Skip to main content
Log in

Multiscale teleconnection analysis of rainfall patterns over Calicut, India using wavelet coherence

  • Published:
Journal of Earth System Science Aims and scope Submit manuscript

Abstract

Understanding the association of climatic oscillations (COs) and meteorological parameters (MPs) with rainfall is of considerable significance in the management of water resources. This study used bivariate wavelet coherence (BWC), partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) formulations for investigating the multiscale coherence of monthly mean rainfall of Calicut, Kerala, India with diverse sets of COs and local MPs. Firstly, the multiscale association between rainfall of 1970 and 2019 with four COs, viz., El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are investigated using different WC formulations. The BWC and PWC analyses detected PDO and NAO as the significant COs influencing rainfall of Calicut, strongly modulated by ENSO and IOD. MWC analysis with 11 combinations of COs revealed the highest coherence for ENSO–IOD and ENSO–PDO–NAO, indicating an equally strong influence of different COs upon the rainfall of Calicut. Further, the teleconnections of rainfall with local MPs, viz., maximum temperature (Tmax), minimum temperature (Tmin), wind speed (U) and evaporation (E) over Calicut are also analyzed. The BWC analysis detected annual periodicity in all the time series, with an additional band at the scale of six months in Tmin series. The coherence strength quantified in terms of average wavelet coherence (AWC) and percentage of significant coherence (PSC) showed that evaporation was the most significant MP (AWC of 0.66 and PSC of 54%) modulated strongly by wind speed. The MWC analysis of rainfall with MPs displayed the highest coherence for TminE and UTminE combinations in the rainfall of Calicut.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

References

  • Adarsh S and Janga Reddy M 2016 Analysing the hydroclimatic teleconnections of summer monsoon rainfall in Kerala, India using multivariate empirical mode decomposition and time dependent intrinsic correlation; IEEE Geosci. Remote Sens. Lett. 13(9) 1221–1225.

    Google Scholar 

  • Adarsh S and Janga Reddy M 2018 Multiscale characterization and prediction of monsoon rainfall in India using Hilbert–Huang transform and time dependent intrinsic correlation analysis; Meteorol. Atmos. Phys. 130 667–688.

    Article  Google Scholar 

  • Adarsh S and Janga Reddy M 2019 Links between global climate teleconnections and Indian monsoon rainfall; In: Climate Change Signals and Response (eds) Venkataraman C, Mishra T, Ghosh S and Karmakar S, Springer, pp. 61–72, https://doi.org/10.1007/978-981-13-0280-0_4.

  • Adarsh S and Janga Reddy M 2021 Multi-Scale Spectral Analysis in Hydrology: From Theory to Practice, CRC Press, ISBN 9780367622015.

  • Araghi A, Mousavi-Baygi M, Adamowski J and Martinez C 2017 Association between three prominent climatic teleconnections and precipitation in Iran using wavelet coherence; Int. J. Climatol. 37(6) 2809–2830.

    Article  Google Scholar 

  • Chang X, Wang B, Yan Y et al. 2019 Characterizing effects of monsoons and climate teleconnections on precipitation in China using wavelet coherence and global coherence; Clim. Dyn. 52 5213–5228, https://doi.org/10.1007/s00382-018-4439-1.

    Article  Google Scholar 

  • Das P and Chanda K 2020 Bayesian Network based modeling of regional rainfall from multiple local meteorological drivers; J. Hydrol. 591 125563.

    Article  Google Scholar 

  • Das P and Chanda K 2022 Bayesian network approach for understanding the role of large-scale and local hydro-meteorological variables as drivers of basin-scale rainfall and streamflow; Stoch. Environ. Res. Risk Assess., https://doi.org/10.1007/s00477-022-02356-2.

    Article  Google Scholar 

  • Das J, Jha S and Goyal M K 2020 On the relationship of climatic and monsoon teleconnections with monthly precipitation over meteorologically homogenous regions in India: Wavelet and global coherence approaches; Atmos. Res. 238, https://doi.org/10.1016/j.atmosres.2020.104889.

  • Ebrahimi A, Rahimi D, Joghataei M et al. 2021 Correlation wavelet analysis for linkage between winter precipitation and three oceanic sources in Iran; Environ. Process., https://doi.org/10.1007/s40710-021-00524-0.

    Article  Google Scholar 

  • Gadgil S, Vinayachandran P N, Francis P A and Gadgil S 2004 Extremes of the Indian summer monsoon rainfall, ENSO and equatorial Indian Ocean oscillation; Geophys. Res. Lett. 31 L12213, https://doi.org/10.1029/2004GL019733.

    Article  Google Scholar 

  • Grinsted A, Moore J C and Jevrejeva S 2004 Application of the cross wavelet transform and wavelet coherence to geophysical time series; Nonlin. Process. Geophys. 11(5) 561–566.

    Article  Google Scholar 

  • Guhathakurta P, Sudeepkumar B L, Menon P, Prasad A K, Sable S T and Advani S C 2020 Observed rainfall variability and changes over Kerala State Met Monograph No.: ESSO/IMD/HS/Rainfall Variability/14(2020)/38, India Meteorological Department, Pune.

  • Hu W and Si B C 2016 Multiple wavelet coherence for untangling scale-specific and localized multivariate relationships in geosciences; Hydrol. Earth Syst. Sci. 20 3183–3191.

    Article  Google Scholar 

  • Hu W and Si B C 2021 Partial wavelet coherency for improved understanding of scale-specific and localized bivariate relationships in geosciences; Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2020-315.

  • Johny K, Pai M L and Adarsh S 2019 Empirical forecasting and Indian Ocean dipole teleconnections of south west monsoon rainfall in Kerala; Meteorol. Atmos. Phys. 131(4) 1055–1065.

    Article  Google Scholar 

  • Kashid S K and Maity R 2012 Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using genetic programming; J. Hydrol. 454–455 26–41.

    Article  Google Scholar 

  • Kumar K K, Rajagopalan B and Cane M A 1999 On the weakening relationship between the Indian Monsoon and ENSO; Science 284 2156–2159.

  • Kurths J, Agarwal A, Shukla R, Marwa N, Rathinasamy M, Caesar L, Krishnan R and Merz B 2019 Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach; Nonlin. Process. Geophys. 26 251–266, https://doi.org/10.5194/npg-26-251-2019.

    Article  Google Scholar 

  • Maity R and Kumar D N 2006a Hydroclimatic association of the monthly summer monsoon rainfall over India with large-scale atmospheric circulations from tropical Pacific Ocean and the Indian Ocean region; Atmos. Sci. Lett. 17(4) 101–107, https://doi.org/10.1002/asl.141.

    Article  Google Scholar 

  • Maity R and Kumar D N 2006b Bayesian dynamic modeling for monthly Indian summer monsoon rainfall using El Nino–Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO); J. Geophys. Res. 111 D07104, https://doi.org/10.1029/2005JD006539.

    Article  Google Scholar 

  • Mihanovic H, Orli M and Pasri Z 2009 Diurnal thermocline oscillations driven by tidal flow around an island in the Middle Adriatic; J. Mar. Syst. 78 S157–S168.

    Article  Google Scholar 

  • Miller A J, Cayan D R, Barnett T P and Graham N E 1994 The 1976–77 climate shift of the Pacific Ocean; Oceanography 7, https://doi.org/10.5670/oceanog.1994.11.

  • Nalley D P 2020 The use of wavelet transform-based methods to analyze variability in hydrological data, multiscale linkages to large-scale climate oscillations, and for hydrological record extension, PhD thesis submitted to McGill University, Montreal, Canada.

  • Nalley D, Adamowski J, Biswas A, Gharabaghi B and Hu W 2019 A multiscale and multivariate analysis of precipitation and streamflow variability in relation to ENSO, NAO and PDO; J. Hydrol. 574 288–307.

    Article  Google Scholar 

  • Ng E and Chan J 2012 Geophysical applications of partial wavelet coherence and multiple wavelet coherence; J. Atmos. Oceanic Tech. 29 1845–1853.

    Article  Google Scholar 

  • Nourani V, Mehr A D and Azad N 2018 Trend analysis of hydroclimatological variables in Urmia lake basin using hybrid wavelet Mann–Kendall and Şen tests; Environ. Earth Sci. 77 207, https://doi.org/10.1007/s12665-018-7390-x.

    Article  Google Scholar 

  • Nourani V, Ghasemzade M, Mehr A D and Sharghi E 2019 Investigating the effect of hydroclimatological variables on Urmia Lake water level using wavelet coherence measure; J. Water Clim. Change 10(1) 13–29, https://doi.org/10.2166/wcc.2018.261.

    Article  Google Scholar 

  • Nourani V, Najafi H, Sharghi E and Roushangar K 2021 Application of Z-numbers to monitor drought using large-scale oceanic–atmospheric parameters; J. Hydrol. 598 126,198, https://doi.org/10.1016/j.jhydrol.2021.126198.

    Article  Google Scholar 

  • Rathinasamy M, Agarwal A, Sivakumar B et al. 2019 Wavelet analysis of precipitation extremes over India and teleconnections to climate indices; Stoch. Environ. Res. Risk Assess. 33 2053–2069, https://doi.org/10.1007/s00477-019-01738-3.

    Article  Google Scholar 

  • Rezaei A and Gurdak J J 2020 Large-scale climate variability controls on climate, vegetation coverage, lake and groundwater storage in the Lake Urmia watershed using SSA and wavelet analysis; Sci. Total Environ. 724 138,273, https://doi.org/10.1016/j.scitotenv.2020.138273.

    Article  Google Scholar 

  • Saji H, Goswami B N, Vinayachandran P N and Yamagata T 1999 A dipole mode in the Tropical Indian Ocean; Nature 401(6751) 360–363.

    Article  Google Scholar 

  • Sang Y-F 2013 A review on the applications of wavelet transform in hydrology time series analysis; Atmos. Res. 122 8–15, https://doi.org/10.1016/j.atmosres.2012.11.003.

    Article  Google Scholar 

  • Song X, Zhang C, Zhang J, Zou X, Mo Y and Tian Y 2020 Potential linkages of precipitation extremes in Beijing–Tianjin–Hebei region, China, with large-scale climate patterns using wavelet-based approaches; Theor. Appl. Climatol. 141 1251–1269.

    Article  Google Scholar 

  • Sreedevi V, Adarsh S and Nourani V 2022 Multiscale coherence analysis of reference evapotranspiration of north western Iran using wavelet transform; J. Water Clim. Change 13(2) 505–521.

    Article  Google Scholar 

  • Su L, Miao C, Duan Q, Lei X and Li H 2019 Multiple-wavelet coherence of world’s large rivers with meteorological factors and ocean signals; J. Geophys. Res.: Atmos. 124 4932–4954.

    Article  Google Scholar 

  • Sudheer K P, Murty B, Narasimhan B, Thomas J, Bindhu V M, Vema V and Kurian C 2018 Role of dams on the floods of August 2018 in Periyar River Basin, Kerala; Curr. Sci. 116(5) 780–794.

    Google Scholar 

  • Tan X, Gan T Y and Shao D 2016 Wavelet analysis of precipitation extremes over Canadian ecoregions and teleconnections to large-scale climate anomalies; J. Geophys. Res. Atmos. 121 14,469–14,486, https://doi.org/10.1002/2016JD025533.

    Article  Google Scholar 

  • Torrence G and Compo G P 1998 A practical guide to wavelet analysis; Bull. Am. Meteorol. Soc. 79(1) 61–78, https://doi.org/10.1175/1520-0477.

    Article  Google Scholar 

Download references

Acknowledgement

The second author gratefully acknowledges the National Institute of Technology Calicut for providing the facility for the Summer Internship programme to perform this research work.

Author information

Authors and Affiliations

Authors

Contributions

AS and AKR conceptualized the problem. AKR provided the data. AS and FS developed the codes. FS implemented the work and prepared the draft version of the manuscript. AS and AKR revised the manuscript, supervised the work and addressed the review comments.

Corresponding author

Correspondence to S Adarsh.

Additional information

Communicated by Parthasarathi Mukhopadhyay

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Adarsh, S., Fathima, S. & Arunkumar, R. Multiscale teleconnection analysis of rainfall patterns over Calicut, India using wavelet coherence. J Earth Syst Sci 133, 20 (2024). https://doi.org/10.1007/s12040-023-02228-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12040-023-02228-5

Keywords

Navigation