Copyright © 2012 Avit Kumar Bhowmik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

1. O. E. Tveito, “The developments in spatialization of meteorological and climatological elements,” in Spatial Interpolation for Climate Data. The Use of GIS in Climatology and Meteorology, H. Dobesch, P. Dumolard, and I. Dyras, Eds., pp. 73–86, ISTE, London, UK, 2007.
2. M. H. K. Chowdhury and S. K. Debsharma, “In Climate change in Bangladesh—a statistical review,” Report on the IOC-UNEP Workshop on the Impacts of Sea Level Rise due to Global Warming, NOAMI, 1992.
3. Y.-H. Chou, “Exploring spatial analysis in geographic information systems,” Exploring Spatial Analysis in Geographic Information Systems, 1997. View at Scopus
4. P. Goovaerts, “Ordinary cokriging revisited,” Mathematical Geology, vol. 30, no. 1, pp. 21–40, 1998. View at Scopus
5. E. H. Isaaks and R. M. Srivastava, An Introduction to Applied Geostatistics, Oxford University Press, New York, NY, USA, 1989.
6. A. G. Journel and C. J. Huijbregts, Mining Geostatistics, Academic Press, New York, NY, USA, 1978.
7. D. L. Phillips, J. Dolph, and D. Marks, “A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain,” Agricultural and Forest Meteorology, vol. 58, no. 1-2, pp. 119–141, 1992. View at Scopus
8. G. Q. Tabios and J. D. Salas, “Comparative analysis of techniques for spatial interpolation of precipitation,” Water Resources Bulletin, vol. 21, no. 3, pp. 365–380, 1985. View at Scopus
9. M. F. Hutchinson, “Interpolating mean rainfall using thin plate smoothing splines,” International Journal of Geographical Information Systems, vol. 9, no. 4, pp. 385–403, 1995. View at Scopus
10. C. Caruso and F. Quarta, “Interpolation methods comparison,” Computers and Mathematics with Applications, vol. 35, no. 12, pp. 109–126, 1998. View at Scopus
11. L. Xin, C. Guodong, and L. Ling, “Comparison of spatial interpolation methods,” Advance in Earth Sciences, pp. 82–102, 2003.
12. P. Goovaerts, Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY, USA, 1st edition, 1997.
13. S. Anderson, “An evaluation of spatial interpolation methods on air temperature in phoenix, AZ,” Journal of Climatology, pp. 57–70, 1987.
14. P. Goovaerts, Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY, USA, 2nd edition, 2007.
15. P. Goovaerts, “Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall,” Journal of Hydrology, vol. 228, no. 1-2, pp. 113–129, 2000. View at Publisher · View at Google Scholar · View at Scopus
16. S. M. Vicente-Serrano, M. A. Saz-Sánchez, and J. M. Cuadrat, “Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature,” Climate Research, vol. 24, no. 2, pp. 161–180, 2003. View at Scopus
17. N. Cressie, Statistics for Spatial Data, Wiley, New York, NY, USA, 1991.
18. T. Szentimrey, Z. Bihari, and S. Szalai, “Comparison of Geostatistical and Meteorological Interpolation Methods (What is What?),” in Spatial Interpolation for Climate Data. the Use of GIS in Climatology and Meteorology, H. Dobesch, P. Dumolard, and I. Dyras, Eds., pp. 73–86, ISTE, London, UK, 2007.
19. T. Szentimrey, Z. Bihari, and S. Szalai, “Limitations of the present GIS methods in respect of meteorological purposes,” in Proceedings of the 5th Annual Meeting of the European Meteorological Society (EMS)/7th ECAM, Utrecht, the Netherlands, September2005.
20. A. K. Bhowmik and P. Cabral, “Statistical evaluation of spatial interpolation methods for small-sampled region: a case study of temperature change phenomenon in Bangladesh,” in Proceedings of the Computational Science and its Applications (ICCSA '11), B. Mugante, O. Gervasi, A. Iglesias, D. Taniar, and B. O. Apduhan, Eds., Lecture Notes in Computer Science, pp. 44–59, Springer, Heidelberg, Germany, 2011.
21. S. A. Islam, Analyzing Changes of Temperature over Bangladesh due to Global Warming Using Historic Data, Climate Change and Bangladesh, 2006.
22. A. Kieffer Weisse and P. Bois, “Topographic effects on statistical characteristics of heavy rainfall and mapping in the French Alps,” Journal of Applied Meteorology, vol. 40, no. 4, pp. 720–740, 2001. View at Scopus
23. U. Haberlandt, “Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event,” Journal of Hydrology, vol. 332, no. 1-2, pp. 144–157, 2007. View at Publisher · View at Google Scholar · View at Scopus
24. C. D. Lloyd, “Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain,” Journal of Hydrology, vol. 308, no. 1–4, pp. 128–150, 2005. View at Publisher · View at Google Scholar · View at Scopus
25. N. Diodato, “The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain,” International Journal of Climatology, vol. 25, no. 3, pp. 351–363, 2005. View at Publisher · View at Google Scholar · View at Scopus
26. E. P. J. Boer, K. M. de Beurs, and A. D. Hartkamp, “Kriging and thin plate splines for mapping climate variables,” International Journal of Applied Earth Observation and Geoinformation, vol. 2001, no. 2, pp. 146–154, 2001. View at Scopus
27. J. J. Carrera-Hernández and S. J. Gaskin, “Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico,” Journal of Hydrology, vol. 336, no. 3-4, pp. 231–249, 2007. View at Publisher · View at Google Scholar · View at Scopus
28. R. Durão, M. J. Pereira, A. C. Costa, J. M. Corte, and A. Soares, “Indices of precipitation extremes in Southern Portugal—a geostatistical approach,” Natural Hazards and Earth System Science, vol. 9, no. 1, pp. 241–250, 2009. View at Scopus
29. A. C. Costa and A. Soares, “Trends in extreme precipitation indices derived from a daily rainfall database for the South of Portugal,” International Journal of Climatology, vol. 29, no. 13, pp. 1956–1975, 2009.
30. P. C. Kyriakidis, J. Kim, and N. L. Miller, “Geostatistical mapping of precipitation from rain gauge data using atmospheric and terrain characteristics,” Journal of Applied Meteorology, vol. 40, no. 11, pp. 1855–1877, 2001. View at Scopus
31. D. Braun, Bangladesh, India Most Threatened by Climate Change, Risk Study Finds, National Geographic, 2010.
32. C. Yao, W. Qian, S. Yang, and Z. Lin, “Regional features of precipitation over Asia and summer extreme precipitation over Southeast Asia and their associations with atmospheric-oceanic conditions,” Meteorology and Atmospheric Physics, vol. 106, no. 1, pp. 57–73, 2010. View at Publisher · View at Google Scholar · View at Scopus
33. T. A. M. G. Klein, T. C. Peterson, and D. A. Quadir, “Changes in daily temperature and precipitation extremes in central and south Asia,” Journal of Geophysical Research, vol. 111, Article ID D16105, 8 pages, 2006.
34. S. Shahid, “Spatio-temporal variability of rainfall over Bangladesh during the time period 1969–2003,” Asia-Pacific Journal of Atmospheric Science, vol. 45, pp. 375–389, 2009.
35. J. Suhaila and A. Jemain, “Spatial analysis of daily rainfall intensity and concentration index in Peninsular Malaysia,” Theoretical and Applied Climatology, vol. 108, no. 1-2, pp. 235–245, 2012. View at Publisher · View at Google Scholar
36. Bangladesh Bureau of Statistics (BBS), Statistical Pocket Book Bangladesh, 2009.
37. “Climate change 2007: impacts, adaptation and vulnerability,” in Intergovermental Panel on Climate Change (IPCC)Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Intergovermental Panel on Climate Change (IPCC), Ed., Cambridge University Press, Cambridge, UK, 2007.
38. Disaster Management Information Center (DMIC) of Comprehensive Disaster Management Program (CDMP), “Bangladesh Meteorological Department,” 2012, http://www.bmd.gov.bd/index.php.
39. P. Dumolard, “Uncertainty from spatial sampling: a case study in the French Alps,” in Spatial Interpolation for Climate Data. The Use of GIS in Climatology and Meteorology, H. Dobesch, P. Dumolard, and I. Dyras, Eds., pp. 57–70, ISTE, London, UK, 2007.
40. K. N. Dirks, J. E. Hay, C. D. Stow, and D. Harris, “High-resolution studies of rainfall on Norfolk Island Part II: interpolation of rainfall data,” Journal of Hydrology, vol. 208, no. 3-4, pp. 187–193, 1998. View at Publisher · View at Google Scholar · View at Scopus
41. Institute for Statistics and Mathematics, WU Wien, (ISMWUWW), “The R Project for Statistical Computing,” 2012, http://www.r-project.org/.
42. T. C. Peterson, C. Folland, G. Gruza, W. Hogg, A. Mokssit, and N. Plummer, “Report on the activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998–2001,” Report WCDMP-47, WMO-TD, 1071, World Meteorological Organization, Geneva, Switzerland, 2001.
43. N. Plummer, M. J. Salinger, N. Nicholls et al., “Changes in climate extremes over the Australian region and New Zealand during the twentieth century,” in Weather and Climate Extremes: Changes, Variations and a Perspective from the insurance Industry, T. R. Karl, N. Nicholls, and A. Ghazi, Eds., pp. 183–202, Kluwer Academic Publishers, 1999.
44. A. C. dos Santos Carlos, C. M. U. Neale, T. V. R. Raoa, Silva, and B. B. Da, “Trends in indices for extremes in daily temperature and precipitation over Utah, USA,” International Journal of Climatology, vol. 31, pp. 1813–1822, 2011.
45. Q. You, S. Kang, E. Aguilar et al., “Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961–2003,” Climate Dynamics, vol. 36, no. 11-12, pp. 2399–2417, 2011. View at Publisher · View at Google Scholar · View at Scopus
46. D. E. Alexander, The Third World Natural Disasters, Kluwer Academic Publishers, Dordrecht, the Netherlands, 1999.
47. Ministry of Environment and Forests Government of the People's Republic of Bangladesh (MEF), Bangladesh Climate Change Strategy and Action Plan, 2008.
48. The World Bank (WB), Bangladesh, “Economics of Adaptation to Climate Change Study,” 2012, http://climatechange.worldbank.org/content/bangladesh-economics-adaptation-climate-change-study/.
49. J. Haslett and A. E. Raftery, “Space-time modelling with long-memory dependence: assessing Ireland's wind power resource,” Journal of the Royal Statistical Society C, vol. 38, pp. 1–50, 1989.
50. K. E. Trenberth, D. P. Stepaniak, and J. M. Caron, “The global monsoon as seen through the divergent atmospheric circulation,” Journal of Climate, vol. 13, no. 22, pp. 3969–3993, 2000. View at Scopus
51. G. Christakos, Modern Spatiotemporal Geostatistics, Oxford University Press, Oxford, UK, 2001.
52. R. J. Hijmans, S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis, “Very high resolution interpolated climate surfaces for global land areas,” International Journal of Climatology, vol. 25, no. 15, pp. 1965–1978, 2005. View at Publisher · View at Google Scholar · View at Scopus
53. R. Christensen, Linear Models for Multivariate, Time Series, and Spatial Data, Springer, New York, NY, USA, 1990.
54. A. Papritz and A. Stein, “Spatial prediction by linear kriging,” in Spatial Statistics for Remote Sensing, A. Stein, F. van der Meer, and B. Gorte, Eds., pp. 83–113, Kluwer Academic Publishers, Dodrecht, the Netherlands, 1999.
55. O. Babak and C. V. Deutsch, “Statistical approach to inverse distance interpolation,” Stochastic Environmental Research and Risk Assessment, vol. 23, no. 5, pp. 543–553, 2009. View at Publisher · View at Google Scholar · View at Scopus
56. R. S. Seaman, “Objective analysis accuracies of statistical interpolation and successive correction schemes,” Australian Meteorological Magazine, vol. 31, no. 4, pp. 225–240, 1983. View at Scopus
57. R. S. Bivand, E. J. Pebesma, and V. Gómez-Rubio, Applied Spatial Data Analysis with R, Springer Science and Business Media, LLC, 2008.
58. S. J. Raudys and A. K. Jain, “Small sample size effects in statistical pattern recognition: recommendations for practitioners,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 252–264, 1991. View at Publisher · View at Google Scholar · View at Scopus
59. J. M. Schuurmans, M. F. P. Bierkens, E. J. Pebesma, and R. Uijlenhoet, “Automatic prediction of high-resolution daily rainfall fields for multiple extents: the potential of operational radar,” Journal of Hydrometeorology, vol. 8, no. 6, pp. 1204–1224, 2007. View at Publisher · View at Google Scholar · View at Scopus
60. M. Voltz and R. Webster, “A comparison of kriging, cubic splines and classification for predicting soil properties from sample information,” Journal of Soil Science, vol. 41, no. 3, pp. 473–490, 1990. View at Scopus
61. P. A. Hancock and M. F. Hutchinson, “Spatial interpolation of large climate data sets using bivariate thin plate smoothing splines,” Environmental Modelling and Software, vol. 21, no. 12, pp. 1684–1694, 2006. View at Publisher · View at Google Scholar · View at Scopus
62. R. Webster and M. A. Oliver, Geostatistics for Environmental Scientists, John Wiley and Sons, Chichester, UK, 2001.
63. R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of International Joint Conference on (AI '95), pp. 1137–1145, 1995.
64. J. Chiles and P. Delfiner, Geostatistics: Modeling Spatial Uncertainty, John Wiley & Sons, New York, NY, USA, 1999.
65. C. Willmott, “On the evaluation of model performance in physical geography,” in Spatial Statistics and Models, G. L. Gaile and C. J. Willmott, Eds., pp. 443–460, 1984.
66. P. M. Atkinson and N. J. Tate, “Spatial scale problems and geostatistical solutions: a review,” Professional Geographer, vol. 52, no. 4, pp. 607–623, 2000. View at Scopus
67. A. H. Auchincloss, A. V. Diez Roux, D. G. Brown, T. E. Raghunathan, and C. A. Erdmann, “Filling the gaps: spatial interpolation of residential survey data in the estimation of neighborhood characteristics,” Epidemiology, vol. 18, no. 4, pp. 469–478, 2007. View at Publisher · View at Google Scholar · View at Scopus
68. R. Weisz, S. Fleischer, and Z. Smilowitz, “Map generation in high-value horticultural integrated pest management: appropriate interpolation methods for site-specific pest management of Colorado potato beetle (Coleoptera: Chrysomelidae),” Journal of Economic Entomology, vol. 88, no. 6, pp. 1650–1657, 1995. View at Scopus
69. K. Kelley, “Sample size planning for the coefficient of variation from the accuracy in parameter estimation approach,” Behavior Research Methods, vol. 39, no. 4, pp. 755–766, 2007. View at Scopus