Identification of Desertification Hot Spot Using Aridity Index

Abstract views: 180 / PDF downloads: 46


  • Viral Dave Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar 382 007, India
  • Megha Pandya Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar 382 007, India
  • Ranendu Ghosh Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar 382 007, India


Aridity index (AI) is a useful parameter to study desertification condition and its pattern. The AI formulation adopted by United Nations Environment Program (UNEP), Food and Agriculture Organization (FAO), and United Nations Convention to Combat Desertification (UNCCD), represents a simple but effective scientific investigation tool. AI is calculated by dividing the total annual precipitation by the annual potential evapotranspiration (PET). The objective of this paper is to study and identify the desertification hotspot using the AI over the Gujarat state. Desertification hot spots are the vulnerable areas within defined aridity zones. The weather data e.g. minimum temperature, maximum temperature, solar radiation, wind speed, humidity and rainfall for more than 18 locations all over Gujarat for the past 20 years has been used in this study. FAO Penman-Monteith method was used to calculate PET. Which along with rainfall were used to calculate AI for different locations. Annual AI map for the whole Gujarat has been generated using these values and compared with CGIAR based aridity map. MODIS-Terra NDVI product for the past 20 year period of rabi season has been used to get a correlation of AI with NDVI. In addition to comparing annual AI and NDVI data, thirty years average AI map has been generated for the State.
Key words: Desertification, aridity index, NDVI, potential evapotranspiration


Download data is not yet available.


Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. 1998. Crop evapotranspiration - Guidelines for computing crop water requirements-FAO Irrigation and Drainage Paper 56. FAO, Rome, 300(9), p. D05109.

Ashraf, B., Yazdani, R., Mousavi-Baygi, M. and Bannayan, M. 2014. Investigation of temporal and spatial climate variability and aridity of Iran. Theoretical and Applied Climatology 118(1-2): 35-46.

Barrow, C.J. 1992. World Atlas of Desertification (United Nations Environment Program) (Eds. N. Middleton and DSG Thomas). Edward Arnold, London, 1992. ISBN 0 340 55512 2, £ 89.50 (hardback), ix+ 69 pp. Land Degradation & Development 3(4): 249-249.

Dave, V.A. and Sur, K., 2018. Fuzzy integrated desertification vulnerability model. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Volume XLII-5, pp. 395-401.

Dubey, S.K., Pranuthi, G. and Tripathi, S.K. 2012. Relationship between NDVI and rainfall over India. International Journal of Water Resources and Environmental Sciences 1(4): 102-108.

Glantz, M.H. and Orlovsky, N.S. 1983. Desertification: A review of the concept. Desertification Control Bulletin 9: 15-22.

Matin, S. and Behera, M.D. 2017. Alarming rise in aridity in the Ganga river basin, India, in past 3.5 decades. Current Science 112(2): 229-230.

Raju, B.M.K., Rao, K.V., Venkateswarlu, B., Rao, A.V.M.S., Rao, C.R., Rao, V.U.M., Rao, B.B., Kumar, N.R., Dhakar, R., Swapna, N. and Latha, P. 2013. Revisiting climatic classification in India: A district-level analysis. Current Science, pp. 492-495.

UNCCD 1994. United Nations Convention to combat desertification in those countries experiencing serious drought and/or desertification, particularly in Africa. A/AC.241/27, Paris.









How to Cite

Identification of Desertification Hot Spot Using Aridity Index. (2019). Annals of Arid Zone, 58(1 & 2).