Abstract: Groundwater is both an invaluable and a vulnerable resource. Aquifer resources management, aiming at the responsible exploitation and adequate protection of the groundwater resources, is therefore of key importance and has to be based on sound hydrological, environmental, economic and social principles. Aquifer-wide groundwater projects are carried out to collect the required area-specific information, to understand ongoing processes, to identify the management issues to be addressed and to develop an adequate management strategy and action plan. The quality of the project results depends to a large extent on the science and methodologies adopted in the design and used during the implementation of the projects. In this context, a project was carried out recently to analyse the scientific aspects of—among others—the transboundary aquifer projects within the IW: Portfolio of the Global Environmental Facility (GEF) and to make recommendations for scientific strengthening and innovation. This paper presents the main outcomes of this analysis. In order to accomplish groundwater resources management goals in the case of transboundary aquifers, a balanced joint strategy is needed. Analysis of documentation on completed and on-going transboundary aquifer projects has shown a wide range of scientific activities that contribute positively to the development of such strategies. This analysis has also identified options for increasing the positive impacts of science on strategy development; some of these options have been pioneered already and deserve wider application other ones are relatively new. Important options are: integrating transboundary aquifer resource management in a wider environmental–socio-economical context (holistic approach); exploring causal chains to better understand the processes of change of groundwater resources; using this improved understanding for optimising groundwater assessment and monitoring programmes; and adaptive management. In addition, to obtain maximum benefit of the scientific results there is a general need to promote effective communication at all levels, between the scientific community and policy-/decision makers, as well as with the local community who have a major role to play in the use and conservation of the resources. All of this should be accompanied by the harmonisation of the legal instruments and co-operation agreements between countries and the communities involved. Two case studies, one in South America and one in Southern Africa, are added as examples of the setting and approach of the analysed transboundary aquifer projects.

Abstract: Kalahari sands which cover a large part of Southern Africa and extend into Central Africa are infertile and marginal soils for intensive agriculture. Therefore, high nitrogen fertilisation rates may degrade ecosystems of rivers with catchments covered by the Kalahari sands. A study on Mashare Farm located in the Okavango River basin showed that irrigated Kalahari sandy soils had a nitrate retention capacity, which enabled the soil to resist nitrate leaching in water saturated conditions. The irrigated soils were modified by agricultural activities; hence this study investigated if uncultivated and cultivated Kalahari sand soils had similar nitrate retention properties. The elementary composition of the soils was investigated for obtaining an insight into chemical properties that may be causing the nitrate retention capacity. A permeameter was used to leach out nitrates from irrigated and uncultivated soil samples, and nitrate concentrations were measured on the leaching effluent from the permeameter. Elemental analysis was done on the cultivated and the uncultivated soil samples using a Scanning Electron Microscope, a portable X-Ray Fluorescence analyzer, and an X-Ray Diffraction machine, and the later was also used for crystalline structure analyses. Sieve analyses confirmed that the Mashare’s cultivated and uncultivated topsoils were similar, and both were similar to Botswana Kalahari topsoil. The irrigated and cultivated subsoil had a higher average nitrate retention capacity of 76% compared to 73% for the uncultivated subsoil. Both samples had the same elements, although the proportions were different. Both soil samples were dominated by a quartz mineral, but the field soil had traces of palygorskite. The presence of aluminum and transition metals outside the minerals structure, but as coatings on the quartz sand grains enhanced nitrate retention capacity properties of the Kalahari sand soils.

Abstract: The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.