Abstract: The quantification of groundwater resources is essential especially in water scarce countries like Namibia. The chloride mass balance (CMB) method and isotopic composition were used in determining groundwater recharge along a precipitation gradient at three sites, namely: Tsumeb (600 mm/a precipitation); Waterberg (450 mm/a precipitation) and Kuzikus/Ebenhaezer (240 mm/a precipitation). Groundwater and rainwater were collected from year 2016–2017. Rainwater was collected monthly while groundwater was collected before, during and after rainy seasons. Rainwater isotopic values for δ18O and δ2H range from −10.70 to 6.10‰ and from −72.7 to 42.1‰ respectively. Groundwater isotopic values for δ18O range from −9.84 to −5.35‰ for Tsumeb; from −10.85 to −8.60‰ for Waterberg and from −8.24 to −1.56‰ for Kuzikus/Ebenhaezer, while that for δ2H range from −65.6 to −46.7‰ for Tsumeb; −69.4 to −61.2‰ for Waterberg and −54.2 to −22.7‰ for Kuzikus/Ebenhaezer. Rainwater scatters along the GMWL. Rainwater collected in January, February and March are more depleted in heavy isotopes than those in November, December, April and May. Waterberg groundwater plots on the GMWL which indicates absence of evaporation. Tsumeb groundwater plots on/close to the GMWL with an exception of groundwater from the karst Lake Otjikoto which is showing evaporation. Groundwater from Kuzikus/Ebenhaezer shows an evaporation effect, probably evaporation occurs during infiltration since it is observed in all sampling seasons. All groundwater from three sites plot in the same area with rainwater depleted in stable isotopic values, which could indicates that recharge only take place during January, February and March. CMB method revealed that Waterberg has the highest recharge rate ranging between 39.1 mm/a and 51.1 mm/a (8.7% – 11.4% of annual precipitation), Tsumeb with rates ranging from 21.1 mm/a to 48.5 mm/a (3.5% – 8.1% of annual precipitation), and lastly Kuzikus/Ebenhaezer from 3.2 mm/a to 17.5 mm/a (1.4% – 7.3% of annual precipitation). High recharge rates in Waterberg could be related to fast infiltration and absence of evaporation as indicated by the isotopic ratios. Differences in recharge rates cannot only be attributed to the precipitation gradient but also to the evaporation rates and the presence of preferential flow paths. Recharge rates estimated for these three sites can be used in managing the savannah aquifers especially at Kuzikus/Ebenhaezer where evaporation effect is observed that one can consider rain harvesting.

Abstract: Carbon dioxide (CO2) enhanced oil recovery (EOR) provides a pathway for economic reuse and storage of CO2, a greenhouse gas. One challenge with this practice is ensuring CO2 injection does not result in target reservoir fluids migrating into overlying shallow (\textless1000 m) groundwater formations. Effective monitoring for leakage from storage formations could involve measuring sensitive chemical indicators in overlying groundwater units and within the producing formation itself for evidence of deviation from an initial state. In this study, produced waters and overlying groundwaters were monitored over a five-year period to evaluate which geochemical signals may be useful to ensure that oilfield produced waters did not impact overlying groundwaters. During this five-year period, a mature carbonate oil reservoir in the Permian Basin transitioned from a waterflooding operation to a water-alternating-gas injection (WAG), in which the formation was flooded with CO2 and various mixtures of produced water. Significant increases in dissolved inorganic constituents [alkalinity, TDS, Na+, Cl−, SO42−] were observed in produced waters following CO2 injection; however, carbonate reservoir dissolution-precipitation reactions appear to be minimal and injected CO2 appears to be stored via solubility trapping. Although there are statistically significant geochemical variations following CO2 injection, applying isometric log-ratios to certain parameters establishes a narrow range for post-CO2 injection produced waters. This narrow range can be considered a baseline for post-CO2 injection produced waters; this baseline can be utilized to monitor overlying local groundwaters for produced water intrusion. Additionally, certain parameters [Na+, Ca2+, K+, Cl−, alkalinity, and TDS] display large concentration disparities between produced water and overlying groundwaters; these parameters would be sensitive indicators of produced water intrusion into overlying groundwaters.

Abstract: A complete hydrogeological understanding of the deep Upper Jurassic carbonate aquifer in the South German Molasse Basin is essential for the future development of this important drinking water resource and geothermally used system. Water chemistry data, δ13CDIC, 14C of the dissolved inorganic carbon (14CDIC) and stable water isotope (δ18O and δD) measurements have been used to evaluate a promising groundwater dating approach with 14C of dissolved organic carbon (14CDOC). The pre-concentration of dissolved organic matter (DOM) was performed by the easy applicable solid phase extraction (SPE) with a styrene-divinylbenzene copolymer sorbent (PPL). Based on the sampling campaign of seven groundwater wells conducted between 2017 and 2019, it was shown that the groundwater is mainly of Ca–HCO3 type with some evidence of ion exchange between Ca2+ and Na+ at two of the investigated wells. The δD values ranged from −89.4‰ to −70.9‰ while δ18O values varied between −12.5‰ and −9.8‰. The obtained stable water isotope signatures indicated that the groundwater is of meteoric origin and was recharged during warm climate (Holocene), intermediate climate and cold climate (Pleistocene) infiltration conditions. The measured 14CDOC activities varied from 5.7 pmC to 51.1 pmC and the calculated piston-flow water ages (ORAs) ranged from 4200 years to 25,248 years using an initial 14C0DOC of 85 pmC. The calculated ORAs showed a very good correlation to the infiltration temperature-sensitive δ18O values which were affirmed with noble gas infiltration temperatures for two wells after Weise et al. (1991) and were also in good accordance with the atmospheric temperature record of the northern hemisphere from Dokken et al. (2015). The results reflect a consistent hydrogeological picture of the carbonate aquifer, which also supports the applicability of the SPE-PPL method for 14CDOC dating in groundwater with a low DOC content (<1 mg/l). In contrast, 14CDIC activities of 1.4 pmC to 21.3 pmC led to geochemically corrected piston-flow ages between 8057 years and >30,000 years and generally to an overestimation of the apparent water ages. This study gives insights into the promising approach of 14CDOC groundwater dating in carbonate aquifers with low DOC contents and allows future sustainable groundwater resource management of the investigated aquifer system.

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.