Comparison of Spatial Interpolation Methods of Precipitation and Temperature Using Multiple Integration Periods

Hadi S. J., TOMBUL M.

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, vol.46, no.7, pp.1187-1199, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 7
  • Publication Date: 2018
  • Doi Number: 10.1007/s12524-018-0783-1
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1187-1199
  • Keywords: Spatial interpolation, Precipitation, Temperature, Kriging, IDW, HIGH-RESOLUTION, RAINFALL DATA, ELEVATION
  • Anadolu University Affiliated: Yes


Eight spatial interpolation methods are used to interpolate precipitation and temperature over several integration periods in a local scale. The methods used are inverse distance weighting (IDW), Thiessen polygons (TP), trend surface analysis, local polynomial interpolation, thin plate spline, and three Kriging methods: ordinary, universal, and simple (OK, UK, and SK). Daily observations from 17 stations in the Seyhan Basin, Turkey, between 1987 and 1994 are used. A variety of parameters and models are used in each method to interpolate surfaces for several integration periods, namely, daily, monthly and annual total precipitation; monthly and annual average precipitation; and daily, monthly and annual average temperature. The performance is assessed using independent validation based on four measurements: the root mean squared error, the mean squared relative error, the coefficient of determination (r(2)), and the coefficient of efficiency. Based on these validation measurements, the method with smallest errors for most of the integration periods concerning both precipitation and temperature is IDW with a power of 3, whereas TP has the highest errors. The Gaussian model is found superior than other models with less errors in the three Kriging methods for interpolating precipitation, but no specific model is better than another for modeling temperature. UK with elevation as the external drift and SK with the mean as an additional parameter show no superiority over OK. For precipitation, annual average and monthly totals are found to be the worst and best modeled integration periods respectively, with the monthly average the best for temperature.