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Gil-Márquez, J. M., Sültenfuß, J., Andreo, B., & Mudarra, M. (2020). Groundwater dating tools (3H, 3He, 4He, CFC-12, SF6) coupled with hydrochemistry to evaluate the hydrogeological functioning of complex evaporite-karst settings. Journal of Hydrology, 580, 124263.
Abstract: The hydrogeological functioning of four different areas in a complex evaporite-karst unit of predominantly aquitard behavior in S Spain was investigated. Environmental dating tracers (3H, 3He, 4He, CFC-12, SF6) and hydrochemical data were determined from spring samples to identify and characterize groundwater flow components of different residence times in the media. Results show a general geochemical evolution pattern, from higher (recharge areas) to lower positions (discharge areas), in which mineralization rises as well as the value of the rCl−/SO42−, evidencing longer water-rock interaction. Ne values show degassing of most of the samples, favored by the high salinity of groundwater and the development of karstification so that the concentration of all the considered gases were corrected according to the difference between the theoretical and the measured Ne. The presence of modern groundwater in every sample was proved by the detection of 3H and CFC-12. At the opposite, the higher amount of radiogenic 4He in most samples also indicates that they have an old component. The 3H/3He dating method does not give reliable ages as a consequence of degassing and the large uncertainty of the 3He/4He ratios of the sources for the radiogenic Helium. The large SF6 concentrations suggest terrigenic production related to halite and dolomite. Binary Mixing and Free Shape Models were created based on 3H and CFC-12 data to interpret the age distribution of the samples. Two parameters (GA50 and >70%) were proposed as an indicator of that distribution, as they provide further information than the mean age. Particularly, GA50 is derived from the median groundwater age and is presented as a new way of interpreting mixed groundwater age data. A greater fraction of old groundwater (3H and CFC-12 free) was identified in discharge areas, while the proportion and estimated infiltration date of the younger fractions in recharge areas were higher and more recent, respectively. The application of different approaches has been useful to corroborate previous theoretical conceptual model proposed for the study area and to test the applicability of the used environmental tracer in dating brine groundwater and karst springs.
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Heine, F., & Einsiedl, F. (2021). Groundwater dating with dissolved organic radiocarbon: A promising approach in carbonate aquifers. Applied Geochemistry, 125, 104827.
Abstract: A complete hydrogeological understanding of the deep Upper Jurassic carbonate aquifer in the South German Molasse Basin is essential for the future development of this important drinking water resource and geothermally used system. Water chemistry data, δ13CDIC, 14C of the dissolved inorganic carbon (14CDIC) and stable water isotope (δ18O and δD) measurements have been used to evaluate a promising groundwater dating approach with 14C of dissolved organic carbon (14CDOC). The pre-concentration of dissolved organic matter (DOM) was performed by the easy applicable solid phase extraction (SPE) with a styrene-divinylbenzene copolymer sorbent (PPL). Based on the sampling campaign of seven groundwater wells conducted between 2017 and 2019, it was shown that the groundwater is mainly of Ca–HCO3 type with some evidence of ion exchange between Ca2+ and Na+ at two of the investigated wells. The δD values ranged from −89.4‰ to −70.9‰ while δ18O values varied between −12.5‰ and −9.8‰. The obtained stable water isotope signatures indicated that the groundwater is of meteoric origin and was recharged during warm climate (Holocene), intermediate climate and cold climate (Pleistocene) infiltration conditions. The measured 14CDOC activities varied from 5.7 pmC to 51.1 pmC and the calculated piston-flow water ages (ORAs) ranged from 4200 years to 25,248 years using an initial 14C0DOC of 85 pmC. The calculated ORAs showed a very good correlation to the infiltration temperature-sensitive δ18O values which were affirmed with noble gas infiltration temperatures for two wells after Weise et al. (1991) and were also in good accordance with the atmospheric temperature record of the northern hemisphere from Dokken et al. (2015). The results reflect a consistent hydrogeological picture of the carbonate aquifer, which also supports the applicability of the SPE-PPL method for 14CDOC dating in groundwater with a low DOC content (<1 mg/l). In contrast, 14CDIC activities of 1.4 pmC to 21.3 pmC led to geochemically corrected piston-flow ages between 8057 years and >30,000 years and generally to an overestimation of the apparent water ages. This study gives insights into the promising approach of 14CDOC groundwater dating in carbonate aquifers with low DOC contents and allows future sustainable groundwater resource management of the investigated aquifer system.
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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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Külls, C. (2001). Groundwater of the North-Western Kalahari, Namibia: estimation of recharge and quantification of the flow system. Doctoral thesis, Hydrogeologie und Umwelt, .
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Patel, D., Pamidimukkala, P., & Chakraborty, D. (2024). Groundwater quality evaluation of Narmada district, Gujarat using principal component analysis. Groundwater for Sustainable Development, 24, 101050.
Abstract: In the present study, the ground water quality parameters were monitored during pre- and post-monsoon seasons across Narmada district, Gujarat, India. Monitoring was done in 89 drinking water samples collected by grid sampling method from the study area. Uranium and fluoride were analyzed along with associated parameters such as pH, dissolved oxygen, Cl−, NO3−, F−, SO42−, total alkalinity, total dissolved solids and hardness. In 4% samples the fluoride content was found to be above WHO permissible limits of 1.5 mg/L (2.36 mg/L in Undaimandava, 1.55 mg/L in Shira, 3.04 mg/L in Fatehpur and 1.83 mg/L in Dholivav) during pre-monsoon season (PRM) and 4.74 mg/L, 2.41 mg/L, 2.34 mg/L and 3.99 mg/L respectively in Bantawadi, Shira, Undai Mandava and Fatepur villages during post-monsoon (POM). The uranium level was within WHO limits in both POM and PRM seasons. The quality of the water was evaluated by Principal Component and Pearson Correlation statistical analysis techniques. The PRM and POM correlation study indicated a strong correlation of TDS with EC, Chloride, total alkalinity and bicarbonate and U while moderately strong correlation of TDS with fluoride were observed indicating that chloride, total alkalinity, bicarbonate, U and fluoride contributed to TDS and EC. Principal component analysis was applied for 14 variables, from which 3 factors were extracted during PRM and POM seasons. The extracted components, contributed 84.391% and 83.315%, to variation during PRM and POM seasons respectively. The study indicated that the analyzed water samples in Narmada district were safe for drinking purpose. However, Tilakwada tehsil groundwater was observed to be unsustainable for drinking, without further water treatment, but was appropriate for agricultural purposes. The study will help the residents of the district to understand the present water quality status and will also help in future management to protect the ground water of Narmada district.
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