GCM Based Fuzzy Clustering to Identify Homogeneous Climatic Regions of North-East India
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GCM Based Fuzzy Clustering to Identify Homogeneous Climatic Regions of North-East India

Authors: Arup K. Sarma, Jayshree Hazarika

Abstract:

The North-eastern part of India, which receives heavier rainfall than other parts of the subcontinent, is of great concern now-a-days with regard to climate change. High intensity rainfall for short duration and longer dry spell, occurring due to impact of climate change, affects river morphology too. In the present study, an attempt is made to delineate the North-eastern region of India into some homogeneous clusters based on the Fuzzy Clustering concept and to compare the resulting clusters obtained by using conventional methods and nonconventional methods of clustering. The concept of clustering is adapted in view of the fact that, impact of climate change can be studied in a homogeneous region without much variation, which can be helpful in studies related to water resources planning and management. 10 IMD (Indian Meteorological Department) stations, situated in various regions of the North-east, have been selected for making the clusters. The results of the Fuzzy C-Means (FCM) analysis show different clustering patterns for different conditions. From the analysis and comparison it can be concluded that nonconventional method of using GCM data is somehow giving better results than the others. However, further analysis can be done by taking daily data instead of monthly means to reduce the effect of standardization.

Keywords: Climate change, conventional and nonconventional methods of clustering, FCM analysis, homogeneous regions.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096994

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[1] D. Mehrotra and R. Mehrotra, “Climate change and hydrology with emphasis on the Indian subcontinent,” Hydrological Sciences Journal, vol. 40:2, pp. 231-242, April 1995.
[2] R. L. Wilby, H. Hassan, and K. Hanaki, “Statistical downscaling of hydrometeorological variables using general circulation model output,” Journal of Hydrology, vol. 205, pp. 1-19, 1998.
[3] R. L. Wilby, T. M. L. Wigley, D. Conway, P. D. Jones, B.C. Hewitson, J. Main, and D. S. Wilks, “Statistical downscaling of general circulation model output: A comparison of methods,” Water Resources Research, vol. 34, no. 11, pp. 2995-3008, Nov. 1998.
[4] X.-C. Zhang, “Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion,” Agricultural and Forest Meteorology, vol. 135, pp. 215-229, 2005.
[5] A. K. Gosain, S. Rao, and D. Basuray, “Climate change impact assessment on hydrology of Indian river basins,” Current Science, vol. 90, no. 3, pp. 346-353, Feb. 2006.
[6] H. D. Fill, and J. R. Stedinger, “Homogeneity tests based upon Gumbel distribution and a critical appraisal of Dalrymple’s test,” Journal of Hydrology, vol. 166, pp. 81-105, 1995.
[7] A. Habib, and M. Ellouze, “Hydrological delineation of homogeneous regions in Tunisia,” Water Resources Management, vol. 20, pp. 961– 977, 2006.
[8] P. Satyanarayana, and V.V. Srinivas, “Regionalization of precipitation in data sparse areas using large scale atmospheric variables – A fuzzy clustering approach,” Journal of Hydrology, vol. 405, pp. 462–473, 2011.