Analyzing the streamflow teleconnections of greater Pampa basin, Kerala, India using wavelet coherence

Climate change is one of the major global threats which affects the streamflow variability. In order to disentangle the relationships between climatic oscillations (COs) and streamflows (SF), this study presents a framework for the identification of main climatic factors integrating Bivariate wavelet coherence (BWC), Multiple wavelet coherence (MWC), and Partial wavelet coherence (PWC) approaches. The application of this method is demonstrated by analysing the monthly SF-CO teleconnections of four stations belonging to the rivers of Greater Pampa region of Kerala, which was severely affected by the 2018 Kerala floods. To investigate the impact of these large-scale climatic patterns on the SF of this region, COs, specifically El Niño Southern Oscillation, Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), and North Atlantic Oscillation (NAO), are taken into consideration. The relationships between different SF-COs are statistically quantified with the help of Average Wavelet Coherence (AWC) and the Percentage of Significant Coherence (PoSC). The BWC analysis showed that the IOD is the dominant predictor for SF1 (AWC = 0.46, PoSC = 26.05%) and SF2 (AWC = 0.5, PoSC = 30.03%) stations while NAO is the dominant predictor for SF3 (AWC = 0.43, PoSC = 18.25%) and SF4 (AWC = 0.48, PoSC = 24.83%) stations. Addition of NAO to the most significant combination of SF-PDO-IOD from the two-factor analysis produced the highest coherence values for all the stations. The PWC analysis indicated a drastic reduction in coherence values with respect to the BWC analysis, indicating a strong interrelationship between different COs and SF.