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Analysis of spatio-temporal variability in observed rainfall over Karnataka using different data analytical techniques

Abstract

Karnataka, a state in south India with nearly 80% of the cultivated land under rainfed farming, is very much dependent on rainfall for agricultural productivity. The spatio-temporal variability in observed rainfall over Karnataka is investigated using various data analytical techniques such as parametric and non-parametric methods, rotated empirical orthogonal function (REOF), clustering and spectral analysis. The observed data used for studying rainfall variability is the daily taluk-wise telemetric rain gauge data for a period of 1960–2016. A similar pattern in trend is observed in annual and south-west monsoon (SWM) rainfall over Karnataka such that taluks in the western and northern parts showed a decreasing trend, whereas the south interior part showed an increasing trend. A significant increasing trend in rainfall was found during pre-monsoon seasons whereas the northeast monsoon (NEM) rainfall showed a decreasing trend. The REOF analysis also indicated an upward (downward) trend in SWM and annual over the northern (southern) Karnataka and a weakening trend in the NEM rainfall. Using the hierarchical clustering method, six homogeneous rainfall clusters were identified over Karnataka based on distribution and variability of rainfall. The spectral analysis over different clusters showed significant oscillations in the annual and SWM rainfall in the 1970s and recent decades except the Western Ghat region where oscillations were much weaker during recent decades. The pre-monsoon and NEM rainfall also showed strong variability with a periodicity of 2–4 years in recent decades. The findings of this study can have implications while designing water resource management strategies across various sectors in Karnataka.

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References

  • Banerjee A, Dimri A P and Kumar K 2020 Rainfall over the Himalayan foot-hill region: Present and future; J. Earth Syst. Sci. 129(11) 1–16.

    Google Scholar 

  • Beecham S and Chowdhury R K 2010 Temporal characteristics and variability of point rainfall: A statistical and wavelet analysis; Int. J. Climatol. 30(3) 458–473.

    Google Scholar 

  • Boyaj A, Dasari H P, Hoteit I and Ashok K 2020 Increasing heavy rainfall events in south India due to changing land use and land cover; Quart. J. Roy. Meteorol. Soc. 146 3064–3085.

    Google Scholar 

  • Devi U, Shekhar M S, Singh G P and Dash S K 2020 Statistical method of forecasting of seasonal precipitation over the northwest Himalayas: North Atlantic oscillation as precursor; Pure Appl. Geophys. 177 3501–3511.

    Google Scholar 

  • Gadgil S and Narayana Iyengar R 1980 Cluster analysis of rainfall stations of the Indian peninsula; Quart. J. Roy. Meteorol. Soc. 106(450) 873–886.

    Google Scholar 

  • Goswami P, Rakesh V, Patra G K and Prakash V S 2012 Real-time quantitative rainfall forecasts at hobli-level over Karnataka: Evaluation for the winter monsoon 2010; Curr. Sci. 102(10) 1426–1433.

    Google Scholar 

  • Guhathakurta P, Sreejith O P and Menon P A 2011 Impact of climate change on extreme rainfall events and flood risk in India; J. Earth Syst. Sci. 120(3) 359.

    Google Scholar 

  • Hamed K H and Rao A R 1998 A modified Mann–Kendall trend test for autocorrelated data; J. Hydrol. 204(1–4) 182–196.

    Google Scholar 

  • Hannachi A, Jolliffe I T, Stephenson D B and Trendafilov N 2006 In search of simple structures in climate: Simplifying EOFs; Int. J. Climatol. 26(1) 7–28.

    Google Scholar 

  • Huth R and Pokorna L 2004 Parametric versus non-parametric estimates of climatic trends; Theor. Appl. Climatol. 77(1–2) 107–112.

    Google Scholar 

  • Jain S K and Kumar V 2012 Trend analysis of rainfall and temperature data for India; Curr. Sci. 102(1) 37–49.

    Google Scholar 

  • Jayasree V and Venkatesh B 2015 Analysis of rainfall in assessing the drought in semi-arid region of Karnataka state, India; Water Resour. Manag. 29(15) 5613–5630.

    Google Scholar 

  • Joshi M K and Pandey A C 2011 Trend and spectral analysis of rainfall over India during 1901–2000; J. Geophys. Res. Atmos. 116 D06104, https://doi.org/10.1029/2010JD014966.

    Article  Google Scholar 

  • Kampata J M, Parida B P and Moalafhi D B 2008 Trend analysis of rainfall in the headstreams of the Zambezi river basin in Zambia; Phys. Chem. Earth Parts A/B/C 33(8–13) 621–625.

    Google Scholar 

  • Kantharao B and Rakesh V 2018 Observational evidence for the relationship between spring soil moisture and June rainfall over the Indian region; Theor. Appl. Climatol. 132(3–4) 835–849.

    Google Scholar 

  • Kendall M G 1975 Rank correlation methods (4th edn); Charles Griffin, London.

    Google Scholar 

  • Kocsis T, Kovács-Székely I and Anda A 2020 Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely, western Hungary; Theor. Appl. Climatol. 139(3–4) 849–859.

    Google Scholar 

  • Kulkarni M A, Singh A and Mohanty U C 2012 Effect of spatial correlation on regional trends in rain events over India; Theor. Appl. Climatol. 109(3–4) 497–505.

    Google Scholar 

  • Kumar V, Jain S K and Singh Y 2010 Analysis of long-term rainfall trends in India; Hydrol. Sci. J. 55(4) 484–496.

    Google Scholar 

  • Lau K M and Weng H 1995 Climate signal detection using wavelet transform: How to make a time series sing; Bull. Am. Meteorol. Soc. 76(12) 2391–2402.

    Google Scholar 

  • Lian T and Chen D 2012 An evaluation of rotated EOF analysis and its application to tropical Pacific SST variability; J. Clim. 25(15) 5361–5373.

    Google Scholar 

  • Machiwal D, Gupta A, Jha M K and Kamble T 2019a Analysis of trend in temperature and rainfall time series of an Indian arid region: Comparative evaluation of salient techniques; Theor. Appl. Climatol. 136(1–2) 301–320.

    Google Scholar 

  • Machiwal D, Kumar S, Meena H M, Santra P, Singh R K and Singh D V 2019b Clustering of rainfall stations and distinguishing influential factors using PCA and HCA techniques over the western dry region of India; Meteorol. Appl. 26(2) 300–311.

    Google Scholar 

  • Malik A and Kumar A 2020 Spatio-temporal trend analysis of rainfall using parametric and non-parametric tests: Case study in Uttarakhand, India; Theor. Appl. Climatol. 140 183–207.

    Google Scholar 

  • Mandal K G, Padhi J, Kumar A, Ghosh S, Panda D K, Mohanty R K and Raychaudhuri M 2015 Analyses of rainfall using probability distribution and Markov chain models for crop planning in Daspalla region in Odisha, India; Theor. Appl. Climatol. 121(3–4) 517–528.

    Google Scholar 

  • Mann H B 1945 Nonparametric tests against trend; Econ. Soc. 13 245–259.

    Google Scholar 

  • Mechiche-Alami A and Abdi A M 2020 Agricultural productivity in relation to climate and cropland management in West Africa; Sci. Rep. 10(1) 1–10.

    Google Scholar 

  • Mishra V, Smoliak B V, Lettenmaier D P and Wallace J M 2012 A prominent pattern of year-to-year variability in Indian summer monsoon rainfall; Proc. Nat. Acad. Sci. 109(19) 7213–7217.

    Google Scholar 

  • Mohapatra G, Rakesh V, Purwar S and Dimri A P 2021 Spatio-temporal rainfall variability over different meteorological subdivisions in India: Analysis using different machine learning techniques; Theor. Appl. Climatol. 145(1) 673–686.

    Google Scholar 

  • Panda A and Sahu N 2019 Trend analysis of seasonal rainfall and temperature pattern in Kalahandi, Bolangir and Koraput districts of Odisha, India; Atmos. Sci. Lett. 20(10) e932.

    Google Scholar 

  • Pingale S M, Khare D, Jat M K and Adamowski J 2014 Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centers of the arid and semi-arid state of Rajasthan, India; Atmos. Res. 138 73–90.

    Google Scholar 

  • Prabhakar A K, Singh K K, Lohani A K and Chandniha S K 2019 Assessment of regional-level long-term gridded rainfall variability over the Odisha state of India; Appl. Water Sci. 9(4) 93.

    Google Scholar 

  • Prakash S, Mahesh C, Sathiyamoorthy V and Gairola R M 2013 Increasing trend of northeast monsoon rainfall over the equatorial Indian Ocean and peninsular India; Theor. Appl. Climatol. 112(1–2) 185–191.

    Google Scholar 

  • Prasanna V 2014 Impact of monsoon rainfall on the total foodgrain yield over India; J. Earth Syst. Sci. 123(5) 1129–1145.

    Google Scholar 

  • Praveen B, Talukdar S, Mahato S, Mondal J, Sharma P, Islam A R M T and Rahman A 2020 Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches; Sci. Rep. 10(1) 1–21.

    Google Scholar 

  • Rajegowda M B, Babu B T R, Janardhanagowda N A and Muralidhara K S 2009 Impact of climate change on agriculture in Karnataka; J. Agrometeorol. 11(2) 125–131.

    Google Scholar 

  • Rakesh V and Goswami P 2015a Impact of data assimilation on high resolution rainfall forecasts: A spatial, seasonal, and category analysis; J. Geophys. Res. Atmos. 120 359–377.

    Google Scholar 

  • Rakesh V and Goswami P 2016 An evaluation strategy of skill of high resolution rainfall forecast for specific agricultural applications; Meteorol. Appl. 23(3) 529–540.

    Google Scholar 

  • Rakesh V, Goswami P and Prakash V S 2015b Evaluation of high-resolution rainfall forecasts over Karnataka for the 2011 southwest and northeast monsoon seasons; Meteorol. Appl. 22(1) 37–47.

    Google Scholar 

  • Rathinasamy M, Agarwal A, Sivakumar B, Marwan N and Kurths J 2019 Wavelet analysis of precipitation extremes over India and teleconnections to climate indices; Stoch. Env. Res. Risk Assess. 33(11–12) 2053–2069.

    Google Scholar 

  • Reddy S G and Prabhu C N 2016 Natural disaster monitoring system – Karnataka model; J. Geol. Soc. India 5 1–10.

    Google Scholar 

  • Richman M B 1986 Rotation of principal components; J. Clim. 6(3) 293–335.

    Google Scholar 

  • Saikranthi K, Rao T N, Rajeevan M and Bhaskara Rao S V 2013 Identification and validation of homogeneous rainfall zones in India using correlation analysis; J. Hydrometeorol. 14(1) 304–317.

    Google Scholar 

  • Sandeep S, Ajayamohan R S, Boos W R, Sabin T P and Praveen V 2018 Decline and poleward shift in Indian summer monsoon synoptic activity in a warming climate; Proc. Natl. Acad. Sci. 115(11) 2681–2686.

    Google Scholar 

  • Sears-Collins A L, Schultz D M and Johns R H 2006 Spatial and temporal variability of nonfreezing drizzle in the United States and Canada; J. Clim. 19(15) 3629–3639.

    Google Scholar 

  • Sen P K 1968 Estimates of the regression coefficient based on Kendall’s tau; J. Am. Stat. Assoc. 63(324) 1379–1389.

    Google Scholar 

  • Sengupta A and Nigam S 2019 The northeast winter monsoon over the Indian subcontinent and Southeast Asia: Evolution, interannual variability, and model simulations; J. Clim. 32(1) 231–249.

    Google Scholar 

  • Singh K, Panda J and Kant S 2020 A study on variability in rainfall over India contributed by cyclonic disturbances in warming climate scenario; Int. J. Climatol. 40(6) 3208–3221.

    Google Scholar 

  • Sujith K, Saha S K, Pokhrel S, Hazra A and Chaudhari H S 2017 The dominant modes of recycled monsoon rainfall over India; J. Hydrometeorol. 18(10) 2647–2657.

    Google Scholar 

  • Tawde S A and Singh C 2015 Investigation of orographic features influencing spatial distribution of rainfall over the Western Ghats of India using satellite data; Int. J. Climatol. 35(9) 2280–2293.

    Google Scholar 

  • Upadhaya A and Singh S R 1998 Estimation of consecutive days maximum rainfall by various methods and their comparison; Indian J. Soil Conserv. 26(2) 1993–2001.

    Google Scholar 

  • Yadav R K, Kumar K R and Rajeevan M 2012 Characteristic features of winter precipitation and its variability over northwest India; J. Earth Syst. Sci. 121(3) 611–623.

    Google Scholar 

  • Yi H and Shu H 2012 The improvement of the Morlet wavelet for multi-period analysis of climate data; C. R. Geosci. 344(10) 483–497.

    Google Scholar 

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Acknowledgements

The observed rainfall data are obtained from Karnataka State Natural Disaster Monitoring Center (KSNDMC) and the authors acknowledge the effort taken by KSNDMC in setting up the observation network and their support. The support from CSIR YSA research grant is acknowledged by V Rakesh. The study is carried out at CSIR 4PI and institutional support is acknowledged.

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AS contributed to data analysis and plotting for the manuscript. VR contributed to conceptualise, design, data analysis and drafting the manuscript. Ms SP contributed to analysing data and generating figures. SMG contributed to observation data analysis and quality control. GNM contributed to manuscript formatting and figure generation.

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Correspondence to V Rakesh.

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Communicated by Parathasarathi Mukhopadhyay

Supplementary material pertaining to this article is available on the Journal of Earth System Science website (http://www.ias.ac.in/Journals/Journal_of_Earth_System_Science).

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Samanth, A., Rakesh, V., Purwar, S. et al. Analysis of spatio-temporal variability in observed rainfall over Karnataka using different data analytical techniques. J Earth Syst Sci 131, 66 (2022). https://doi.org/10.1007/s12040-022-01810-7

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  • DOI: https://doi.org/10.1007/s12040-022-01810-7

Keywords

  • Cluster analysis
  • Sen’s slope
  • M–K test
  • spectral analysis
  • rotated empirical orthogonal function
  • wavelet analysis
  • rainfall variability