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.