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Statistical Evaluation of Spatial Interpolation Methods for Small-Sampled Region: A Case Study of Temperature Change Phenomenon in Bangladesh

  • Avit Kumar Bhowmik
  • Pedro Cabral
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6782)

Abstract

This study compares three interpolation methods to create continuous surfaces that describe temperature trends in Bangladesh between years 1948 and 2007. The reviewed techniques include Spline, Inverse Distance Weighting (IDW) and Kriging. A statistical assessment based on univariate statistics of the resulting continuous surfaces indicates that there is little difference in the predictive power of these techniques making hard the decision of selecting the best interpolation method. A Willmott statistical evaluation has been applied to minimize this uncertainty. Results show that IDW performs better for average and minimum temperature trends and Ordinary Kriging for maximum temperature trends. Results further indicate that temperature has an increasing trend all over Bangladesh noticably in the northern and coastal southern parts of the country. The temperature follows an overall increasing trend of 1.06 o C per 100 years.

Keywords

Spatial Interpolation Spline Inverse Distance Weighting Ordinary Kriging Univariate Statistics Willmott Statistics 

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References

  1. 1.
    Divya, Mehrotra, R.: Climate Change and hydrology with emphasis on the Indian subcontinent. Hydrologic Sciences Journal 40, 231–241 (1995)CrossRefGoogle Scholar
  2. 2.
    Chowdhury, M.H.K., Debsharma, S.K.: Climate change in Bangladesh – A statistical review. Report on IOC-UNEP Workshop on Impacts of Sea Level Rise due to Global Warming, NOAMI, November 16-19, 1992, Bangladesh (1992)Google Scholar
  3. 3.
    Chou, Y.-H.: Exploring Spatial Analysis in Geographic Information Systems, p. 474. OnWord Press, Santa Fe (1997)Google Scholar
  4. 4.
    Goovaerts, P.: Geostatistics for Natural Resources Evaluation. Oxford University Press, New York (1997)Google Scholar
  5. 5.
    Goovaerts, P.: Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biol. Fertil. Soils 27(4), 315–334 (1998a)CrossRefGoogle Scholar
  6. 6.
    Journel, A.G., Huijbregts, C.J.: Mining Geostatistics. Academic Press, New York (1978)Google Scholar
  7. 7.
    Phillips, D.L., Dolph, J., Marks, D.: A comparison of geostatistical procedures for spatial analysis of precipitations in mountainous terrain. Agric. and Forest Meteor. 58, 119–141 (1992)CrossRefGoogle Scholar
  8. 8.
    Tabios, G.Q., Salas, J.D.: A comparative analysis of techniques for spatial interpolation of precipitation. Water Resources Bulletin 21(3), 365–380 (1985)CrossRefGoogle Scholar
  9. 9.
    Dirks, K.N., Hay, J.E., Stow, C.D., Harris, D.: High-resolution studies of rainfall on Norfolk Island Part II: interpolation of rainfall data. J. Hydrol. 208(3-4), 187–193 (1998)CrossRefGoogle Scholar
  10. 10.
    Willmott, C.J.: On the evaluation of model performance in physical geography. In: Gaile, G.L., Willmott, C.J. (eds.) Spatial Statistics and Models, pp. 443–460 (1984)Google Scholar
  11. 11.
    Statistical Pocket Book Bangladesh, Bangladesh Bureau of Statistics (BBS). Retrieved 2009-10-10 (2008)Google Scholar
  12. 12.
    Karmakar, S., Shrestha, M.L.: Recent climate change in Bangladesh. SMRC No.4, SMRC, Dhaka (2000)Google Scholar
  13. 13.
    Mia, N.M.: Variations of temperature of Bangladesh. In: Proceedings of SAARC Seminars on Climate Variability In the South Asian Region and its Impacts, SMRC, Dhaka (2003)Google Scholar
  14. 14.
    Parthasarathy, B., Sontake, N.A., Monot, A.A., Kothawale, D.R.: Drought-flood in the summer monsoon season over different meteorological subdivisions of India for the period 1871-1984. Journal of Climatology 7, 57–70 (1987)CrossRefGoogle Scholar
  15. 15.
    Centro de Modelização de Reservatórios Petrolíferos (Decemeber 01, 2008) Centro de Modelização de Reservatórios Petrolíferos, http://cmrp.ist.utl.pt/index.php?lg=2&cont=19 (retrieved January 31, 2011)
  16. 16.
    Birkes, Dodge: Alternative Methods of Regression. Word Press (2009)Google Scholar
  17. 17.
    Deutsch, C.V., Journel, A.G.: GSLIB: Geostatistical Software Library and User’s Guide, 2nd edn. Oxford University Press, New York (1998)Google Scholar
  18. 18.
    Goovaerts, P.: Ordinary cokriging revisited. Math. Geol. 30(1), 21–42 (1998b)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Isaaks, E.H., Srivastava, R.M.: An Introduction to Applied Geostatistics. Oxford University Press, New York (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Avit Kumar Bhowmik
    • 1
  • Pedro Cabral
    • 1
  1. 1.Instituto Superior de Estatística e Gestão de Informação, ISEGIUniversidade Nova de LisboaLisbaoPortugal

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