Abstract: Water harvesting is the intentional collection and concentration of rainwater and runoff to offset irrigation demands. Secondary benefits include decreased flood and erosion risk. Water harvesting techniques include micro- and macro-catchment systems, floodwater harvesting, and rooftop and groundwater harvesting. The techniques vary with catchment type and size, and the method of water storage. Micro-catchment water harvesting, for example, requires the development of small structures and targets increased water delivery and storage to the root zone whereas macro-catchment systems collect runoff water from large areas. The sustainability of water harvesting techniques at the local level are usually constrained by several factors such as labor, construction costs, loss of productive land, and maintenance, suggesting that multiple solutions are required to sustain the benefits of water harvesting techniques.

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.

Abstract: The concentrations of U in natural waters are usually low, being typically less than 4 μg/L in river water, around 3.3 μg/L in open seawater, and usually less than 5 μg/L in groundwater. Higher concentrations can occur in both surface water and groundwater and the range spans some six orders of magnitude, with extremes in the mg/L range. However, such extremes in surface water are rare and linked to localized mineralization or evaporation in alkaline lakes. High concentrations in groundwater, substantially above the WHO provisional guideline value for U in drinking water of 30 μg/L, are associated most strongly with (i) granitic and felsic volcanic aquifers, (ii) continental sandstone aquifers especially in alluvial plains and (iii) areas of U mineralization. High-U groundwater provinces are more common in arid and semi-arid terrains where evaporation is an additional factor involved in concentrating U and other solutes. Examples of granitic and felsic volcanic terrains with documented high U concentrations include several parts of peninsular India, eastern USA, Canada, South Korea, southern Finland, Norway, Switzerland and Burundi. Examples of continental sandstone aquifers include the alluvial plains of the Indo-Gangetic Basin of India and Pakistan, the Central Valley, High Plains, Carson Desert, Española Basin and Edwards-Trinity aquifers of the USA, Datong Basin, China, parts of Iraq and the loess of the Chaco-Pampean Plain, Argentina. Many of these plains host eroded deposits of granitic and felsic volcanic precursors which likely act as primary sources of U. Numerous examples exist of groundwater impacted by U mineralization, often accompanied by mining, including locations in USA, Australia, Brazil, Canada, Portugal, China, Egypt and Germany. These may host high to extreme concentrations of U but are typically of localized extent. The overarching mechanisms of U mobilization in water are now well-established and depend broadly on redox conditions, pH and solute chemistry, which are shaped by the geological conditions outlined above. Uranium is recognized to be mobile in its oxic, U(VI) state, at neutral to alkaline pH (7–9) and is aided by the formation of stable U–CO3(±Ca, Mg) complexes. In such oxic and alkaline conditions, U commonly covaries with other similarly controlled anions and oxyanions such as F, As, V and Mo. Uranium is also mobile at acidic pH (2–4), principally as the uranyl cation UO22+. Mobility in U mineralized areas may therefore occur in neutral to alkaline conditions or in conditions with acid drainage, depending on the local occurrence and capacity for pH buffering by carbonate minerals. In groundwater, mobilization has also been observed in mildly (Mn-) reducing conditions. Uranium is immobile in more strongly (Fe-, SO4-) reducing conditions as it is reduced to U(IV) and is either precipitated as a crystalline or ‘non-crystalline’ form of UO2 or is sorbed to mineral surfaces. A more detailed understanding of U chemistry in the natural environment is challenging because of the large number of complexes formed, the strong binding to oxides and humic substances and their interactions, including ternary oxide-humic-U interactions. Improved quantification of these interactions will require updating of the commonly-used speciation software and databases to include the most recent developments in surface complexation models. Also, given their important role in maintaining low U concentrations in many natural waters, the nature and solubility of the amorphous or non-crystalline forms of UO2 that result from microbial reduction of U(VI) need improved quantification. Even where high-U groundwater exists, percentage exceedances of the WHO guideline value are variable and often small. More rigorous testing programmes to establish usable sources are therefore warranted in such vulnerable aquifers. As drinking-water regulation for U is a relatively recent introduction in many countries (e.g. the European Union), testing is not yet routine or established and data are still relatively limited. Acquisition of more data will establish whether analogous aquifers elsewhere in the world have similar patterns of aqueous U distribution. In the high-U groundwater regions that have been recognized so far, the general absence of evidence for clinical health symptoms is a positive finding and tempers the scale of public health concern, though it also highlights a need for continued investigation.

Abstract: The mining sector is at the edge of expanding to cater for natural resources that are much needed for technological development and manufacturing. Mushrooming of mines will consequently increase the number of mines closure. Moreover, mines closure have adverse impact on the environment at large and specifically on water resources. This study analyses historical groundwater quality parameters in mine intensive basin of Westrand Basin (WRB) to understand the status of groundwater quality in relation to mining activities and mine closure. Geographic information system (GIS) was used to map the spatio-temporal variation of groundwater quality in the basin and groundwater quality index (GQI) to evaluate its status. The coefficient of variation (CV) was applied to understand the stability of groundwater quality after the mine closure. Results indicated unstable and altered trend with increasing levels of acidity and salts concentration around the mines vicinity following the mine closure. The resultant maps indicated a significant deterioration of groundwater quality around the WRB with concentrations decreasing downstream. Obtained average GQI for the study period of 1996–2015 suggested a moderate groundwater quality at a range of GQI = 64–73. The CV indicated varying water quality at CV \textgreater 30% suggesting presence of source of contamination. Observed groundwater quality trends in Westrand basin suggested that mines closure present potential threat on groundwater quality and thus, a need for a robust mine closure plan and implementation.