Space-Time Kriging of Groundwater Data

A large number of geohydrological variables may be viewed as realizations of spatiotemporal random functions. In many instances, the available data are composed of long time series, located at few scattered points. To analyse such data sets it seems imperative to expand the available geostatistical techniques into the time-space domain. It is assumed that the spatial and the temporal components of the data can be characterized by intrinsic random functions. The space-time kriging is then applied to groundwater quantity data in southern Georgia. It appears that this procedure yields maps with lower estimation variances, while allowing forecasting and hindcasting.