Abstract: Conventional continental geoarchives are rarely available in arid southern Africa. Therefore, palaeoclimate data in this area are still patchy and late Quaternary climate development is only poorly understood. In the western Kalahari, salt pans (playas, ephemeral lakes) are common and can feature quasi-continuous sedimentation. This study presents the first climate-related biomarker record using sediments from the Omongwa Pan, a Kalahari salt pan located in eastern Namibia. Our approach to reconstruct vegetation and hydrology focuses on biogeochemical bulk parameters and plant wax-derived lipid biomarkers (n-alkanes, n-alkanols, and fatty acids) and their compound-specific carbon and hydrogen isotopic compositions. The presented record reaches back to 27 ka. During the glacial, rather low δ2H values of n-alkanes and low sediment input exclude a strong influence of winter rainfall. n-Alkane and n-alkanol distributions and δ13C values of n-hentriacontane (n-C31) indicate a shift to a vegetation with a higher proportion of C4 plants at the end of the Last Glacial Maximum until the end of Heinrich Stadial I (ca. 18–14.8 ka), which we interpret to indicate an abrupt excursion to a short wetter period likely to be caused by a temporary southward shift of the Intertropical Convergence Zone. Shifts in δ2H values of n-C31 and plant wax parameters give evidence for changes to drier conditions during early Holocene. Comparison of this dataset with representative continental records from the region points to a major influence of summer rainfall at Omongwa Pan during the regarded time span and demonstrates the potential of southern African salt pans as archives for biomarker-based climate proxies.

Abstract: Water availability and quality issues will only gain importance in the future, with climate change impacts putting increasing pressure on global water resources. Dealing with these challenges requires drawing on all available water management tools, including Managed Aquifer Recharge (MAR). Although MAR has seen increasing global implementation during the last half a century, it is still often overlooked as a management tool. While technical, bio-physical, and hydrogeological aspects of MAR are well researched, this cannot be said for socio-economic and other governance factors. Where information is available, this study seeks to understand the conditions necessary for MAR success. We apply fuzzy-set Qualitative Comparative Analysis on 313 world MAR applications, and also model separately for high- and low-middle-income countries. Results show that sophisticated hydrogeological site understanding and scheme operation is paramount for MAR success, as is utilizing natural water sources for high value end uses. Successful high-income country MAR schemes tend to be large and utilize natural water sources and sophisticated water injection and treatment methods to augment potable water supply; while successful low-middle-income country schemes are not large, older than 20 years, and use gravity infiltration methods and (limited) no water treatment. These findings will help inform the future suitability of MAR application design and its likely success within various contexts.

Abstract: The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights.