Comparison of Geostatistical and Deterministic Interpolation to Derive Climatic Surfaces for Mountain Ecosystem

Abstract

Spatial interpolation is the process of estimating value of continuous target variable at unknown location based on available samples. At present, there are many interpolation techniques available, and each technique has its own pros and cons. Accuracy of interpolation mainly depends on (1) sampling pattern and number of samples, (2) interpolation model adopted, and (3) presence of co-variable if a number of sample points are less. There are certain sampling techniques available, namely, regular, random, stratified, cluster, etc. Due to complex topography of mountain ecosystem like the Himalaya, stratified sampling technique is supposed to give the best prediction. But due to high variability in elevation and remote locations in mountain regions, installation of automatic weather stations (AWS) as per stratified sampling method is very difficult. So mountain regions face lack of sufficient number of samples/observations for accurate prediction (Stahl et al. 2006). There are many interpolation techniques available, but they are mainly classified into two categories: deterministic and geostatistical techniques. Deterministic techniques are based on the geometric properties of the samples, whereas geostatistical techniques are based on geometric as well as spatial autocorrelation of the target variable. Some of the deterministic techniques are inverse distance weighted (IDW), spline, Thiessen polygon, and linear regression, and geostatistical techniques are simple kriging, ordinary kriging, universal kriging, co-kriging, regression kriging, indicator kriging, etc. Stationarity, isotropy, intrinsic hypothesis, and unbiasedness are the basic assumptions of geostatistical techniques (Sluiter 2009).