Skip to main content
Log in

Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression

  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

Abstract

Mapping spatial distribution of climatological parameters with a good degree of accuracy is crucial in environmental modeling and planning. Nowadays, there are various models to estimate and predict spatial variables in an area but some such as cokriging and geographically weighted regression (GWR) have got more attention from experts in this field. The objectives of this study are to evaluate and compare GWR with ordinary cokriging (OCK) techniques for estimating the mean annual air temperature (MAT) of Iran using European Centre for Medium-Range Weather Forecasts (ECMWF) data and auxiliary variables (e.g., longitude, latitude and altitude). The MAT-gridded data for Iran was collected in pixels during the time interval of 1987–2015 from the ERA-Interim re-analysis version of ECMWF. Validation results indicate that cokriging model with latitude and altitude for estimating MAT has the lowest MAE (0.0155), MBE (0.00085), RMSE (0.0251), and the highest NS (0.9999) in relation to other cokriging methods. On the other hand, GWR with altitude has better results than those of GWR with other auxiliary variables because of its MAE (0.1271), MBE (0.0124), RMSE (0.1760), and NS (0.9969). By comparing two mentioned methods, cokriging with latitude and altitude has provided the best performance in relation to GWR for prediction of MAT in Iran. To obtain accurate estimation of the spatial distribution of MAT, local residuals were analyzed. Results concluded that residuals of the OCK model have high spatial adaptations between the observed and predicted MAT data compared to the GWR model. Hence, OCK was a relatively optimum method for the estimation of MAT compared with GWR.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Stahl, K., et al. (2006). Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agricultural and Forest Meteorology, 139(3), 224–236.

    Article  Google Scholar 

  2. Li, S., et al. (2013). Spatial variability of the adaptation of grassland vegetation to climatic change in Inner Mongolia of China. Applied Geography, 43, 1–12.

    Article  Google Scholar 

  3. Wu, T., & Li, Y. (2013). Spatial interpolation of temperature in the United States using residual kriging. Applied Geography, 44, 112–120.

    Article  Google Scholar 

  4. Trisurat, Y., Shrestha, R. P., & Kjelgren, R. (2011). Plant species vulnerability to climate change in Peninsular Thailand. Applied Geography, 31(3), 1106–1114.

    Article  Google Scholar 

  5. Hudson, G., & Wackernagel, H. (1994). Mapping temperature using kriging with external drift: theory and an example from Scotland. International journal of Climatology, 14(1), 77–91.

    Article  Google Scholar 

  6. New, M., et al. (2011). Four degrees and beyond: the potential for a global temperature increase of four degrees and its implications. The Royal Society.

  7. Stainforth, D. A., et al. (2005). Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433(7024), 403–406.

    Article  CAS  Google Scholar 

  8. Change, I.P.o.C. (2014). Climate change 2014–impacts, adaptation and vulnerability: Regional aspects. Cambridge University Press.

  9. Schuur, E., et al. (2015). Climate change and the permafrost carbon feedback. Nature, 520(7546), 171–179.

    Article  CAS  Google Scholar 

  10. IPCC. (2007). Climate change 2007: The physical science basis. Summary for policymakers. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Paris: Summary for policymakers formally approved at the 10th session of working group I of the IPCC.

  11. Soltani, A., Meinke, H., & de Voil, P. (2004). Assessing linear interpolation to generate daily radiation and temperature data for use in crop simulations. European Journal of Agronomy, 21(2), 133–148.

    Article  Google Scholar 

  12. Tabari, H., et al. (2014). A survey of temperature and precipitation based aridity indices in Iran. Quaternary International, 345, 158–166.

    Article  Google Scholar 

  13. Pingale, S. M., et al. (2014). Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centers of the arid and semi-arid state of Rajasthan, India. Atmospheric Research, 138, 73–90.

    Article  Google Scholar 

  14. Khosravi, Y., Lashkari, H., & Asakereh, H. (2017). Spatial variability of water vapour in south and southwest of Iran. Mausam, 68(1), 9–22.

    Google Scholar 

  15. Masih, I., et al. (2010). Regionalization of a conceptual rainfall–runoff model based on similarity of the flow duration curve: a case study from the semi-arid Karkheh basin, Iran. Journal of Hydrology, 391(1), 188–201.

    Article  Google Scholar 

  16. Javanmard, S., et al. (2010). Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran. Advances in Geosciences, 25, 119–125.

    Article  Google Scholar 

  17. Alijani, B., O’brien, J., & Yarnal, B. (2008). Spatial analysis of precipitation intensity and concentration in Iran. Theoretical and Applied Climatology, 94(1), 107–124.

    Article  Google Scholar 

  18. Li, X., Cheng, G., & Lu, L. (2005). Spatial analysis of air temperature in the Qinghai-Tibet plateau. Arctic, Antarctic, and Alpine Research., 37(2), 246–252.

    Article  Google Scholar 

  19. Yang, J., Wang, Y., & August, P. (2004). Estimation of land surface temperature using spatial interpolation and satellite-derived surface emissivity. Journal of Environmental Informatics, 4(1), 37–44.

    Article  Google Scholar 

  20. Knotters, M., Brus, D., & Voshaar, J. O. (1995). A comparison of kriging, co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations. Geoderma, 67(3–4), 227–246.

    Article  Google Scholar 

  21. Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228(1), 113–129.

    Article  Google Scholar 

  22. Stewart Fotheringham, A., Charlton, M., & Brunsdon, C. (1996). The geography of parameter space: an investigation of spatial non-stationarity. International Journal of Geographical Information Systems, 10(5), 605–627.

    Article  Google Scholar 

  23. Cardozo, O. D., García-Palomares, J. C., & Gutiérrez, J. (2012). Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography, 34, 548–558.

    Article  Google Scholar 

  24. Aalto, J., et al. (2013). Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models. Theoretical and Applied Climatology, 112(1–2), 99–111.

    Article  Google Scholar 

  25. Wu, J., et al. (2016). Comparison analysis of sampling methods to estimate regional precipitation based on the kriging interpolation methods: A case of northwestern China.

    Google Scholar 

  26. Seo, D. J., et al. (1990). Stochastic interpolation of rainfall data from rain gages and radar using cokriging: 2. Results. Water Resources Research, 26(5), 915–924.

    Google Scholar 

  27. Sinclair, S., & Pegram, G. (2005). Combining radar and rain gauge rainfall estimates using conditional merging. Atmospheric Science Letters, 6(1), 19–22.

    Article  Google Scholar 

  28. Piazza, A. D., et al. (2015). Comparative analysis of spatial interpolation methods in the Mediterranean area: application to temperature in Sicily. Water, 7(5), 1866–1888.

    Article  Google Scholar 

  29. Lapen, D. R., & Hayhoe, H. N. (2003). Spatial analysis of seasonal and annual temperature and precipitation normals in southern Ontario, Canada. Journal of Great Lakes Research, 29(4), 529–544.

    Article  Google Scholar 

  30. Dyras, I., & Ustrnul, Z. (2007). The spatial analysis of the selected meteorological fields in the example of Poland. Spatial Interpolation for Climate Data: the Use of GIS in Climatology and Meteorology, 87–96.

  31. Benavides, R., et al. (2007). Geostatistical modelling of air temperature in a mountainous region of northern Spain. Agricultural and Forest Meteorology, 146(3–4), 173–188.

    Article  Google Scholar 

  32. Hsu, S., Mavrogianni, A., & Hamilton, I. (2017). Comparing spatial interpolation techniques of local urban temperature for heat-related health risk estimation in a subtropical city. Procedia Engineering, 198, 354–365.

    Article  Google Scholar 

  33. Wang, M., et al. (2017). Comparison of spatial interpolation and regression analysis models for an estimation of monthly near surface air temperature in China. Remote Sensing, 9(12), 1278.

    Article  Google Scholar 

  34. Javari, M. (2017). Comparison of interpolation methods for modeling spatial variations of precipitation in Iran. International Journal of Environmental and Science Education.

  35. Ahani, H., et al. (2013). Non-parametric trend analysis of the aridity index for three large arid and semi-arid basins in Iran. Theoretical and Applied Climatology, 112(3–4), 553–564.

    Article  Google Scholar 

  36. Dee, D. P., & Uppala, S. (2009). Variational bias correction of satellite radiance data in the ERA-interim reanalysis. Quarterly Journal of the Royal Meteorological Society, 135(644), 1830–1841.

    Article  Google Scholar 

  37. McBratney, A. B., & Webster, R. (1983). Optimal interpolation and isarithmic mapping of soil properties: V. Coregionalization and multiple sampling strategy. Journal of Soil Science, 34, 137–162.

    Article  Google Scholar 

  38. Wang, K., Zhang, C., & Li, W. (2013). Predictive mapping of soil total nitrogen at a regional scale: a comparison between geographically weighted regression and cokriging. Applied Geography, 42, 73–85.

    Article  CAS  Google Scholar 

  39. Eldeiry, A. A., & Garcia, L. A. (2010). Comparison of ordinary kriging, regression kriging, and cokriging techniques to estimate soil salinity using LANDSAT images. Journal of Irrigation and Drainage Engineering, 136(6), 355–364.

    Article  Google Scholar 

  40. Yates, S., & Warrick, A. (1987). Estimating soil water content using cokriging. Soil Science Society of America Journal, 51(1), 23–30.

    Article  Google Scholar 

  41. Ali, M. G., et al. (2011). Assessment of geostatistical methods for spatial analysis of SPI and EDI drought indices. World Applied Sciences Journal, 15(4), 474–482.

    Google Scholar 

  42. Sluiter, R. (2009). Interpolation methods for climate data: literature review. R&D Information and Observation Technology, 1–28.

  43. Georganos, S., et al. (2017). Examining the NDVI-rainfall relationship in the semi-arid Sahel using geographically weighted regression. Journal of Arid Environments, 146, 64–74.

    Article  Google Scholar 

  44. Łukawska-Matuszewska, K., & Urbański, J. A. (2014). Prediction of near-bottom water salinity in the Baltic Sea using ordinary least squares and geographically weighted regression models. Estuarine, Coastal and Shelf Science, 149, 255–263.

    Article  Google Scholar 

  45. Fotheringham, A. S., Charlton, M., & Brunsdon, C. (1997). Measuring spatial variations in relationships with geographically weighted regression. In Recent developments in spatial analysis (pp. 60–82). Springer.

  46. Fotheringham, A. S., & Oshan, T. M. (2016). Geographically weighted regression and multicollinearity: dispelling the myth. Journal of Geographical Systems, 18(4), 303–329.

    Article  Google Scholar 

  47. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons.

  48. Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320–328.

    Article  Google Scholar 

  49. Mahdian, M., et al. (2009). Appraisal of the geostatistical methods to estimate monthly and annual temperature. Journal of Applied Sciences, 9(1), 128–134.

    Article  Google Scholar 

  50. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—a discussion of principles. Journal of Hydrology, 10(3), 282–290.

    Article  Google Scholar 

  51. Pratt, B., & Chang, H. (2012). Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales. Journal of Hazardous Materials, 209, 48–58.

    Article  Google Scholar 

  52. Masoudian, A., & Kaviani, M. R. (2009). Climatology of Iran. Isfahan: University of Isfahan press.

    Google Scholar 

  53. Alijani, B. (1994). Climatology of Iran. Tehran: Payam Noor University Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Younes Khosravi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khosravi, Y., Balyani, S. Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression. Environ Model Assess 24, 341–354 (2019). https://doi.org/10.1007/s10666-018-9623-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-018-9623-5

Keywords

Navigation