Water table mapping is seen as a fundamental tool in hydrogeology. Water table maps inform groundwater system conceptualisation and can be used to estimate flow directions and magnitudes or hydraulic parameters. Despite their widespread use, no guidelines exist regarding the selection and use of various types of data for spatial interpolation. Input data include direct observations of the water table, such as hydraulic head measured in bores. Yet these only cover a very limited extent of regional aquifer systems. Geophysical, remote sensing and geological conceptual data can bridge the gap between bores and provide information about water table with broad spatial coverage.
In order to use geophysical, remote sensing, and geological conceptual data for water table mapping, a clear relation must exist between these data and the transition in the subsurface from unsaturated to saturated. This relation is contingent on the data type, the height of the water table, and other subsurface properties such as clay content or groundwater salinity. In this research, we classify indirect observations about the water table as either proxy or covariate. Proxy data can be defined as a function of the water table and, conversely, the water table can be defined as a function of covariate data. A well-defined classification scheme helps to determine the means for combining and interpolating multiple data types.
This study presents a comprehensive review of published water table mapping studies. This includes a detailed summary of various data types, including their: relation to the water table, pre-processing requirements, scale of resolution, and associated forms of measurement error. Overall, we demonstrate that the use of geophysical, remote sensing, and geological conceptual data in water table mapping has the potential to strongly enhance the use of hydrogeological data alone.