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Hofmann, H.; Pearce, J.K.; Hayes, P.; Golding, S.D.; Hall, N.; Baublys, K.A.; Raiber, M.; Suckow, A. |
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Multi-tracer approach to constrain groundwater flow and geochemical baseline assessments for CO2 sequestration in deep sedimentary basins |
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2023 |
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International Journal of Coal Geology |
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104438 |
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Keywords |
CO geological storage, Great Artesian Basin, Groundwater chemistry, Isotopic tracer, Surat Basin |
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Abstract |
Geological storage of gases will be necessary in the push to net zero and the energy transition to reduce carbon emissions to atmosphere. These include CO2 geological storage in suitable sandstone reservoirs. Understanding groundwater flow, connectivity and hydrogeochemical processes in aquifer and storage systems is vital to prevent risk and protect important water resources, such as the Great Artesian Basin. Here, we provide a ‘tool-box’ of geochemical assessment methods to provide information on flow patterns through the basin’s aquifers (changes in chemistry along flow path), stagnant versus flowing conditions (cosmogenic isotopes and noble gases), inter-aquifer connectivity and seal properties (major ions, Sr and stable isotopes), water quality (major ions and metals) and general assessments on residence times of groundwater (cosmogenic isotopes and noble gases). This information can be used with reservoir and groundwater models to inform on possible changes in the above-mentioned processes and serve as input parameters for CO2 injection impact modelling. We demonstrate the use and interpretation on an example of a potential CO2 storage geological sequestration site in the Surat Basin, part of the Great Artesian Basin, and the aquifers that overly the reservoir. The stable water isotopes are depleted compared to average rainfall and most likely indicate greater contributions from monsoonal rain events from the northern monsoonal troughs, where amount and rainout effects lead to the depletion rather than colder recharge climates. This is supported by the modern recharge temperatures from noble gases. Inter-aquifer mixing between the Precipice Sandstone reservoir and the Hutton Sandstone aquifer seems unlikely as the Sr isotope ratios are distinctly different suggesting that the Evergreen Formation is a seal in the locations sampled. Mixing, however, occurs on the edges of the basin, especially in the south-east and east where the Surat Basin transitions into the Clarence-Moreton Basin. Groundwater flow appears to be to the south in the Precipice Sandstone, with a component of flow east to the Clarence-Morton Basin. The cosmogenic isotopes and noble gases strongly indicate very long residence times of groundwater in the central south Precipice Sandstone around a proposed storage site. 14C values below analytical uncertainty, R36Cl ratios at secular equilibrium as well as high He concentrations and high 40Ar/36Ar ratios support the argument that groundwater flow in this area is extremely slow or groundwater is stagnant. The results of this study reflect the geological and hydrogeological complexities of sedimentary basins and that baseline studies, such as this one, are paramount for management strategies. |
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0166-5162 |
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THL @ christoph.kuells @ hofmann_multi-tracer_2023 |
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165 |
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Edmunds, W.M.; Shand, P.; Hart, P.; Ward, R.S. |
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Title |
The natural (baseline) quality of groundwater: a UK pilot study |
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2003 |
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Science of The Total Environment |
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310 |
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1 |
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25-35 |
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Baseline quality, Groundwater, Hydrogeochemistry, Monitoring, Water Policy |
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Knowledge of the natural baseline quality of groundwaters is an essential prerequisite for understanding pollution and for imposing regulatory limits. The natural baseline of groundwaters may show a range of concentrations depending on aquifer mineralogy, facies changes, flow paths and residence time. The geochemical controls on natural concentrations are discussed and an approach to defining baseline concentrations using geochemical and statistical tools is proposed. The approach is illustrated using a flowline from the Chalk aquifer in Berkshire, UK where aerobic and anaerobic sections of the aquifer are separately considered. The baseline concentrations for some elements are close to atmospheric values whereas others evolve through time-dependent water–rock interaction. Certain solutes (K, NH4+), often considered contaminants, reach naturally high concentrations due to geochemical controls; transition metal concentrations are generally low, although their concentrations may be modified by redox controls. It is recommended that the baseline approach be incorporated into future management strategies, notably monitoring. |
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0048-9697 |
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THL @ christoph.kuells @ edmunds_natural_2003 |
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166 |
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Uddin, M.G.; Diganta, M.T.M.; Sajib, A.M.; Hasan, M.A.; Moniruzzaman, M.; Rahman, A.; Olbert, A.I. |
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Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches |
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2023 |
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Heliyon |
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9 |
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9 |
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19668 |
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CCME index, Groundwater quality, Hydrogeochemistry, Irrigation indices, Nuclear power plant, Water quality index |
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Groundwater resources around the world required periodic monitoring in order to ensure the safe and sustainable utilization for humans by keeping the good status of water quality. However, this could be a daunting task for developing countries due to the insufficient data in spatiotemporal resolution. Therefore, this research work aimed to assess groundwater quality in terms of drinking and irrigation purposes at the adjacent part of the Rooppur Nuclear Power Plant (RNPP) in Bangladesh. For the purposes of achieving the aim of this study, nine groundwater samples were collected seasonally (dry and wet season) and seventeen hydro-geochemical indicators were analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), total organic carbon (TOC), bicarbonate (HCO3−), chloride (Cl−), phosphate (PO43−), sulfate (SO42−), nitrite (NO2−), nitrate (NO3−), sodium (Na+), potassium (K+), calcium (Ca2+) and magnesium (Mg2+). The present study utilized the Canadian Council of Ministers of the Environment water quality index (CCME-WQI) model to assess water quality for drinking purposes. In addition, nine indices including EC, TDS, TH, sodium adsorption ratio (SAR), percent sodium (Na%), permeability index (PI), Kelley’s ratio (KR), magnesium hazard ratio (MHR), soluble sodium percentage (SSP), and Residual sodium carbonate (RSC) were used in this research for assessing the water quality for irrigation purposes. The computed mean CCME-WQI score found higher during the dry season (ranges 48 to 74) than the wet season (ranges 40 to 65). Moreover, CCME-WQI model ranked groundwater quality between the “poor” and “marginal” categories during the wet season implying unsuitable water for human consumption. Like CCME-WQI model, majority of the irrigation index also demonstrated suitable water for crop cultivation during dry season. The findings of this research indicate that it requires additional care to improve the monitoring programme for protecting groundwater quality in the RNPP area. Insightful information from this study might be useful as baseline for national strategic planners in order to protect groundwater resources during the any emergencies associated with RNPP. |
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2405-8440 |
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THL @ christoph.kuells @ uddin_assessment_2023 |
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167 |
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Singh, A.; Patel, S.; Bhadani, V.; Kumar, V.; Gaurav, K. |
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AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
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2024 |
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Engineering Applications of Artificial Intelligence |
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127 |
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107405 |
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AutoML, Bayesian optimisation, Groundwater, Machine learning |
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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. |
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0952-1976 |
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THL @ christoph.kuells @ singh_automl-gwl_2024 |
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168 |
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Botha, R.; Lindsay, R.; Newman, R.T.; Maleka, P.P.; Chimba, G. |
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Radon in groundwater baseline study prior to unconventional shale gas development and hydraulic fracturing in the Karoo Basin (South Africa) |
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2019 |
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Applied Radiation and Isotopes |
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147 |
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7-13 |
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The prospect of unconventional shale gas development in the semi-arid Karoo Basin (South Africa) has created the prerequisite to temporally characterise the natural radioactivity in associated groundwater which is solely depended on for drinking and agriculture purposes. Radon (222Rn) was the primary natural radionuclide of interest in this study; however, supplementary radium (226Ra and 228Ra) in-water measurements were also conducted. A total of 53 aquifers spanning three provinces were studied during three separate measurement campaigns from 2014 to 2016. The Karoo Basin’s natural radon-in-water levels can be characterised by a minimum of 1 ± 1 Bq/L (consistent with zero or below LLD), a maximum of 183 ± 18 Bq/L and mean of 41 ± 5 Bq/L. The mean radon-in-water levels for shallow aquifers were systematically higher (55 ± 10 Bq/L) compared to deep (14 ± 3 Bq/L) or mixed aquifers (20 ± 6 Bq/L). Radon-in-water activity concentration fluctuations were predominantly observed from shallow aquifers compared to the generally steady levels of deep aquifers. A collective seasonal mean radon-in-water levels increase from the winter of 2014 (44 ± 8 Bq/L) to winter of 2016 (61 ± 16 Bq/L) was noticed which could be related to the extreme national drought experienced in 2015. Radium-in-water (228Ra and 226Ra) levels ranged from below detection level to a maximum of 0.008 Bq/L (226Ra) and 0.015 Bq/L (228Ra). The 228Ra/226Ra ratio was characterised by a minimum of 0.93, a maximum of 6.5 and a mean value of 3.3 ± 1.3. Developing and improving baseline naturally occurring radionuclide groundwater databases is vital to study potential radiological environmental impacts attributed to industrial processes such as hydraulic fracturing or mining. |
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0969-8043 |
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THL @ christoph.kuells @ botha_radon_2019 |
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169 |
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