Assessment of Spacio-temporal Variability by Geostatistical Modelling

The sustainable management of groundwater resources needs quantitative information on its behaviors in space and time. With the availability of distributed models, which can handle large volume of data, the spatial information of groundwater at a grid pattern is not only useful but is necessary to get reliable results. Various methods, both simple and complicated, are available and are in use to get the information about any parameter on a pre specified grid when the parameter is measured at random points in the field. Of these simpler methods, nearest neighbor method, distance weighted method, and polynomial interpolation use distance between data points to relate the change in the magnitude of the entry. However, geophysical attributes exhibit some spatial structuring (Delfiner and Delhomme, 1975). This spatial structure, if deciphered and incorporated in model building, can improve the quality of interpolation (Sabourin, 1983). Kriging is one such technique that takes into consideration the spatial structure of the parameter and so scores over the other methods. Also, kriging provides the estimation of variance, i.e. variance of estimation error, at every estimated point, also indicates the accuracy of the estimated value. This is considered as the major advantage of kriging over other estimation techniques. Geostatistics is based on the observation that the values of spatially distributed data are spatially correlated with values recorded at close locations being highly correlated compared to those recorded at widely spaced locations.