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Benites Lazaro, L. L., Bellezoni, R., Puppim de Oliveira, J., Jacobi, P. R., & Giatti, L. (2022). Ten Years of Research on the Water-Energy-Food Nexus: An Analysis of Topics Evolution. Frontiers in Water, 4.
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Severi, A., Masoudian, M., Kordi, E., & Roettcher, K. (2015). Discharge coefficient of combined-free over-under flow on a cylindrical weir-gate. ISH Journal of Hydraulic Engineering, 21(1), 42–52.
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de Jong, I. J. H., Arif, S. S., Gollapalli, P. K. R., Neelam, P., Nofal, E. R., Reddy, K. Y., et al. (2021). Improving agricultural water productivity with a focus on rural transformation*. Irrigation and Drainage, 70(3), 458–469.
Abstract: ABSTRACT As a result of population growth, economic development and climate change, feeding the world and providing water security will require important changes in the technologies, institutions, policies and incentives that drive present-day water management, as captured in Goal 6.4 of the Millennium Development Goals. Irrigation is the largest and most inefficient water user, and there is an expectation that even small improvements in agricultural water productivity will improve water security. This paper argues that improvements in irrigation water productivity involves a complex and comprehensive rural transformation that goes beyond mere promotion of water saving technologies. Many of the measures to improve water productivity require significant changes in the production systems of farmers and in the support provided to them. Looking forward, water use and competition over water are expected to further increase. By 2025, about 1.8 billion people will be living in regions or countries with absolute water scarcity. Demand for water will rise exponentially, while supply becomes more erratic and uncertain, prompting the need for significant shifts of inter-sectoral water allocation to support continued economic growth. Advances in the use of remote sensing technologies will make it increasingly possible to cost-effectively and accurately estimate crop evapotranspiration from farmers’ fields.
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Stone, A. E. C., & Edmunds, W. M. (2014). Naturally-high nitrate in unsaturated zone sand dunes above the Stampriet Basin, Namibia. Journal of Arid Environments, 105, 41–51.
Abstract: Elevated groundwater nitrate levels are common in drylands, often in excess of WHO guidelines, with concern for human and animal health. In light of recent attempts to identify nitrate sources in the Kalahari this paper presents the first unsaturated zone (USZ) nitrate profiles and recharge rate estimates for the important transboundary Stampriet Basin, alongside the first rainfall chemistry records. Elevated subsurface nitrate reaches 100–250 and 250–525 mg/L NO3–N, with NO3–N/Cl of 4–12, indicating input above evapotranspiration. Chloride mass balance recharge rates range from 4 to 27 mm/y, indicating a vertical movement of these nitrate pulses toward the water table over multi-decadal timescales. These profiles are sampled from dune crests, away from high concentrations of animals and without termite mounds. Given low-density animal grazing is unlikely to contribute consistent spot-scale nitrate over decades, these profiles give an initial estimate of naturally-produced concentrations. This insight is important for the management of the Stampriet Basin and wider Kalahari groundwater. This study expands our knowledge about elevated nitrate in dryland USZs, demonstrating that it can occur as pulses, probably in response to transient vegetation cover and that it is not limited to long-residence time USZs with very limited downward moisture flux (recharge).
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Aderemi, B. A., Olwal, T. O., Ndambuki, J. M., & Rwanga, S. S. (2023). Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa. Systems and Soft Computing, 5, 200049.
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.
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