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Bresinsky, L., Kordilla, J., Hector, T., Engelhardt, I., Livshitz, Y., & Sauter, M. (2023). Managing climate change impacts on the Western Mountain Aquifer: Implications for Mediterranean karst groundwater resources. Journal of Hydrology X, 20, 100153.
Abstract: Many studies highlight the decrease in precipitation due to climate change in the Mediterranean region, making it a prominent hotspot. This study examines the combined impacts of climate change and three groundwater demand scenarios on the water resources of the Western Mountain Aquifer (WMA) in Israel and the West Bank. While commonly used methods for quantifying groundwater recharge and water resources rely on regression models, it is important to acknowledge their limitations when assessing climate change impacts. Regression models and other data-driven approaches are effective within observed variability but may lack predictive power when extrapolated to conditions beyond historical fluctuations. A comprehensive assessment requires distributed process-based numerical models incorporating a broader range of relevant physical flow processes and, ideally, ensemble model projections. In this study, we simulate the dynamics of dual-domain infiltration and precipitation partitioning using a HydroGeoSphere (HGS) model for variably saturated water flow coupled to a soil-epikarst water balance model in the WMA. The model input includes downscaled high-resolution climate projections until 2070 based on the IPCC RCP4.5 scenario. The results reveal a 5% to 10% decrease in long-term average groundwater recharge compared to a 30% reduction in average precipitation. The heterogeneity of karstic flow and increased intensity of individual rainfall events contribute to this mitigated impact on groundwater recharge, underscoring the importance of spatiotemporally resolved climate models with daily precipitation data. However, despite the moderate decrease in recharge, the study highlights the increasing length and severity of consecutive drought years with low recharge values. It emphasizes the need to adjust current management practices to climate change, as freshwater demand is expected to rise during these periods. Additionally, the study examines the emergence of hydrogeological droughts and their propagation from the surface to the groundwater. The results suggest that the 48-month standardized precipitation index (SPI-48) is a suitable indicator for hydrogeological drought emergence due to reduced groundwater recharge.
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Rusli, S. R., Weerts, A. H., Mustafa, S. M. T., Irawan, D. E., Taufiq, A., & Bense, V. F. (2023). Quantifying aquifer interaction using numerical groundwater flow model evaluated by environmental water tracer data: Application to the data-scarce area of the Bandung groundwater basin, West Java, Indonesia. Journal of Hydrology: Regional Studies, 50, 101585.
Abstract: Study Region: Bandung groundwater basin, Indonesia. Study focus: Groundwater abstraction of various magnitudes, pumped out from numerous depths in a multitude of layers of aquifers, stimulates different changes in hydraulic head distribution, including ones under vertical cross-sections. This generates groundwater flow in the vertical direction, where groundwater flows within its storage from the shallow to the underlying confined aquifers. In the Bandung groundwater basin, previous studies have identified such processes, but quantitative evaluations have never been conducted, with data scarcity mainly standing as one of the major challenges. In this study, we utilize the collated (1) environmental water tracer data, including major ion elements (Na+/K+, Ca2＋, Mg2+, Cl−, SO42−,HCO3−), stable isotope data (2H and δ18O), and groundwater age determination (14C), in conjunction with (2) groundwater flow modeling to quantify the aquifer interaction, driven mainly by the multi-layer groundwater abstraction in the Bandung groundwater basin, and demonstrate their correspondence. In addition, we also use the model to quantify the impact of multi-layer groundwater abstraction on the spatial distribution of the groundwater level changes. New hydrological insights for the region: In response to the limited calibration data availability, we expand the typical model calibration that makes use of the groundwater level observations, with in-situ measurement and a novel qualitative approach using the collated environmental water tracers (EWT) data for the model evaluation. The analysis in the study area using EWT data and quantitative methods of numerical groundwater flow modeling is found to collaborate with each other. Both methods show agreement in their assessment of (1) the groundwater recharge spatial distribution, (2) the regional groundwater flow direction, (3) the groundwater age estimates, and (4) the identification of aquifer interaction. On average, the downwelling to the deeper aquifer is quantified at 0.110 m/year, which stands out as a significant component compared to other groundwater fluxes in the system. We also determine the unconfined aquifer storage volume decrease, calculated from the change in the groundwater table, resulting in an average declining rate of 51 Mm3/year. This number shows that the upper aquifer storage is dwindling at a rate disproportionate to its groundwater abstraction, hugely influenced by losses to the deeper aquifer. The outflow to the deeper aquifer contributes to 60.3% of the total groundwater storage lost, despite representing only 32.3% of the total groundwater abstraction. This study shows the possibility of quantification of aquifer interaction and groundwater level change dynamics driven by multi-layer groundwater abstraction in a multi-layer hydrogeological setting, even in a data-scarce environment. Applying such methods can assist in deriving basin-scale groundwater policies and management strategies under the changing anthropogenic and climatic factors, thereby ensuring sustainable groundwater management.
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Marteleto, T. de P., Abreu, A. E. S. de, Barbosa, M. B., Yoshinaga-Pereira, S., Bertolo, R. A., & Enzweiler, J. (2024). Groundwater apparent ages and isotopic composition in Crystalline, Diabase and Tubarão aquifers contact area in Campinas, Southeastern Brazil. Journal of South American Earth Sciences, 135, 104783.
Abstract: This study refines the hydrogeological conceptual model of an area with three interconnected aquifers, namely the Crystalline Aquifer System (CAS – igneous and metamorphic rocks), which is in contact with the Tubarão Aquifer System (TAS – sedimentary rocks) and the Diabase Aquifer System (DAS – diabase rocks). The detailed investigation involved geophysical logging and hydraulic and hydrodynamic characterization with straddle packers in a local tubular well, in which groundwater presents high uranium concentrations. Hydrogeochemical and isotope (δ2H, δ18O, 3H, δ13C, 14C) analysis in this well and in other three neighboring wells, with lower U concentrations, showed that ancient and modern waters (3H from <0.8 to 1.12 TU, 14C from 69.43 to 78.72 pMC) mix within the aquifer. During groundwater pumping, vertical fractures in the diabase aquifer possibly induce water mixing and recharge of the deeper levels of the aquifers from shallow layers. The high [U] are related to ancient waters from a confined aquifer hosted in CAS that reaches the wells through hydraulically active fractures located deeper than 159 m depth. Groundwater apparent ages do not increase systematically with depth, revealing a complex circulation model for CAS. The results obtained from the other wells, which are all located on drainage lineaments, reveal that one extracts modern water from DAS and TAS, another one extracts modern and ancient water from DAS and CAS, and the third extracts only ancient water from CAS, confirming the complexity of the local hydrogeology. Regarding regional groundwater management, the study revealed the need to characterize the sources of groundwater in each well, in order to protect modern waters from anthropogenic contamination and to protect ancient groundwater from overexploitation, as CAS hosts groundwaters recharged thousands of years ago or more.
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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.
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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.
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