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Musy, S., & Purtschert, R. (2023). Reviewing 39Ar and 37Ar underground production in shallow depths with implications for groundwater dating. Science of The Total Environment, 884, 163868.
Abstract: Argon-37 (37Ar) and Argon-39 (39Ar) are used for groundwater dating on timescales from weeks to centuries. For both isotopes, the quantification of underground sources is essential to accurately infer water residence times from sampled dissolved activities. Subsurface production resulting from interactions with neutrons from the natural radioactivity in rocks and with primary cosmogenic neutrons has been known for a long time. More recently, the capture of slow negative muons and reactions with muon-induced neutrons were documented for 39Ar subsurface production in the context of underground particle detectors (e.g. for Dark Matter research). However, the contribution from these particles was never considered for groundwater dating applications. Here, we reevaluate the importance of all potential depth-related production channels at depth ranges relevant for 39Ar groundwater dating [0 − 200 meters below the surface (m.b.s)]. The production of radioargon by muon-induced processes is considered in this depth range for the first time. The uncertainty on the total depth-dependent production rate is estimated with Monte Carlo simulations assuming a uniform distribution of the parameter uncertainties. This work aims to provide a comprehensive framework for interpreting 39Ar activities in terms of groundwater residence times and for exposure age dating of rocks. The production of 37Ar is also addressed since this isotope is relevant as a proxy for 39Ar production, for the timing of river-groundwater exchanges, and in the context of on-site inspections (OSI) within the verification framework of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). In this perspective, we provide an interactive web-based application for the calculation of 37Ar and 39Ar production rates in rocks.
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Zhang, Y., Liu, X., Yuan, S., Song, J., Chen, W., & Dias, D. (2023). A two-dimensional experimental study of active progressive failure of deeply buried Qanat tunnels in sandy ground. Soils and Foundations, 63(3), 101323.
Abstract: As an ancient underground hydraulic engineering facility, the Qanat system has been used to draw groundwater from arid regions. A qanat is a horizontal tunnel with a slight incline that draws groundwater from a higher location and delivers it to lower agricultural land. During long-term water delivery, the qanat tunnel has experienced different degrees of aging and collapse, which may result in the significant ground settlement and even disasters. This paper developed a two-dimensional laboratory system to investigate the influence of progressive failure on the stability of deeply buried qanat tunnels. The developed system is fully instrumented with a particle image velocimetry (PIV) system and earth pressure and displacement monitoring. A special cylindrical membrane tube is designed and connected to an advanced pressure–volume controller to simulate the step-wise failure process of the tunnel. Three model tests were conducted on a dry sand considering the buried qanat tunnels at three different depths. Experimental results clearly show the progressive evolution of soil arching effect in the dry sand associated with the progressive failure of the tunnels. The failure of the Qanat ground starts from the vault and develops upwards, which is closely related to the evolution of stress contour at three consecutive stages. Ground surface settlement and volume loss corresponding to three burial depths were compared. A deeply buried qanat tunnel has a small effect on surface settlement. Earth pressure evolution on the 2D plane shows the load redistribution when the qanat collapses. The maximum arch and the initial point of the limit state correspond to a volume loss of 12.5 % and 50 %, respectively. For the collapse of the deep buried qanat tunnel, ground earth pressure evolution can be divided into a stress-increasing region, stress-decreasing region, and no redistribution region. Furthermore, a multi trap-door model considering soil expansion is proposed to describe the progressive failure behavior and its effects.
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Holmes, M., Campbell, E. E., Wit, M. de, & Taylor, J. C. (2023). Can diatoms be used as a biomonitoring tool for surface and groundwater?: Towards a baseline for Karoo water. South African Journal of Botany, 161, 211–221.
Abstract: The environmental risks from shale gas extraction through the unconventional method of ‘fracking’ are considerable and impact on water supplies below and above ground. Since 2010 the recovery of natural shale gas through fracking has been proposed in parts of the fragile semi-arid ecosystems that make up the Karoo biome in South Africa. These unique ecosystems are heavily reliant on underground water, intermittent and ephemeral springs, which are at great risk of contamination by fracking processes. Diatoms are present in all water bodies and reflect aspects of the environment in which they are located. As the possibility of fracking has not been removed, the aim of the project was to determine if diatoms could be used for rapid biomonitoring of underground and surface waters in the Karoo. Over a period of 24 months, water samples and diatom species were collected simultaneously from 65 sites. A total of 388 diatom taxa were identified from 290 samples with seasonal and substrate variation affecting species composition but not the environmental information. Species diversity information, on the other hand, often varied significantly between substrates within a single sample. Analysis using CCA established that the diatom composition was affected by lithium, oxidized nitrogen, electrical conductivity, and sulphate levels in the sampled water. We conclude that changes in diatom community composition in the Karoo do reflect the water chemistry and could be useful as bioindicators.
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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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Arya, S., & Kumar, A. (2023). Evaluation of stormwater management approaches and challenges in urban flood control. Urban Climate, 51, 101643.
Abstract: Across the globe, the damage caused by urban floods has increased manifold. The unchecked development has encroached the natural drainage, and the conventional drainage systems are inadequate in handling the augmented hydrological response. To counter this, a variety of approaches with the ability to adjust within the constraints of complex environments by managing surface runoff are being widely investigated and applied worldwide. These can put the flood water to better use, and the ecological balance may get restored. This review discusses recent progress made in the area of Green Infrastructure (GI), modelling tools that help in stormwater management, vulnerability analysis and flood risk assessment. Different ways of handling the problem are summarized through an extensive literature survey. The gaps and barriers that impede the implementation of stormwater management solutions and strategies for further improvement have also been presented. A case study of Gurugram city, India depicting the challenges being faced by urban flooding and the possible solutions through an expert survey is also presented.
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