A review of GIS-integrated statistical techniques for groundwater quality evaluation and protection

Water quality evaluation is critically important for the protection and sustainable management of groundwater resources, which are variably vulnerable to ever-increasing human-induced physical and chemical pressures (e.g., overexploitation and pollution of aquifers) and to climate change/variability. Preceding studies have applied a variety of tools and techniques, ranging from conventional to modern, for characterization of the groundwater quality worldwide. Recently, geographic information system (GIS) technology has been successfully integrated with the advanced statistical/geostatistical methods, providing improved interpretation capabilities for the assessment of the water quality over different spatial scales. This review intends to examine the current standing of the GIS-integrated statistical/geostatistical methods applied in hydrogeochemical studies. In this paper, we focus on applications of the time series modeling, multivariate statistical/geostatistical analyses, and artificial intelligence techniques used for groundwater quality evaluation and aquifer vulnerability assessment. In addition, we provide an overview of salient groundwater quality indices developed over the years and employed for the assessment of groundwater quality across the globe. Then, limitations and research gaps of the past studies are outlined and perspectives of the future research needs are discussed. It is revealed that comprehensive applications of the GIS-integrated advanced statistical methods are generally rare in groundwater quality evaluations. One of the major challenges in future research will be implementing procedures of statistical methods in GIS software to enhance analysis capabilities for both spatial and temporal data (multiple sites/stations and time frames) in a simultaneous manner.

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Acknowledgements

The authors are grateful to Dr. Olaf Kolditz (Editor-in-Chief) and two anonymous reviewers for their useful suggestions and comments, which helped improve earlier version of this article.