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Musy, S., Meyzonnat, G., Barbecot, F., Hunkeler, D., Sültenfuss, J., Solomon, D. K., et al. (2021). In-situ sampling for krypton-85 groundwater dating. Journal of Hydrology X, 11, 100075.
Abstract: Krypton-85 and other radioactive noble gases are widely used for groundwater dating purposes. 85Kr analysis require large volumes of water to reach the analytical requirements. Conventionally, this water is pumped to the surface to be degassed with a gas extraction system. The large pumping rate may disturb the natural flow field and requires substantial field logistics. Hence, we propose a new in-situ degassing method, in which membrane contactors are used to degas the groundwater directly in the well and gas is collected at the surface. This way, field work is facilitated, groundwater system disturbance is minimized, and the gas sample is collected at a specific depth. We demonstrate the tightness of the system regarding atmospheric air contamination for a collection times of 24 h, which is sufficient for both low-level counting and laser-based counting methods for 85Kr. The minimal borehole diameter is 7.5 cm for the prototype presented in this research but can easily be reduced to smaller diameters. In a case study, we compare the results obtained with the new passive method with those from a conventional packer setup sampling. Additionally, 3H/3He samples were collected for both sampling regimes and the dating results were compared with those from 85Kr. A good agreement between tracer ages is demonstrated and the age stratigraphy is consistent with the expected age distribution for a porous unconfined aquifer. In addition, our study emphasizes the differences between the age information sampled with various methods. In conclusion, we demonstrate that the new in situ quasi-passive method provides a more representative age stratigraphy with depth in most cases.
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Bresinsky, L., Kordilla, J., Hector, T., Engelhardt, I., Livshitz, Y., & Sauter, M. (2023). Managing climate change impacts on the Western Mountain Aquifer: Implications for Mediterranean karst groundwater resources. Journal of Hydrology X, 20, 100153.
Abstract: Many studies highlight the decrease in precipitation due to climate change in the Mediterranean region, making it a prominent hotspot. This study examines the combined impacts of climate change and three groundwater demand scenarios on the water resources of the Western Mountain Aquifer (WMA) in Israel and the West Bank. While commonly used methods for quantifying groundwater recharge and water resources rely on regression models, it is important to acknowledge their limitations when assessing climate change impacts. Regression models and other data-driven approaches are effective within observed variability but may lack predictive power when extrapolated to conditions beyond historical fluctuations. A comprehensive assessment requires distributed process-based numerical models incorporating a broader range of relevant physical flow processes and, ideally, ensemble model projections. In this study, we simulate the dynamics of dual-domain infiltration and precipitation partitioning using a HydroGeoSphere (HGS) model for variably saturated water flow coupled to a soil-epikarst water balance model in the WMA. The model input includes downscaled high-resolution climate projections until 2070 based on the IPCC RCP4.5 scenario. The results reveal a 5% to 10% decrease in long-term average groundwater recharge compared to a 30% reduction in average precipitation. The heterogeneity of karstic flow and increased intensity of individual rainfall events contribute to this mitigated impact on groundwater recharge, underscoring the importance of spatiotemporally resolved climate models with daily precipitation data. However, despite the moderate decrease in recharge, the study highlights the increasing length and severity of consecutive drought years with low recharge values. It emphasizes the need to adjust current management practices to climate change, as freshwater demand is expected to rise during these periods. Additionally, the study examines the emergence of hydrogeological droughts and their propagation from the surface to the groundwater. The results suggest that the 48-month standardized precipitation index (SPI-48) is a suitable indicator for hydrogeological drought emergence due to reduced groundwater recharge.
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Khaneiki, M. L., Al-Ghafri, A. S., Klöve, B., & Haghighi, A. T. (2022). Sustainability and virtual water: The lessons of history. Geography and Sustainability, 3(4), 358–365.
Abstract: This article aims to show that virtual water has historically been an adaptation strategy that enabled some arid regions to develop a prosperous economy without putting pressure on their scarce water resources. Virtual water is referred to as the total amount of water that is consumed to produce goods and services. As an example, in arid central Iran, the deficiency in agricultural revenues was offset by more investment in local industries that enjoyed a perennial capacity to employ more workers. The revenues of local industries weaned the population from irrigated agriculture, since most of their raw materials and also food stuff were imported from other regions, bringing a remarkable amount of virtual water. This virtual water not only sustained the region’s inhabitants, but also set the stage for a powerful polity in the face of a rapid population growth between the 13th and 15th centuries AD. The resultant surplus products entailed a vast and safe network of roads, provided by both entrepreneurs and government. Therefore, it became possible to import more feedstock such as cocoons from water-abundant regions and then export silk textiles with considerable value-added. This article concludes that a similar model of virtual water can remedy the ongoing water crisis in central Iran, where groundwater reserves are overexploited, and many rural and urban centers are teetering on the edge of socio-ecological collapse. History holds an urgent lesson on sustainability for our today’s policy that stubbornly peruses agriculture and other high-water-demand sectors in an arid region whose development has always been dependent on virtual water.
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Sahoo, S. K., Jha, V. N., Patra, A. C., Jha, S. K., & Kulkarni, M. S. (2020). Scientific background and methodology adopted on derivation of regulatory limit for uranium in drinking water – A global perspective. Environmental Advances, 2, 100020.
Abstract: Guideline values are prescribed for drinking water to ensure long term protection of the public against anticipated potential adverse effects. There is a great public and regulatory agencies interest in the guideline values of uranium due to its complex behavior in natural aquatic system and divergent guideline values across the countries. Wide variability in guideline values of uranium in drinking water may be attributed to toxicity reference point, variation in threshold values, uncertainty within intraspecies and interspecies, resource availability, socio-economic condition, variation in ingestion rate, etc. Although guideline values vary to a great extent, reasonable scientific basis and technical judgments are essential before it could be implemented. Globally guideline values are derived considering its radiological or chemical toxicity. Minimal or no adverse effect criterions are normally chosen as the basis for deriving the guideline values of uranium. In India, the drinking water limit of 60 µg/L has been estimated on the premise of its radiological concern. A guideline concentration of 2 µg/L is recommended in Japan while 1700 µg/L in Russia. The relative merit of different experimental assumption, scientific approach and its methodology adopted for derivation of guideline value of uranium in drinking water in India and other countries is discussed in the paper.
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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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