Abstract: More than 2.5 billion people depend upon groundwater worldwide for drinking, and giving quality water has become one of the great apprehensions of human culture. The contamination of Uranium (U) and Arsenic (As) in the groundwater of India is gaining global attention. The current review provides state-of-the-art groundwater contamination with U and As in different zones of India based on geology and soil texture. The average concentration of U in different zones of India was in the order: West Zone (41.07 μg/L) \textgreater North Zone (37.7 μg/L) \textgreater South Zone (13.5 μg/L)\textgreater Central Zone (7.4 μg/L) \textgreater East Zone (5.7 μg/L) \textgreaterSoutheast Zone (2.4 μg/L). The average concentration of As in groundwater of India is in the order: South Zone (369.7 μg/L)\textgreaterCentral Zone (260.4 μg/L)\textgreaterNorth Zone (67.7 μg/L)\textgreaterEast Zone (60.3 μg/L)\textgreaterNorth-east zone (9.78 μg/L)\textgreaterWest zone (4.14 μg/L). The highest concentration of U and As were found in quaternary sediments, but U in clay skeletal and As in loamy skeletal. Results of health risk assessment showed that the average health quotient of U in groundwater for children and adults was less than unity. In contrast, it was greater than unity for As posing a harmful impact on human health. This review provides the baseline data regarding the U and As contamination status in groundwater of India, and appropriate, effective control measures need to be taken to control this problem.

Abstract: 81Kr and 36Cl can both be used to date groundwater beyond the dating range of 14C. 81Kr usually provides reliable groundwater ages because it has uniform initial distribution and negligible subsurface generation, while 36Cl is commonly influenced by subsurface sources or “dead” chloride dissolution. Therefore, the combined use of 81Kr and 36Cl could provide clues on the evolution history of groundwater. In the present study, we performed 36Cl and 81Kr dating of geothermal water in Weihe Basin of China and interpreted the possible cause of disagreement. Two distinct water masses were identified with distinctive isotopic signals: groundwater with significant δ18O shifts (up to −2.0‰), dissolved dead Cl and ages < 1.0 Ma (Cluster A), and older water with little δ18O shifts, negligible dissolved Cl and ages >1.0 Ma (Cluster B). The results confirm the eastward flow path of Cluster B to the Ancient Sanmen Lake with an increasing trend of Cl concentration and age. Modern recharge from the mountains flows to the basin center with intense interaction between water and carbonate under respective reservoir temperatures (100 ∼ 130 °C). These waters flow through the saline stratum emerging from the spillover of the Ancient Sanmen Lake, resulting in higher dead Cl dissolution. A significant linear relationship is observed with the older end-member of ∼1.3Ma under the topographically-driven faster circulation effect. 81Kr ages seem to support the hypothesis that the birth of the modern Yellow River was at about 1.0–1.3 Ma. We inferred the drainage reorganization from the Ancient Sanmen Lake to the modern Yellow River since the Mid-Pleistocene Transition induced the change in groundwater dynamics as well as its chemical evolution. The excavation of the Ancient Sanmen Lake and the accentuated incision of the Weihe River induced groundwater gradient, and therefore the recharge from precipitation from both slopes of the Qinling Mountains in the south and the Beishan Mountains in the north. Our results highlight the effects of dead Cl on 36Cl dating and demonstrate the significant impact of catchment reorganization on groundwater dynamics and its chemistry.

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: Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management.

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.