Groundwater Vulnerability Assessment Techniques: A Comparative Review of Models and Applications

Abstract

Assessment of groundwater vulnerability is a prerequisite tool that safeguards the subsurface water resources against mounting pressures of contamination caused by agricultural intensification, urbanization, and industrialization. The review gives a critical and elaborate comparison of some of the most popular methods of vulnerability assessment of groundwater, such as index-based methods, process-based numerical methods, statistical methods, and new methods based on machine learning. The conceptual framework that shapes the vulnerability assessment is first discussed, which shows the difference between intrinsic and specific vulnerability and the importance of key hydrogeological factors. After that, popular models are systematically divided and analyzed according to their predictive performance, data needs, computational complexity, interpretability, and scalability. It is found that index-based models continue to be important in large-scale applications as they are simple, whereas process-based models are more physically realistic in site-specific studies but demand a lot of data. Machine learning methods are highly predictive but have difficulties concerning the dependency and transparency of the data. Multi-methodological hybrid models are promising to overcome these weaknesses. The usefulness of vulnerability assessments in groundwater management has been demonstrated through the use of the tool in a variety of different hydrogeological environments, with the problems of uncertainty, data access, and model transferability remaining. This review reveals that standardized methodologies, better uncertainty quantification, and increased incorporation of advanced data sources are necessary. The future studies must target the development of dynamic, data-driven, and hybrid frameworks in order to improve the credibility and usability of groundwater vulnerability assessment.