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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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Title |
Multi-tracer approach to constrain groundwater flow and geochemical baseline assessments for CO2 sequestration in deep sedimentary basins |
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Journal Article |
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Year |
2023 |
Publication |
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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Author |
Mekuria, W.; Tegegne, D. |
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Title |
Water harvesting |
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Book Chapter |
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Year |
2023 |
Publication |
Encyclopedia of Soils in the Environment (Second Edition) |
Abbreviated Journal |
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593-607 |
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Climate change, Ecosystem services, Environmental benefits, Population growth, Resilient community, Resilient environment, Socio-economic benefits, Urbanizations, Water harvesting, Water quality, Water security |
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Water harvesting is the intentional collection and concentration of rainwater and runoff to offset irrigation demands. Secondary benefits include decreased flood and erosion risk. Water harvesting techniques include micro- and macro-catchment systems, floodwater harvesting, and rooftop and groundwater harvesting. The techniques vary with catchment type and size, and the method of water storage. Micro-catchment water harvesting, for example, requires the development of small structures and targets increased water delivery and storage to the root zone whereas macro-catchment systems collect runoff water from large areas. The sustainability of water harvesting techniques at the local level are usually constrained by several factors such as labor, construction costs, loss of productive land, and maintenance, suggesting that multiple solutions are required to sustain the benefits of water harvesting techniques. |
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Academic Press |
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Oxford |
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Goss, M.J.; Oliver, M. |
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978-0-323-95133-3 |
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THL @ christoph.kuells @ Mekuria2023593 |
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225 |
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Mekuria, W.; Tegegne, D. |
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Title |
Water harvesting |
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Book Chapter |
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2023 |
Publication |
Encyclopedia of Soils in the Environment (Second Edition) |
Abbreviated Journal |
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593-607 |
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Climate change, Ecosystem services, Environmental benefits, Population growth, Resilient community, Resilient environment, Socio-economic benefits, Urbanizations, Water harvesting, Water quality, Water security |
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Abstract |
Water harvesting is the intentional collection and concentration of rainwater and runoff to offset irrigation demands. Secondary benefits include decreased flood and erosion risk. Water harvesting techniques include micro- and macro-catchment systems, floodwater harvesting, and rooftop and groundwater harvesting. The techniques vary with catchment type and size, and the method of water storage. Micro-catchment water harvesting, for example, requires the development of small structures and targets increased water delivery and storage to the root zone whereas macro-catchment systems collect runoff water from large areas. The sustainability of water harvesting techniques at the local level are usually constrained by several factors such as labor, construction costs, loss of productive land, and maintenance, suggesting that multiple solutions are required to sustain the benefits of water harvesting techniques. |
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Academic Press |
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Oxford |
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Goss, M.J.; Oliver, M. |
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978-0-323-95133-3 |
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THL @ christoph.kuells @ Mekuria2023593 |
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265 |
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Author |
Zwartendijk, B.W.; Ghimire C. P.; Ravelona M.; Lahitiana J.; van Meerveld H. J. |
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Title |
Hydrometric data and stable isotope data for streamflow and rainfall in the Marolaona catchment, Madagascar, 2015-2016 |
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Miscellaneous |
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2023 |
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NERC EDS Environmental Information Data Centre |
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THL @ christoph.kuells @ ref10.5285/f93d87ed-7bc4-4d03-9690-3856e6cbbd11 |
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289 |
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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
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Title |
Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
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Journal Article |
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2023 |
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Systems and Soft Computing |
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5 |
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200049 |
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Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series |
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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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2772-9419 |
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THL @ christoph.kuells @ Aderemi2023200049 |
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219 |
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