Practice of geostatistics in the estimation of ion deposition from acid precipitation in the presence of sparse data networks

Abstract

The estimation of the spatial variability of ion deposition from acid precipitation is commonly faced with the problem of sparse data networks sampled over a short period of time. In such cases, geostatistical techniques are pertinent as long as they are properly applied. According to some practical examples, the use of ordinary kriging 1 given an experimental semi-variogram function based on scattered data values can be misleading in terms of predicted estimation variances; however, the choice of additional sampling stations based on such a semi-variogram remains valid. It is also preferable to work with regularly spaced data values that allow the identification of preferential directional variabilities even from a small number of data points. In order to predict the performance of kriging, the use of semi-variogram cross-validation techniques in the presence of small data sets can be misleading and is not recommended. Finally, the integration of additional information from denser precipitation networks through the cokriging technique is questionable when based on a very small number of concomitant deposition and precipitation data values.