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Spatial homogeneity of extreme precipitation indices using fuzzy clustering over northeast India

Author

Listed:
  • Manish Kumar Goyal

    (Indian Institute of Technology Guwahati
    Indian Institute of Technology Indore)

  • Gupta Shivam

    (Indian Institute of Technology Guwahati)

  • Arup K. Sarma

    (Indian Institute of Technology Guwahati)

Abstract

Regionalization on the basis of the properties of hydro-meteorological data helps in identifying the regions reflecting the similar characteristics which could be useful in designing hydrological structures as well as planning and management of water resources of the region. In this study, rainfall data of northeast India were utilized for calculation of extreme precipitation indices as suggested by expert team on climate change detection and monitoring. Trend analysis of the indices was carried out using Mann–Kendall nonparametric test, and Sen’s slope estimator was used for calculating the magnitude of trend. Further fuzzy c-means method was used for clustering of the selected stations on the basis of six parameters of all these precipitation indices, i.e., latitude, longitude, mean, standard deviation, minimum value and maximum value. Three cluster validity indices, namely fuzzy performance index, modified partition entropy and cluster separation index were used for selecting the optimum cluster numbers. Analysis shows insignificant trend for the indices like consecutive dry days and consecutive wet days, whereas maximum 1-day precipitation (R1 day) and maximum 5-day precipitation (R5 day) are not showing any clear trend. It is observed that the number of rainy days is decreasing followed by increasing 1-day precipitation. Cluster analysis of the precipitation indices shows five major clusters for most of the indices.

Suggested Citation

  • Manish Kumar Goyal & Gupta Shivam & Arup K. Sarma, 2019. "Spatial homogeneity of extreme precipitation indices using fuzzy clustering over northeast India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(2), pages 559-574, September.
    • Handle: RePEc:spr:nathaz:v:98:y:2019:i:2:d:10.1007_s11069-019-03715-z
    DOI: 10.1007/s11069-019-03715-z
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    References listed on IDEAS

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    1. Manish Kumar Goyal & Ashutosh Sharma, 2016. "A fuzzy c-means approach regionalization for analysis of meteorological drought homogeneous regions in western India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 1831-1847, December.
    2. Manish Goyal & Vivek Gupta, 2014. "Identification of Homogeneous Rainfall Regimes in Northeast Region of India using Fuzzy Cluster Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4491-4511, October.
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    Cited by:

    1. Renato Marques Sanches Pereira & Henderson Silva Wanderley & Rafael Coll Delgado, 2022. "Homogeneous regions for rainfall distribution in the city of Rio de Janeiro associated with the risk of natural disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 333-351, March.
    2. Sultan Mahmud & Ferdausi Mahojabin Sumana & Md Mohsin & Md. Hasinur Rahaman Khan, 2022. "Redefining homogeneous climate regions in Bangladesh using multivariate clustering approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1863-1884, March.

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