Comparative study of geo-statistical and multivariate models for air temperature interpolation in central and northern regions of Iran

Document Type : Original Article

Authors

1 Assistant Professor, Irrigation & Reclamation Engineering Department, University of Tehran, Karaj, Iran

2 Graduated student of agrometeorology, Irrigation and Reclamation Engineering Department, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

3 Associate Professor of Agrometeorology, Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources

Abstract

Air temperature is one of the major variables required for agroclimatic classifications. For spatial zoning of temperature point observations, the interpolation approaches in which the horizontal and vertical gradients are included may be applied. In this research, the skill of Kriging, Co-Kriging, geographically weighted regression and Linear Multivariate Regression was evaluated for the interpolation of the monthly mean temperature values using the data of 56 synoptic stations located in the northern and central regions of Iran. The results of the statistical analysis indicated that the geographically weighted regression have the greatest difference with the other methods in month of December, with root mean square error (RMSE) equal to 0.83 °C, Based on the RMSE values of all months, the geographically regression method (with RMSE of 1.26°C) is the most suitable approach for temperature spatial zoning in this region. and then linear multiple regression method with RMSE of 2.24 °C, Kriging with RMSE of 2.52 °C and Cokriging with highest RMSE of 2.86 °C were ranked second to fourth, respectively. Besides, it is concluded that for high altitude areas where almost no weather station exist, the geographically weighted regression method provided the most accurate interpolated data of the air temperature.

Keywords

References

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Journal of Agricultural Meteorology
Volume 6, Issue 2 - Serial Number 12
Vol. 6, No. 2, Autumn & Winter 2019
March 2019
Pages 58-70

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