NS  Vol.3 No.12 , December 2011
Analysis and comparison of spatial interpolation methods for temperature data in Xinjiang Uygur Autonomous Region, China
ABSTRACT
Spatial interpolation methods are frequently used to estimate values of meteorological data in locations where they are not measured. However, very little research has been investigated the relative performance of different interpolation methods in meteorological data of Xinjiang Uygur Autonomous Region (Xinjiang). Actually, it has importantly practical significance to as far as possibly improve the accuracy of interpolation results for meteorological data, especially in mountainous Xinjiang. There- fore, this paper focuses on the performance of different spatial interpolation methods for monthly temperature data in Xinjiang. The daily observed data of temperature are collected from 38 meteorological stations for the period 1960- 2004. Inverse distance weighting (IDW), ordinary kriging (OK), temperature lapse rate method (TLR) and multiple linear regressions (MLR) are selected as interpolated methods. Two rasterized methods, multiple regression plus space residual error and directly interpolated observed temperature (DIOT) data, are used to analyze and compare the performance of these interpolation methods respectively. Moreover, cross-validation is used to evaluate the performance of different spatial interpolation methods. The results are as follows: 1) The method of DIOT is unsuitable for the study area in this paper. 2) It is important to process the observed data by local regression model before the spatial interpolation. 3) The MLR-IDW is the optimum spatial interpolation method for the monthly mean temperature based on cross-validation. For the authors, the reliability of results and the influence of measurement accuracy, density, distribution and spatial variability on the accuracy of the interpolation methods will be tested and analyzed in the future.

Cite this paper
Chai, H. , Cheng, W. , Zhou, C. , Chen, X. , Ma, X. and Zhao, S. (2011) Analysis and comparison of spatial interpolation methods for temperature data in Xinjiang Uygur Autonomous Region, China. Natural Science, 3, 999-1010. doi: 10.4236/ns.2011.312125.
References
[1]   Willmott, C.J., Rowe, C. and Philpot, W. (1985) Small- cale climate maps: A sensitivity analysis of some common assumptions associated. The American Cartographer, 12, 5-16. doi:10.1559/152304085783914686

[2]   Ishida, T. and Kawashima, S. (1993) Use of cokriging to estimate surface air temperature from elevation. Theoretical and Applied Climatology, 47, 147-157. doi:10.1007/BF00867447

[3]   Willmott, C.J. and Matsuura, K. (1995) Smart interpolation of annually averaged air temperature in the United States. Journal of Applied Meteorology, 34, 2577-2586. doi:10.1175/1520-0450(1995)034<2577:SIOAAA>2.0.CO;2

[4]   Courault, D. and Monestiez, P. (1999) Spatial interpolation of air temperature according to atmospheric circulation patterns in southeast France. International Journal of Climatology, 19, 365-378. doi:10.1002/(SICI)1097-0088(19990330)19:4<365::AID-JOC369>3.0.CO;2-E

[5]   Price, D.T., McKenney, D.W., Nalder, I.A., Hutchinson, M. F. and Kesteven, J.L. (2000) A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agricultural and Forest Meteorology, 101, 81-94. doi:10.1016/S0168-1923(99)00169-0

[6]   Hofierka, J., Parajka, J., Mitasova, H. and Mitas, L. (2002) Multivariate interpolation of precipitation using regularized spline with tension. Transactions in GIS, 6, 135-150. doi:10.1111/1467-9671.00101

[7]   Rawlins, M.A. and Willmott, C.J. (2003) Winter air temperature change over the terrestrial arctic, 1961-1990. Arctic, Antarctic, and Alpine Research, 35, 530-537. doi:10.1657/1523-0430(2003)035[0530:WATCOT]2.0.CO;2

[8]   Dobesch, H., Dumolard, P. and Dyras, I. (2007) Spatial interpolation for climate data: The use of GIS in climatology and meteorology. ISTE Ltd., London.

[9]   Muhammad, W.A., Zhao, C., Ni, J. and Muhammad, A. (2010) GIS-based high-resolution spatial interpolation of precipitation in mountain-plain areas of Upper Pakistan for regional climate change impact studies. Theoretical and Applied Climatology, 99, 239-253. doi:10.1007/s00704-009-0140-y

[10]   ?en, Z. and ?ahin, A. D. (2001) Spatial interpolation and estimation of solar irradiation by cumulative semivario- grams. Solar Energy, 71, 11-21. doi:10.1016/S0038-092X(01)00009-3

[11]   Murphy, R.R., Curriero, F.C., Ball, W.P. and Asce, M. (2010) Comparison of spatial interpolation methods for water quality evaluation in the chesapeake bay. Journal of Environmental Engineering, 136, 160-171. doi:10.1061/(ASCE)EE.1943-7870.0000121

[12]   Luo, W., Taylor, M.C. and Parker, S.R. (2008) A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. International Journal of Climatology, 28, 947-99. doi:10.1002/joc.1583

[13]   Janis, M.J., Hubbard, K.G. and Redmond, K.T. (2004) Station density strategy for monitoring long term climatic change in the contiguous United States. Journal of climate, 17, 151-162. doi:10.1175/1520-0442(2004)017<0151:SDSFML>2.0.CO;2

[14]   Kong, Y. and Tong, W. (2008) Spatial exploration and interpolation of the surface precipitation data. Geographical Research, 27, 1097-1108 (in Chinese).

[15]   Abtew, W., Obeysekera, J. and Shih, G. (1993) Spatial analysis for monthly rainfall in South Florida. Water Resources Bulletin.American Water Resources Association, 29, 179-188. doi:10.1111/j.1752-1688.1993.tb03199.x

[16]   Anderson, S. (2002) An evaluation of spatial interpolation methods on air temperature in Phoenix, AZ. Department of Geography, Arizona State University. http://www.cobblestoneconcepts.com/ucgis2summer/anderson/anderson.htm

[17]   Daly, C., Helmer, E.H. and Qui?ones, M. (2003) Mapping the climate of Puerto Rico, Vieques and Culebra. International Journal of Climatology, 23, 1359-1381. doi:10.1002/joc.937

[18]   Zhu, H., Liu, S. and Jia, S. (2004) Problems of the spatial interpolation of physical geographical elements. Geographical Research, 23, 425-432. (in Chinese)

[19]   Li, M., Shao, Q. and Renzullo, L. (2010) Estimation and spatial interpolation of rainfall intensity distribution from the effective rate of precipitation. Stoch Environ Res Risk Assess, 24, 117-130. doi:10.1007/s00477-009-0305-3

[20]   Chiu, C., Lin, P. and Lu, K. (2009). GIS-based tests for quality control of meteorological data and spatial interpolation of climate data—A case study in mountainous Taiwan. Mountain Research and Development, 29, 339- 49. doi:10.1659/mrd.00030

[21]   Dodson, R. and Marks, D. (1997) Daily air temperature interpolated at high spatial resolution over a large mountainous region. Climate Resource, 8, 1-20. doi:10.3354/cr008001

[22]   Dubois, G. (1998) Spatial interpolation comparison 97: Foreword and introduction. Journal of Geographic Information and Decision Analysis, 2, 1-11.

[23]   Zimmerman, D., Pavlik, C., Ruggles, A. and Armstrong, M.P. (1999) An experimental comparison of ordinary and universal kriging and inverse distance weighting. Mathe- matical Geology, 31, 375-390. doi:10.1023/A:1007586507433

[24]   Li, X., Cheng, G.D. and Lu, L. (2000) Comparison of spatial interpolation methods. Advance in Earth sciences, 15, 260-265 (in Chinese).

[25]   Lin, Z., Mo, X., Li, H. and Li, H. (2002) Comparison of three spatial interpolation methods for climat variables in China. Acta Geographica Sinica, 57, 47-56 (in Chinese).

[26]   Shi, Y.F., Shen, Y.P. and Hu, R.J. (2002) Preliminary study on signal, impact and foreground of climatic shaft from warm-dry to warm-wet in Northwest China. Journal of Glaciology and Geocryology, 24, 219-226 (in Chinese).

[27]   Ishiyama, T. (2003) Estimation of surface conditions around oases in alluvial fan of Tarim Basin based on satellite data. Proceedings of the Third Symposium on Xinjiang Uyghur, China, 15-18.

[28]   Fang, J.Y., Piao, S.L., He, J.S. and Ma, W.H. (2004) Increasing terrestrial vegetation activity in China, 1982-1999. Science in China Series C, Life Sciences, 47, 229-240.

[29]   Jia, B.Q., Zhang, Z.Q., Ci, L.J., Ren, Y.P., Pan, B.R., Zhang Z. (2004) Oasis land-use dynamics and its influence on the oasis environment in Xinjiang, China. Journal of Arid Environments, 56, 11-26. doi:10.1016/S0140-1963(03)00002-8

[30]   Cheng W.M., Zhou C.H., Liu H.J., Zhang Y., Jiang Y., Zhang, Y.C. and Yao, Y.H. (2006) The oasis expansion and eco-environment change over the last 50 years in Manas River Valley, Xinjiang. Science in China (Series D), 49, 163-175. doi:10.1007/s11430-004-5348-1

[31]   Shi, Y.F., Shen, Y.P., Kang, E., Li, D.L., Ding, Y.J., Zhang, G.W. and Hu, R.J. (2007) Recent and future climate change in Northwest China. Climatic Change, 80, 379- 93. doi:10.1007/s10584-006-9121-7

[32]   Qian, Y.B., Wu, Z.N., Yang, Q. Zhang, L.Y. and Wang, X.Y. (2007) Ground-surface conditions of sand-dust event occurrences in the southern Junggar Basin of Xinjiang, China. Journal of Arid Environments, 70, 49-62. doi:10.1016/j.jaridenv.2006.12.001

[33]   Shen, Y.L. (2009) The social and environmental costs associated with water management practices in state environmental protection projects in Xinjiang, China. Environmental Science and Policy, 12, 970-980. doi:10.1016/j.envsci.2009.03.006

[34]   Zhao, X., Tan, K., Zhao, S. and Fang, J. (2011) Changing climate affects vegetation growth in the arid region of the northwestern China. Journal of Arid Environments, 75, 946-952.

[35]   Pan, Y.Z., Gong, D.Y., Deng, L., Li, J. and Gao, J. (2004) Smart distance searching-based and DEM-informed interpolation of surface air temperature in China. Acta Geographica Sinica, 59, 366-374 (in Chinese).

[36]   Zhong, J. (2007) Method of spatial interpolation of air temperature based on GIS and RS in Xinjiang. Desert and Oasis Meteorology, 1, 33-35 (in Chinese).

[37]   Zhong, J. (2010) Study on spatial precipitation interpolation precision based on GIS in Xinjiang. Arid Environmental Monitoring, 24, 43-46, 57 (in Chinese).

[38]   He, Y., Fu, D., Zhao, Z., Su, J. and Lü, G. (2008) Analysis of spatial interpolation methods to precipitation based on GIS in Xinjiang. Research of Soil and Water Conservation, 15, 35-37 (in Chinese).

[39]   Integrated Scientific Research Team of Xinjiang (ISRT), CAS, The geography department of Beijing Normal University. (1978) Xinjiang Geomorphology. Science Press, Beijing (in Chinese).

[40]   Daly, C. (2006) Guidelines for Assessing the Suitability of Spatial Climate Data Sets. International Journal of Climatology, 26, 707-721. doi:10.1002/joc.1322

[41]   Liao, S.B. and Li, Z.H. (2004) Some practical problems related to rasterization of air temperature. Meteorological Science and Technology, 32, 352-356 (in Chinese).

[42]   Cressie Noel, A.C. (1993) Statistics for spatial data. Wiley-Interscience, New York.

[43]   Yang, L.P. (1987) An Abstract for Comprehensive Geographical Regionalization in Xinjiang. Science Press, Beijing (in Chinese).

[44]   You, S.C. and Li, J. (2005) Study on error and its pervasion of temperature estimation. Journal of natural resources, 20, 140-144 (in Chinese).

[45]   Collins, F.C. (1996) A comparison of spatial interpolation techniques in temperature estimation. Proceedings of the Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Barbara, January 21-26. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/collins_fred/collins.html

[46]   Myers, R.H. (1990) Classical and modern regression with applications. PWS-Kent Publishing, Boston.

 
 
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