Natural Hazards

, Volume 84, Issue 3, pp 1831–1847 | Cite as

A fuzzy c-means approach regionalization for analysis of meteorological drought homogeneous regions in western India

Original Paper


Drought is a frequently occurring natural hazard in many parts of the world and affects economy, environment and human lives. In India, the western states are severely affected by droughts. Global warming and climate change is causing more frequent and intense droughts in the region. In the present study, meteorological drought is studied using standardized precipitation index for four different timescales of 1, 3, 6 and 12 months. Drought homogeneous regions are identified over 81 stations in three western states of India using monthly precipitation data from 1901 to 2002. Based on fuzzy c-means clustering algorithm and five cluster validity indices, the optimal number of clusters obtained for 1-, 3-, 6- and 12-month timescales are 4, 5, 3 and 4, respectively. Homogeneity of the clusters is ensured using two L-moment-based homogeneity tests (H-Test). Clusters obtained for different timescales are compared and significant variation in cluster pattern is observed for different timescales. The identified regions are anticipated to assist policy maker in effective planning and management of water resources during drought.


Regionalization Drought Fuzzy Homogeneity 


  1. American Meteorological Society (AMS) (2004) Statement on meteorological drought. Bull Am Meteorol Soc 85:771–773Google Scholar
  2. Bezdek JC (1974a) Numerical taxonomy with fuzzy sets. J Math Biol 1(1):57–71CrossRefGoogle Scholar
  3. Bezdek JC (1974b) Cluster validity with fuzzy sets. J Cybern 3(3):58–72CrossRefGoogle Scholar
  4. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkCrossRefGoogle Scholar
  5. Blain GC (2011) Standardized precipitation index based on Pearson type III distribution. Rev Bras Meteorol 26(2):167–180CrossRefGoogle Scholar
  6. Clausen B, Pearson CP (1995) Regional frequency analysis of annual maximum streamflow drought. J Hydrol 173:111–130CrossRefGoogle Scholar
  7. Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3(3):32–57CrossRefGoogle Scholar
  8. Fukuyama Y, Sugeno M (1989) A new method of choosing the number of clusters for the fuzzy c-means method. In: Proceedings of Fifth Fuzzy Systems Symposium, vol 247, pp 247–250Google Scholar
  9. Goyal MK, Gupta V (2014) Identification of homogeneous rainfall regimes in Northeast Region of India using fuzzy cluster analysis. Water Resour Manage 28(13):4491–4511CrossRefGoogle Scholar
  10. Goyal MK, Gupta V (2016) Hydrological Drought: Water Surface and Duration Curve Indices, Chapter 04 in Handbook of Drought and Water Scarcity (HDWS), Francis and Taylor, CRC Group (Accepted)Google Scholar
  11. Goyal MK, Ojha CSP (2012) Downscaling of precipitation on a lake basin: evaluation of rule and decision tree induction algorithms. Hydrol Res 43(3):215–230CrossRefGoogle Scholar
  12. Goyal MK, Sharma A (2016) Regionalization of Drought Prediction, Chapter 15 in Handbook of Drought and Water Scarcity (HDWS), Francis and Taylor, CRC Group (Accepted)Google Scholar
  13. Guttman NB (1999) Accepting the standardized precipitation index: a calculation algorithm. J Am Water Resour As 35(2):311–322CrossRefGoogle Scholar
  14. Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J IntellInfSyst 17:107–145Google Scholar
  15. Hayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteorol Soc 80(3):429–438CrossRefGoogle Scholar
  16. Hisdal H, Tallaksen LM (2003) Estimation of regional meteorological and hydrological drought characteristics: a case study for Denmark. J Hydrol 281(3):230–247CrossRefGoogle Scholar
  17. Hosking JRM, Wallis JR (1993) Some statistics useful in regional frequency analysis. Water Resour Res 29(2):271–281CrossRefGoogle Scholar
  18. Hosking JRM, Wallis JR (1997) Regional frequency analysis: an approach based on L-moments. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  19. Jain SK, Keshri R, Goswami A, Sarkar A (2010) Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India. Nat Hazards 54(3):643–656CrossRefGoogle Scholar
  20. Keyantash J, Dracup JA (2002) The quantification of drought: an evaluation of drought indices. Bull Am Meteorol Soc 83(8):1167–1180CrossRefGoogle Scholar
  21. Kwon SH (1998) Cluster validity index for fuzzy clustering. Electron Lett 34(22):2176–2177CrossRefGoogle Scholar
  22. Lettenmaier DP, McCabe G, Stakhiv EZ (1996) Global climate change: effects on hydrologic cycle. In: Mays LW (ed) Water Resources Handbook, Part V. McGraw-Hill, New YorkGoogle Scholar
  23. Liu X, Wang S, Zhou Y, Wang F, Li W, Liu W (2015) Regionalization and spatiotemporal variation of drought in China based on standardized precipitation evapotranspiration index (1961–2013). Adv Meteorol 2015(950262):1–18Google Scholar
  24. Lloyd-Hughes B, Saunders MA (2002) A drought climatology for Europe. Int J Climatol 22(13):1571–1592CrossRefGoogle Scholar
  25. Madsen H, Mikkelsen PS, Rosbjerg D, Harremoës P (1998) Estimation of regional intensity-duration-frequency curves for extreme precipitation. Water Sci Technol 37(11):29–36CrossRefGoogle Scholar
  26. McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to timescales. In: Proceedings of the 8th conference on applied climatology, vol 17, No 22. American Meteorological Society, Boston, pp 179–183Google Scholar
  27. Mirakbari M, Ganji A, Fallah SR (2010) Regional bivariate frequency analysis of meteorological droughts. J Hydrol Eng 15(12):985–1000CrossRefGoogle Scholar
  28. Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Environ Res Risk Assess 19(5):326–339CrossRefGoogle Scholar
  29. Mishra AK, Singh VP (2009) Analysis of drought severity‐area‐frequency curves using a general circulation model and scenario uncertainty. J Geophys Res Atmos 114(D6):2156–2202Google Scholar
  30. Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1):202–216CrossRefGoogle Scholar
  31. Pai DS, Sridhar L, Guhathakurta P, Hatwar HR (2011) District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI). Nat Hazards 59(3):1797–1813CrossRefGoogle Scholar
  32. Pal NR, Bezdek JC (1995) On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst 3(3):370–379CrossRefGoogle Scholar
  33. Rajsekhar D, Mishra AK, Singh VP (2011) Drought regionalization of brazos river basin using an entropy approach. In: 2011 Symposium on data-driven approaches to droughts. Paper 40Google Scholar
  34. Rajsekhar D, Mishra AK, Singh VP (2012) Regionalization of drought characteristics using an entropy approach. J Hydrol Eng 18(7):870–887CrossRefGoogle Scholar
  35. Rao AR, Srinivas VV (2006) Regionalization of watersheds by fuzzy cluster analysis. J Hydrol 318(1):57–79CrossRefGoogle Scholar
  36. Raziei T, Bordi I, Pereira LS (2008) A precipitation-based regionalization for Western Iran and regional drought variability. Hydrol Earth Syst Sci 12(6):1309–1321CrossRefGoogle Scholar
  37. Riebsame WE, Changnon SA, Karl TR (1991) Drought and natural resources management in the United States. Impacts and implications of the 1987–89 drought. Westview Press IncGoogle Scholar
  38. Sahoo RN, Dutta D, Khanna M, Kumar N, Bandyopadhyay SK (2015) Drought assessment in the Dhar and Mewat Districts of India using meteorological, hydrological and remote-sensing derived indices. Nat Hazards 77(2):733–751CrossRefGoogle Scholar
  39. Santos JF, Portela MM, Pulido-Calvo I (2011) Regionalization of droughts in Portugal. River Basin Manag VI 146:239CrossRefGoogle Scholar
  40. Sen Z (1980) Statistical analysis of hydrologic critical droughts. J. Hydraulics Div., ASCE 106 (1), 99–115Google Scholar
  41. Urcid G, Ritter GX (2012) C-means clustering of lattice auto-associative memories for endmember approximation. In: Graña M, Toro C, Posada J, Robert J Howlett, Lakhmi C Jain (eds) Advances in Knowledge-Based and Intelligent Information and Engineering Systems (pp 2140–2149). IOS PressGoogle Scholar
  42. Vasiliades L, Loukas A, Patsonas G (2009) Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts. Nat Hazards Earth Syst Sci 9(3):879–894CrossRefGoogle Scholar
  43. Wilhite DA (2000) Drought as a natural hazard: concepts and definitions. Drought: A Global Assessment. Routledge, LondonGoogle Scholar
  44. Wilhite DA, Glantz MH (1985) Understanding: the drought phenomenon: the role of definitions. Water Int 10(3):111–120CrossRefGoogle Scholar
  45. Wu H, Svoboda MD, Hayes MJ, Wilhite DA, Wen F (2007) Appropriate application of the standardized precipitation index in arid locations and dry seasons. Int J Climatol 27(1):65–79CrossRefGoogle Scholar
  46. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 8:841–847CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of TechnologyGuwahatiIndia

Personalised recommendations