For 25 years, the UK’s Environment Agency has commissioned groundwater flow models of the main aquifers in England. These regional-scale models are regularly updated, occasionally recalibrated and used for water resources management, regulatory decisions and impact assessment of groundwater abstractions.

There is a transboundary groundwater reservoir on the Polish–Ukrainian borderlands, which is of key importance in shaping strategic groundwater resources. Due to the particular importance of this reservoir, the two neighbouring countries are obliged to undertake joint actions to protect it.

Two numerical simulations using Feflow® software were conducted to demonstrate the utility of geophysical data to accurately determine groundwater levels and provide additional data to the groundwater modelling community to improve the model’s accuracy. One simulation is based on regional piezometric data, and the other uses geophysical data acquired through transient electromagnetic (TEM), electrical resistivity (ERT), and ground-penetrating radar (GPR) surveys.

Aquifer test analysis is complex, and in many regards, the interpretation resembles an art more than a science. Under the best circumstances, aquifer test analysis is still plagued by ambiguity and uncertainty, compounded by the general lack of information on the subsurface. An approach which has seen widespread adoption in other fields that need to classify time series data is machine learning. A Python script that generates numerical groundwater flow models by interfacing directly with the modelling software produces training data for deep learning.

The study focuses on the overlapping effects of low-enthalpy geothermal plants in urbanized areas, showing the importance of quantifying thermal groundwater exploitation to manage the resource adequately. Geothermal energy connects groundwater use to one of the ever-growing needs nowadays: energy. For low-temperature geothermal, the form of energy we can harness is thermal energy for building heating or cooling, one of the most polluting sectors, representing 34% of CO2 emissions in Europe.

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The Saldanha / Langebaan area is expanding at a significant rate, increasing the water demand for the area. The expansion comes from the industrial, residential and tourism sector. In addition there are economically viable deposits of silica and phosphate in the area. Ecosystem functioning in the area is also to a degree dependent on groundwater. All of these factors require an improved understanding of the geohydrology of the area. The geology of the area consists of basement Cape Granite and Malmesbury Group rocks that underlie the sediments of the Sandveld Group.

Environmental isotope and hydrochemical analyses were employed to improve existing understanding of groundwater flow dynamics in the defunct mine for assessing the source of water at a pumping shaft located near Stilfontein Town, Northwest Province, South Africa. Currently pumping is done using the shaft at an average rate of 37,000 m3 /day to prevent flooding of downstream mines.

There are various software packages used by hydrogeologists for a variety of purposes ranging from project management, database management, data interpretation, conceptual and numerical modelling and decision making. Software is either commercial (produced for sale) or open source (freely available to anyone and for any purpose).

The objective of this paper is to promote open source software that can be used by the hydrogeological community to reduce expenses, enhance productivity and maximise efficiency.