Review: Advances in groundwater potential mapping

• Published in: Hydrogeology journal Vol. 27; no. 7; pp. 2307 - 2324
• Main Authors: Díaz-Alcaide, S., Martínez-Santos, P.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-11-2019; Springer Nature B.V
• Subjects: Anomalies; Aquatic Pollution; Cartographic databases; Data integration; Drainage; Drainage density; Earth and Environmental Science; Earth Sciences; Environmental changes; Evapotranspiration; Exploration; Geology; Geophysics/Geodesy; Groundwater; Groundwater mapping; Groundwater potential; Groundwater resources; Hydrogeology; Hydrology/Water Resources; Land cover; Land use; Landforms; Learning algorithms; Machine learning; Mapping; Population growth; Quality assurance; Rain; Rainfall; Remote sensing; Satellite imagery; Sensing techniques; Soil; Soil moisture; Soils; Spaceborne remote sensing; Surveys; Waste Water Technology; Water Management; Water Pollution Control; Water Quality/Water Pollution; Water resources; Decision support systems; Drinking water; Geographical information systems; Groundwater survey; Remote sensing
• ISSN: 1431-2174; 1435-0157
• DOI: 10.1007/s10040-019-02001-3

Groundwater resources can be expected to be increasingly relied upon in the near future, as a consequence of rapid population growth and global environmental change. Cost-effective and efficient techniques for groundwater exploration are gaining recognition as tools to underpin hydrogeological surveys in mid- and low-income regions. This paper provides a state of the art on groundwater potential mapping, an explorative technique based on remote sensing and geographical databases that has experienced major developments in recent years. A systematic review of over 200 directly relevant papers is presented. Twenty variables were found to be frequently involved in groundwater potential investigations, of which eight are almost always present: geology, lineaments, landforms, soil, land use/land cover, rainfall, drainage density, and slope. The more innovative approaches draw from satellite images to develop indicators related to vegetation, evapotranspiration, soil moisture and thermal anomalies, among others. Data integration is carried out either through expert judgement or through machine-learning techniques, the latter being less common. Three main conclusions were reached: (1) for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute; (2) the potential of remote-sensing techniques in groundwater exploration is enormous, particularly when the power of machine learning is harnessed by involving human judgement; (3) quality assurance remains the main challenge ahead, as exemplified by the fact that a majority of the existing studies in the literature lack adequate validation.