Dear Colleagues,

Rapid urbanization and climate change have continued to affect both the quality and quantity of groundwater. The increase in population and climate change has led to the mismanagement and high abstraction of groundwater to fulfil the needs, resulting in the decline and depletion of the water level. This has led to an increase in the aquifer stress index in many cities across the world. This important resource, groundwater, is vulnerable to contamination by chemicals resulting from human activities such as the population density increasing. While discussing groundwater quality, groundwater chemistry should be considered as it is the deciding factor for utility. On the other hand, groundwater is intimately connected with the landscape and land use that it underlies and most of the landscape and is vulnerable to the anthropogenic activities on the land surface above. In this scenario, the United Nations has declared in the Sustainable Development Goals (SDG-6, Clean Water, Sanitation and Hygiene) to achieve the target “universal and equitable access to safe and affordable drinking water for all” by 2030. In order to achieve this, the problem should be addressed in a multi-dimensional approach considering the end users, stakeholders, policymakers, researchers, agriculturalists, and politicians. In recent years, artificial intelligence methods have been widely used to predict water-system variables due to their high ability to learn complex mathematical relationships between output and prediction variables. One of the most common machine-learning algorithms used to predict the groundwater level is the artificial neural network (ANN). A large portion of the world’s groundwater is stored in the aquifers shared by several counties known as trans-boundary aquifers (TBA). So far, 592 identified trans-boundary aquifers were identified as defined in the European Union Water Framework Directive, EU WFD, underlying almost every nation. There is an urgent need for improved governance to address the over-exploitation and degradation of this vital ‘shared and hidden' groundwater aquifer, to preserve and optimize this decline and depletion. Groundwater depletion can pose other serious threats to the environment apart from the depletion of resources, such as saltwater intrusion, submarine groundwater discharge, limited biodiversity, and the development of dangerous sinkholes. Reports also indicate that more comprehensive research and data sharing is essential to replenish the resource and conserve it for sustainable use in the future.

Themes:

• Traditional methods to enhance the aquifer potential.
• Impact of land-use/land-cover changes on groundwater.
• Influence of surface water and extreme events on groundwater resources.
• Modern approaches (machine learning, IoT, AI, GIS, RS, big-data analysis, groundwater modeling) for groundwater management.
• Geochemical approaches (soil and water chemistry) to assess the aquifer vulnerability through natural and anthropogenic processes.
• The impact of population and climate change on groundwater resources.
• SDGs—capacity building and community-level participation for groundwater management.

Prof. Dr. AL. Ramanathan
Dr. Prasanna Mohan Viswanathan
Dr. Chidambaram Sabarathinam
Dr. Banajarani Panda
Dr. Elumalai Vetrimurugan
Guest Editors

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• aquifer
• SDG
• TBA
• pollution
• groundwater chemistry
• land use/landcover
• artificial intelligence