|
|
Musy, S., Meyzonnat, G., Barbecot, F., Hunkeler, D., Sültenfuss, J., Solomon, D. K., et al. (2021). In-situ sampling for krypton-85 groundwater dating. Journal of Hydrology X, 11, 100075.
Abstract: Krypton-85 and other radioactive noble gases are widely used for groundwater dating purposes. 85Kr analysis require large volumes of water to reach the analytical requirements. Conventionally, this water is pumped to the surface to be degassed with a gas extraction system. The large pumping rate may disturb the natural flow field and requires substantial field logistics. Hence, we propose a new in-situ degassing method, in which membrane contactors are used to degas the groundwater directly in the well and gas is collected at the surface. This way, field work is facilitated, groundwater system disturbance is minimized, and the gas sample is collected at a specific depth. We demonstrate the tightness of the system regarding atmospheric air contamination for a collection times of 24 h, which is sufficient for both low-level counting and laser-based counting methods for 85Kr. The minimal borehole diameter is 7.5 cm for the prototype presented in this research but can easily be reduced to smaller diameters. In a case study, we compare the results obtained with the new passive method with those from a conventional packer setup sampling. Additionally, 3H/3He samples were collected for both sampling regimes and the dating results were compared with those from 85Kr. A good agreement between tracer ages is demonstrated and the age stratigraphy is consistent with the expected age distribution for a porous unconfined aquifer. In addition, our study emphasizes the differences between the age information sampled with various methods. In conclusion, we demonstrate that the new in situ quasi-passive method provides a more representative age stratigraphy with depth in most cases.
|
|
|
|
Heine, F., & Einsiedl, F. (2021). Groundwater dating with dissolved organic radiocarbon: A promising approach in carbonate aquifers. Applied Geochemistry, 125, 104827.
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.
|
|
|
|
Musy, S., & Purtschert, R. (2023). Reviewing 39Ar and 37Ar underground production in shallow depths with implications for groundwater dating. Science of The Total Environment, 884, 163868.
Abstract: Argon-37 (37Ar) and Argon-39 (39Ar) are used for groundwater dating on timescales from weeks to centuries. For both isotopes, the quantification of underground sources is essential to accurately infer water residence times from sampled dissolved activities. Subsurface production resulting from interactions with neutrons from the natural radioactivity in rocks and with primary cosmogenic neutrons has been known for a long time. More recently, the capture of slow negative muons and reactions with muon-induced neutrons were documented for 39Ar subsurface production in the context of underground particle detectors (e.g. for Dark Matter research). However, the contribution from these particles was never considered for groundwater dating applications. Here, we reevaluate the importance of all potential depth-related production channels at depth ranges relevant for 39Ar groundwater dating [0 − 200 meters below the surface (m.b.s)]. The production of radioargon by muon-induced processes is considered in this depth range for the first time. The uncertainty on the total depth-dependent production rate is estimated with Monte Carlo simulations assuming a uniform distribution of the parameter uncertainties. This work aims to provide a comprehensive framework for interpreting 39Ar activities in terms of groundwater residence times and for exposure age dating of rocks. The production of 37Ar is also addressed since this isotope is relevant as a proxy for 39Ar production, for the timing of river-groundwater exchanges, and in the context of on-site inspections (OSI) within the verification framework of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). In this perspective, we provide an interactive web-based application for the calculation of 37Ar and 39Ar production rates in rocks.
|
|
|
|
Aderemi, B. A., Olwal, T. O., Ndambuki, J. M., & Rwanga, S. S. (2023). Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa. Systems and Soft Computing, 5, 200049.
Abstract: 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.
|
|
|
|
Jing, M., Kumar, R., Attinger, S., Li, Q., Lu, C., & Heße, F. (2021). Assessing the contribution of groundwater to catchment travel time distributions through integrating conceptual flux tracking with explicit Lagrangian particle tracking. Advances in Water Resources, 149, 103849.
Abstract: Travel time distributions (TTDs) provide an effective way to describe the transport and mixing processes of water parcels in a subsurface hydrological system. A major challenge in characterizing catchment TTD is quantifying the travel times in deep groundwater and its contribution to the streamflow TTD. Here, we develop and test a novel modeling framework for an integrated assessment of catchment scale TTDs through explicit representation of 3D-groundwater dynamics. The proposed framework is based on the linkage between a flux tracking scheme with the surface hydrologic model (mHM) for the soil-water compartment and a particle tracking scheme with the 3D-groundwater model OpenGeoSys (OGS) for the groundwater compartment. This linkage provides us with the ability to simulate the spatial and temporal dynamics of TTDs in these different hydrological compartments from grid scale to regional scale. We apply this framework in the Nägelstedt catchment in central Germany. Simulation results reveal that both shape and scale of grid-scale groundwater TTDs are spatially heterogeneous, which are strongly dependent on the topography and aquifer structure. The component-wise analysis of catchment TTD shows a time-dependent sensitivity of transport processes in soil zone and groundwater to driving meteorological forcing. Catchment TTD exhibits a power-law shape and fractal behavior. The predictive uncertainty in catchment mean travel time is dominated by the uncertainty in the deep groundwater rather than that in the soil zone. Catchment mean travel time is severely biased by a marginal error in groundwater characterization. Accordingly, we recommend to use multiple summary statistics to minimize the predictive uncertainty introduced by the tailing behavior of catchment TTD.
|
|
