You can easily spot every stream, river, delta and lake on a map. The moment water goes into the earth; it becomes invisible. This year’s theme for World Water Day is groundwater, making the invisible visible, both metaphorically and literally.

Groundwater contributes to 98% of the Earth’s available freshwater. It is a finite source in demand for drinking water, agriculture, industry and sustaining aquatic ecosystems. The recognition of groundwater’s pivotal role and contribution to tomorrow’s resources and global climate change has to be increased. Groundwater sustainability management requires high-level interactions between science and policy.

A key aspect of groundwater protection is the knowledge of groundwater quantity and quality, of which data are the foundation of scientific understanding. Monitoring and modelling approaches are essential tools to establish sustainable solutions, as they can provide quantitative and qualitative information on groundwater quantities and contaminants. Data are the focus of a number of recent publications looking at both quantity and quality of groundwater.

Groundwater quantity

Groundwater potential mapping provides information on each type of aquifer and groundwater stocks. Sensor technologies are typically deployed to characterise water potential. et al. elaborated that the water potential information gap lies in insufficient in-situ measurements of discrete and sparse water potential observations. What leads to the gap?

• Groundwater potential is highly associated with each region's geological, hydrological, and ecological factors. Therefore, the uncertainties accompanying data and modelling are significant.
• There is a lack of global data due to the relatively high costs of sensor technologies in low-income regions.

A combination of remote sensing technologies and geographical information system (GIS) may help to fill this gap. Researchers now work on applying various machine learning algorithms for improved groundwater potential mapping.

Groundwater quality

One of the challenges to scientists is incorporating various environmental factors in groundwater modelling for characterising and assessing contaminants. Researchers from the Indian Institute of Technology in Kharagpur overcame this challenge with hybrid, predictive models, using statistical methods and artificial intelligence (AI) techniques. A digital map was created, showing that half of the regions in the Ganges river delta are vulnerable to elevated arsenic exposure. Data improve the scientific understanding of groundwater containments and their risks and visualises groundwater quality.