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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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Moreau, M., & Daughney, C. (2021). Defining natural baselines for rates of change in New Zealand’s groundwater quality: Dealing with incomplete or disparate datasets, accounting for impacted sites, and merging into state of the-environment reporting. Science of The Total Environment, 755, 143292.
Abstract: To effectively manage sustainably groundwater bodies, it is essential to establish what the naturally occurring ranges of chemical concentrations in groundwaters are and how they change over time. We defined baseline trends for New Zealand groundwaters using: 1) pattern recognition techniques to deal with inconsistent monitoring suites between the national (110 sites) and the denser regional network (\textgreater1000 sites), and 2) multivariate statistics to identify and remove impacted sites from the enhanced dataset. Rates of changes were calculated for 13 parameters between January 2005 and December 2014 at more than 1000 groundwater quality monitoring sites. The resulting dataset included 262 complete cases (CC), which was enhanced using Machine-Learning (ML) techniques to a total of 607 sites. Hierarchical cluster analysis was used to identify trend clusters that were consistent between the CC, ML-enhanced datasets and a 2006 study based on solely on the national network. The largest cluster (WR) consisted of low magnitude changes across all parameters and was attributed to water-rock interaction processes. The second largest cluster (I) exhibited fast changes particularly for parameters linked to human-induced impact. The third largest cluster (D) comprised decreases of all parameters and was associated with dilution processes. Trend clusters were further refined using groundwater quality state information, enabling the identification of impacted sites outside of Cluster I in the ML-enhanced and CC datasets. Corresponding trend baselines were subsequently derived at unimpacted sites using univariate quantile distribution (5th and 95th percentile thresholds). Finally, we developed classifications combining baselines (state and trend) and natural variability to enhance state of the environment reporting. This allowed the new identification of deteriorating trends at sites where groundwater quality state is not yet affected in addition to trend reversals. These classifications can be adapted to incorporate new knowledge or align with surface water quality reporting.
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Hofmann, H., Pearce, J. K., Hayes, P., Golding, S. D., Hall, N., Baublys, K. A., et al. (2023). Multi-tracer approach to constrain groundwater flow and geochemical baseline assessments for CO2 sequestration in deep sedimentary basins. International Journal of Coal Geology, , 104438.
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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Singh, A., Patel, S., Bhadani, V., Kumar, V., & Gaurav, K. (2024). AutoML-GWL: Automated machine learning model for the prediction of groundwater level. Engineering Applications of Artificial Intelligence, 127, 107405.
Abstract: 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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Botha, R., Lindsay, R., Newman, R. T., Maleka, P. P., & Chimba, G. (2019). Radon in groundwater baseline study prior to unconventional shale gas development and hydraulic fracturing in the Karoo Basin (South Africa). Applied Radiation and Isotopes, 147, 7–13.
Abstract: 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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