Table 3: Different spatial interpolation methods.

Thin plate splineInverse distance weightingOrdinary krigingUniversal kriging

Description of the climate variabilitySmoothed spline exactly through the measured spatial pointsStraight line exactly through the measured spatial pointsSmoothed line that best fits through the measured semivariance considering anisotropySmoothed line that best fits through the measured semivariance considering spatial trend
Performance parametersPerformance in spite of the changing spatial point configurationPerforms comparatively worse than the stochastic methods and shows improved performance in cross-validation with the increased number of spatial point observationsPerforms comparatively better than other methods when the number of spatial point observations is too little but in general shows improved performance in cross-validation with the increased number of spatial point observationsPerforms comparatively better than the deterministic methods and shows improved performance in cross-validation with the increased number of spatial point observationsPerforms best of all the methods applied and shows improved performance in cross-validation with increased number of spatial point observations
Measurement errors’ inclusionIncludes measurement errors partially in results because of the minimal smoothingIncludes measurement errors in results totallyExcludes measurement errors in result by smoothingExcludes measurement errors in result by smoothing
Short versus long range variabilityDescribes mostly long range variability by removing the noise of short range variability Describes only short range variability Adjusts between the short and long range variability by incorporating anisotropy and semivariance Describes mostly long range variability in light of the spatial trend