Abstract: During the second half of the twentieth century, northwestern New Mexico served as the primary production site for one of the world’s largest nuclear arsenals. From 1948 to 1970 the “Grants uranium district” provided almost half of the total uranium ore accumulated by the United States federal government for the production of nuclear weapons, in addition to becoming a national source for commercial nuclear energy from the late 1960s to the early 1990s. By the twenty-first century, after a prolonged period of economic decline that began in the late 1970s, all uranium mining and milling in New Mexico had ceased, leaving a legacy of environmental health impacts. What was once referred to as “The Uranium Capital of the World” now encompasses over a thousand abandoned uranium mines and seven massive uranium mill tailings piles, which are associated with airborne and soil contamination as well as groundwater plumes of uranium and other contaminants of concern, in a landscape that has been fractured by underground mine workings and punctured by thousands of exploratory boreholes. This article presents an ethnographic study of the diverse forms of expertise involved in monitoring and managing the mine waste and mill tailings. Drawing from over two years of ethnographic research, I describe the relationship between different stakeholders from local communities, government agencies, and transnational mining corporations as they deliberate about the possibility of cleaning up the former mining district. My thesis is that the possibility of cleaning up the Grants district hinges on the “politics of baselining”—a term I introduce to describe the relationship between stakeholders and their competing environmental models and hydrogeological theories; each accounts for a different geological past prior to mining that can be deemed “natural,” as the background against which to measure the anthropogenic impacts from mining.

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: 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.