Time–frequency characterization of sub-divisional scale seasonal rainfall in India using the Hilbert–Huang transform

Abstract

Time-frequency characterization is useful in understanding the nonlinear and non-stationary signals of the hydro-climatic time series. The traditional Fourier transform, and wavelet transform approaches have certain limitations in analyzing non-linear and non-stationary hydro-climatic series. This paper presents an effective approach based on the Hilbert-Huang transform to investigate time-frequency characteristics, and the changing patterns of sub-divisional rainfall series in India, and explored the possible association of monsoon seasonal rainfall with different global climate oscillations. The proposed approach integrates the complete ensemble empirical mode decomposition with adaptive noise algorithm and normalized Hilbert transform method for analyzing the spectral characteristics of two principal seasonal rainfall series over four meteorological subdivisions namely Assam-Meghalaya, Kerala, Orissa and Telangana subdivisions in India. The Hilbert spectral analysis revealed the dynamic nature of dominant time scales for two principal seasonal rainfall time series. From the trend analysis of instantaneous amplitudes of multiscale components called intrinsic mode functions (IMFs), it is found that both intra and inter decadal modes are responsible for the changes in seasonal rainfall series of different subdivisions and significant changes are noticed in the amplitudes of inter decadal modes of two seasonal rainfalls in the four subdivisions since 1970s. Further, the study investigated the links between monsoon rainfall with the global climate oscillations such as Quasi Bienniel Oscillation (QBO), El Nino Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multidecadal Oscillation (AMO) etc. The study noticed that the multiscale components of rainfall series IMF1, IMF2, IMF3, IMF4 and IMF5 have similar periodic structure of QBO, ENSO, SN, tidal forcing and AMO respectively. As per the seasonal rainfall patterns is concerned, the results of the study indicated that for Assam-Meghalaya subdivision, there is a likelihood of extreme rare events at *0.2 cycles per year, and both monsoon and pre-monsoon rainfall series have decreasing trends; for Kerala subdivision, extreme events can be expected during monsoon season with shorter periodicity (*2.5 years), and monsoon rainfall has statistically significant decreasing trend and post-monsoon rainfall has a statistically significant increasing trend; and for Orissa subdivision, there are chances of extremes rainfall events in monsoon season and a relatively stable rainfall pattern during post-monsoon period, but both monsoon and post-monsoon rainfall series showed an overall decreasing trend; for Telangana subdivision, there is a likelihood of extreme events during monsoon season with a periodicity of *4 years, but both monsoon and post-monsoon rainfall series showed increasing trends. The results of correlation analysis of IMF components of monsoon rainfall and five climate indices indicated that the association is expressed well only for low frequency modes wit...

A Comparison of Time‐Frequency Signal Processing Methods for Identifying Non‐Perennial Streamflow Events From Streambed Surface Temperature Time Series

“…Applications of the CEEMDAN method in hydrology include by Antico et al. (2014), Adarsh and Reddy (2016), Reddy and Adarsh (2016), and Liu et al. (2018).…”

“…Reddy and Adarsh (2016) used the CEEMDAN approach to characterize monthly precipitation data from four meteorological subdivisions in India recorded over 142 years, 1871–2013. The CEEMDAN method was also used to characterize four large‐scale climatic indices (the Atlantic Multidecadal Oscillation [AMO], the El Nino Southern Oscillation [ENSO], the Equatorial Indian Ocean Oscillation, and the Quasi‐Biennial Oscillation [QBO]) and one solar index (the sunspot cycle).…”

“…Applications of the CEEMDAN method in hydrology include by Antico et al (2014), Adarsh and Reddy ( 2016), Reddy and Adarsh (2016), and Liu et al (2018). Antico et al (2014) used the CEEMDAN method to decompose and analyze the monthly mean discharge of the Parana River, South America.…”