Groundwater Level Research Articles

Monitoring seismic velocity changes at Campi Flegrei (Italy) using seismic noise interferometry

Campi Flegrei is a volcanic field located west of Naples (Italy) in a densely populated area. Since 2005, its ground has been rising steadily due to the accumulation of fluids at shallow depths. The inflation of volcanic edifices is a possible precursor of an impending eruption. The uplift is accompanied by increasing seismic activity. This raises concerns about the possibility that the volcano may be on the verge of an eruption. To track the fluid movement, it is possible to monitor subtle changes of velocities of seismic waves by exploring ambient seismic noise. By examining different frequency bands, we can observe velocity changes at different depths. We interpret these changes as a monitoring of depth-dependent deformation in addition to the standard monitoring of surface deformation. We observe a velocity decrease in the long-term trend, presumably due to the extension of the hydrothermal system at shallow depths. To explain the long-term changes, we model a spherical pressure source to simulate volumetric strain changes induced by recent fluid activity. The model explains both, surface and subsurface deformation which leads to the opening of microcracks and pores, resulting in the observed velocity decrease. The short-term velocity changes are mainly driven by temperature or groundwater level changes. Once velocity changes are corrected for seasonal effects, remaining short term velocity changes can be associated with volcanic activity and earthquake swarms.

Analysis of Excavation Methods Against Deformation in Highway Tunnels

Cisumdawu tunnel is a highway tunnelling which composed of very weak rock mass conditions has an average compressive strength less than 1 MPa. It is located below groundwater level. The shape of tunnell is horseshoe, diameter of tunnel section 14,413 m and height 11,083 m. Cisumdawu tunnel is a shallow tunnel which has a maximum overburden of 52,8 m and minimum overburden approximately 14 m. In the case of shallow tunnel which has a large section could be lead an instability during tunnel construction. Tunnel deformation analysis is using an observation and numerical modelling approach with finite element method which is assisted by RS 2019 (Rocscience) software. Based on the research is obtained that excavation method greatly affects the level of deformation that occurs during tunnel construction. As tunnelling advanced, Vertical displacement in the longitudinal direction using three bench seven step excavation method is smaller than full face and bench excavtion method. The maximum displacement using three bench excavation method is -4.23 mm, the full face method is -5.51 mm and the bench method is -4.91 mm. The amount of displacement is affected by the length of excavation and stage in tunnelling. The rate of deformation is increasing as the length of excavation getting deeper, but this is applied in unsupported tunnel.

Drone‐towed electromagnetic and magnetic systems for subsurface characterization and archaeological prospecting

AbstractDrone‐based geophysical surveying is an emerging measuring platform. High time‐cost efficiency and flexibility to survey over inaccessible areas make drones attractive or sometimes the only feasible option to carry geophysical measurements. This study presents a new drone‐towed electromagnetic induction and magnetic gradient sensor system used for near‐surface characterization and areal mapping.The system uses various datasets to enhance processing and interpretation. The system includes; an electromagnetic induction instrument; magnetic sensors; GNSS‐IMU system; photogrammetry; Lidar model data; and geoid model data. Robust data processing and stochastic inversion subsurface characterization for archaeological prospecting with drone‐towed electromagnetic induction and magnetic gradient sensor systems. Robust statistical methods were used to process the data.We conducted the fieldwork at one of the ancient Viking settlements in Denmark. The surveyed area was approximately 100200 m. We then implemented and applied a one‐dimensional laterally constrained non‐linear stochastic inversion to image the subsurface electrical conductivity. The inversion results show a consistent conductive layer at 5–8 m depths, likely associated with the groundwater level. This conductive layer is disrupted under a prominent anomaly within a 2–4 m wide area. Our analysis showed that this conductivity disruption could be a flint mine extending 7 m deep. This anomaly also has a strong signature in magnetic gradient data.

Integration of multiple observations for validation of mechanisms of earthquake-triggered groundwater level Anomalies: 2016 Taiwan Meinong earthquake

This study analyzes multi-depth groundwater level data and integrated observation data to validate the previously proposed mechanisms of hydrological anomalies triggered by the 2016 M6.4 Meinong earthquake in Taiwan. The main influence area was northwest of the epicenter, which may be due to the blind fault rupture, intensity distribution, and hydrogeological properties. The step changes in groundwater level do not fit the concept of epicentral distance, static stress–strain theory, or the focal mechanism. The distribution of step changes in groundwater level have a pattern similar to that of horizontal peak ground velocity. The results imply that these changes may be driven by dynamic stress–strain, instead of static stress–strain. The minimum horizontal peak ground velocity and acceleration of the Meinong earthquake, which induced obvious step changes in groundwater level and soil liquefaction, are provided. The pressure dissipation ability of an aquifer (e.g., transmissivity) may affect the persistence of groundwater responses. Four wells located near the surface rupture area, which has cemented or partial cemented geological material, showed an obvious step decrease in the groundwater level at a deep depth and all wells showed an increase in the groundwater level at a shallow depth. These decreases and increases of the groundwater level at different depths have different mechanisms, which are discussed in this study. The integrated observations made during the Meinong earthquake show that ground motion and the hydraulic properties might be important factors in hydrological anomalies. The results of this study are an important reference for further studies on earthquake hydrology.

A Robust Multi-Model Framework for Groundwater Level Prediction: The BFSA-MVMD-GRU-RVM Model

Groundwater level prediction is critical for environmental protection and agricultural planning. Accurate predictions help manage risks associated with excessive groundwater extraction and land subsidence. This study introduces a novel model combining multivariate variational mode decomposition (MVMD), gated recurrent unit (GRU), and relevance vector machine (RVM), along with the Boruta feature selection algorithm (BFSA), for precise groundwater level forecasting. The model operates in several stages. First, BFSA selects the most informative features as input data. Next, the MVMD method decomposes the time series of these features. The GRU model then extracts crucial information from these decompositions. Finally, the extracted data is fed into the RVM model to predict groundwater levels one month ahead in Iran's Bastam Plain. To assess the model's accuracy, multiple error indices were employed. The MVMD-GRU-RVM model outperformed other models, improving the testing Nash–Sutcliffe Efficiency and mean absolute error by 24-31% and 6-61%, respectively. Additionally, it enhanced R2 values of the MVMD-CNN, MVMD-RNN, MVMD-MLP, and MVMD-RBFNN models by 1.0-3.33%. These results demonstrate that the MVMD-GRU-RVM model significantly improves groundwater level prediction accuracy. The key points of the paper are the successful effectiveness of MVMD in processing non-stationary time series and improving the predictive performance of the models, as well as the high ability of the GRU model in data feature extraction. The study also shows that the novel model can provide outputs with lower uncertainty.

Impact of lead and nickel contamination on metabolic health: Associations with diabetes mellitus in a pakistani cohort

Environmental exposure to heavy metals, particularly lead (Pb) and nickel (Ni), is implicated in chronic metabolic diseases, including diabetes mellitus (DM). This cross-sectional study assessed the Pb and Ni levels in groundwater using ICP-OES and urine samples collected from 2688 participants using ICP-MS. We aimed to establish the associations between Pb and Ni exposure and risk factors for DM and metabolic disorders. Groundwater analysis revealed the elevated levels of total dissolved solids, electrical conductivity, hardness, turbidity, Ni, and Pb, exceeding the WHO guidelines. The mean concentration of Pb in groundwater samples of study area was 0.025 mg/L which was higher than the WHO permissible limit of 0.01 mg/L. Similarly the mean concentration of Ni in groundwater samples of study area was 0.038 mg/L which was also higher than the WHO permissible limit of 0.02 mg/L. In human study, participants, categorized into Pb-detected and Ni-detected groups, exhibited significantly higher Pb and Ni levels and non-exposed non-diabetic groups. Ni-detected diabetics showed elevated Ni levels compared to non-exposed non-diabetics. Similarly, Pb-detected diabetics showed elevated Pb levels compared to non-exposed non-diabetics. These findings suggest a potential contribution of Pb and Ni exposure to DM development. The study also identified associations between heavy metal exposure and disruptions in various biomarkers related to DM, lipid profile, inflammation, oxidative stress, liver function, and kidney function. Pb-detected diabetics demonstrated elevated levels of glycemic index biomarkers, including fasting blood glucose (P < 0.0001) and HbA1c (P < 0.0001). Ni-detected diabetics exhibited increased inflammatory markers, such as CRP (P < 0.0001) and IL-6 (P < 0.0001). Both Pb and Ni exposure were associated with dyslipidemia, as indicated by elevated levels of total cholesterol (P < 0.0001) and LDL (P < 0.0001). Additionally, heavy metal exposure was linked to impaired liver and kidney function, supported by elevated levels of AST (P < 0.0001), ALT (P < 0.0001), creatinine (P < 0.0001), and blood urea nitrogen (P < 0.0001), with Pb exposure also associated with higher levels of MDA (P < 0.0001). Correlation analyses demonstrated significant associations between urinary Pb and Ni concentrations and various biomarkers related to DM and metabolic disorders. In conclusion, this study provides substantial evidence linking Pb and Ni exposure to the development of DM and metabolic disorders in a Pakistani population, emphasizing the need for strict regulations and preventive measures to reduce heavy metal contamination and safeguard public health. Future longitudinal studies and interventions are warranted to elucidate mechanistic links between heavy metal exposure and metabolic diseases.

Detecting active sinkholes through combination of morphometric-cluster assessment and deformation precursors

Sinkhole hazards pose considerable challenges in the west-central region of Texas. This study integrates multisource datasets and innovative techniques to detect early sinkhole development and identify the processes governing their formation. The techniques employed encompass feature extraction, cluster analysis, and deformation process monitoring. Potential sinkholes were initially mapped using high-resolution elevation data, and a circularity index (CI) threshold value of 0.85 was applied to filter out depressions with noncircular shapes. Subsequently, Persistent Scatterer Interferometry (PSInSAR) followed by the Getis-Ord Gi* statistic methods were used to identify statistically significant subsidence clusters with rates of <−2 mm/yr, indicating active sinkhole formation. The findings reveal the following: (1) surface deformation analysis revealed significant subsidence hotspots, particularly in areas underlain by units containing significant sequences of evaporites, which nominally belong to the Clear Fork Group and the Blaine Formation, compared with those dominated by carbonates; (2) potential sinkholes are undergoing displacement up to a rate of −11.31 mm/yr, and (3) extreme groundwater pumping of karst aquifers and the subsequent decline in groundwater levels are found to be the leading causes of the susceptibility of the region to sinkhole hazards. In such cases, the decline of the water table deprives the overlying bedrock of buoyant support provided by the groundwater in the cavity. The bedrock will undergo sagging subsidence under the influence of gravity and eventually form a sinkhole. Extensive oil and gas activity in the study area, including extreme withdrawal rates and the injection of fluids associated with the activity, are other potential causes of the observed sinkhole susceptibility in several parts of the study area. This study underscores the importance of understanding the geologic, hydrologic, and anthropogenic factors driving sinkhole hazards for effective mitigation strategies to protect public safety, infrastructure, and the environment.

Preliminary Assessment of Liquefaction Vulnerability using Microtremor Analysis in North Lombok

Abstract One of the disasters associated with the Mw 6.9 Lombok earthquake on August 5, 2018 was liquefaction. The liquefaction took the form of sand boil and lateral spreading, causing damage to buildings, docks, and wells in the North Lombok region. Therefore, liquefaction vulnerability assessment in this region is necessary to prevent future damage. This study presents an assessment of the liquefaction vulnerability in North Lombok using microtremor analysis. Single-station microtremor measurements were conducted at 37 sites, including around the liquefaction sites caused by the 2018 event. The soil susceptibility index (Kg ) was estimated from these recordings using the horizontal-to-vertical spectral ratio (HVSR) and compared with geological conditions and liquefaction history. A comparison of the spatial distribution between Kg values and liquefaction sites shows that almost all liquefaction events occur at Kg values greater than 6. Our results also show that liquefaction events occur mostly in coastal areas, illustrating a strong influence of groundwater level on liquefaction potential. This research finds that microtremor analysis combined with groundwater level information can adequately illustrate the liquefaction potential of an area. Therefore, this study can also identify highly liquefiable zones in North Lombok.

Very‐Near‐Field Coseismic Fault Pressure Drop and Delayed Postseismic Cross‐Fault Flow Induced by Fault Damage From the 2018 M6.3 Hualien, Taiwan Earthquake

AbstractEarthquakes can produce rock damage, poroelastic deformation, and ground shaking that modify fault zone hydrogeologic properties. Coseismic and postseismic hydrologic response to the ruptured fault can serve as constraints on hydrogeologic property changes. Here, we document fluid pressure responses to the 2018 M6.3 Hualien, Taiwan earthquake and model the postseismic fault zone hydrology inferred from very‐near‐fault data. Two groundwater wells located ∼180–250 m from the fault damage zone experienced 10–15 m of groundwater level decline followed by a prolonged (&gt;6 months) recovery. Poroelastic models indicate that strain dilation in the fault's hanging wall can produce water level reductions of several meters. However, the model cannot explain groundwater level change in the footwall nor the delayed postseismic recovery, suggesting fault zone damage played a primary role in both the coseismic and postseismic water level reductions. Fluid flow models incorporating the effects of coseismic fault damage on the temporal evolution of hydrologic fault properties show enhanced hydraulic anisotropy after the earthquake. The best‐fit models show that while both vertical hydraulic conductivity and specific storage likely increased within the fault zone, the horizontal hydraulic conductivity is low, suggesting the fault zone behaves as a hydraulic barrier to cross‐fault flow with modeled diffusivities as low as ∼10−3 m2/s. High‐fidelity hydrologic measurements in the very‐near‐field of fault zones (e.g., within a few hundred meters) may be a useful tool to constrain the spatial and temporal evolution of fault zone properties before, during, and after rupture.

Landslide risk assessment in mining areas using hybrid machine learning methods under fuzzy environment

Landslide risk assessment in coal mining economic cities faces challenges due to inadequate knowledge of indicator systems and modeling techniques, despite its crucial significance in geological disaster management. This study addresses this gap by proposing a novel framework encompassing data preparation, susceptibility analysis, consequence analysis, and final risk assessment. The primary objective is to reduce the complexity and inherent uncertainties associated with landslide risk assessment in these specific urban environments. First, two comprehensive databases for the indication system were established. One database focuses on landslide susceptibility, considering the region’s mining disturbances and disaster-prone environments. The other evaluates the effect of consequences while accounting for the pertinent risk variables. Second, a hybrid machine learning model called Geo-FR-SVM was developed by combining the GeoDetector, Frequency Ratio method, and Support Vector Machine model to improve landslide susceptibility mapping. Integrating the susceptibility map with the consequence map created using fuzzy set theory and fuzzy analytical hierarchy process methodologies resulted in the final landslide risk map. Our analysis revealed that elevation, distance to rivers and roads, groundwater level, and coal mining subsidence were the key factors influencing landslide occurrence in the Xishan mine area. The majority of the study area exhibits low landslide risk. However, higher consequence zones were primarily concentrated in highly populated residential regions and coal mine industrial zones in the east-central and western urban areas. Additionally, they are mainly distributed along the roads in the center region. With comparatively large percentages of landslide high-risk and extremely high-risk regions (4.596%, 3.855%, 3.625%, and 3.188%, respectively) at Tunlan Mine, Xiqu Mine, Zhenchengdi Mine, and Baijiazhuang Mine, these findings highlight the need for these mines to prioritize potential landslide identification and risk mitigation strategies. This framework offers broad applicability to other coal mining economic cities, serving as a valuable foundation for landslide risk management decision-making.

Assimilation of Sentinel‐Based Leaf Area Index for Modeling Surface‐Ground Water Interactions in Irrigation Districts

AbstractVegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield.

Visual MODFLOW, solute transport modeling, and remote sensing techniques for adapting aquifer potentiality under reclamation and climate change impacts in coastal aquifer

Global environmental changes, such as climate change and reclamation alterations, significantly influence hydrological processes, leading to hydrologic nonstationarity and challenges in managing water availability and distribution. This study introduces a conceptual underpinning for the rational development and sustainability of groundwater resources. As one of the areas intended for the development projects within the Egyptian national plan for the reclamation of one and a half million acres; hundreds of pumping wells were constructed in the Moghra area to fulfill the reclamation demand. This study investigates the long-term impacts of exploiting the drilled pumping wells under climate change. The approach is to monitor the groundwater levels and the salinity values in the Moghra aquifer with various operational strategies and present proposed sustainable development scenarios. The impact of global warming and climate change is estimated for a prediction period of 30 years by using satellite data, time series geographical analysis, and statistical modeling. Using MODFLOW and Solute Transport (MT3DMS) modules of Visual MODFLOW USGS 2005 software, a three-dimensional (3D) finite-difference model is created to simulate groundwater flow and salinity distribution in the Moghra aquifer with the input of forecast downscaling (2020–2050) of main climatic parameters (PPT, ET, and Temp). The optimal adaptation-integrated scenario to cope with long-term groundwater withdrawal and climate change impacts is achieved when the Ministry of Irrigation and Water Resources (MWRI) recommends that the maximum drawdown shouldn’t be more significant than 1.0 m/ year. In this scenario, 1,500 pumping wells are distributed with an equal space of 500 m, a pumping rate of 1,200 m3/day and input the forecast of the most significant climatic parameters after 30 years. The output results of this scenario revealed a drawdown level of 42 m and a groundwater salinity value of 16,000 mg/l. Climate change has an evident impact on groundwater quantity and quality, particularly in the unconfined coastal aquifer, which is vulnerable to saltwater intrusion and pollution of drinking water resources. The relationship between climate change and the hydrologic cycle is crucial for predicting future water availability and addressing water-related issues.

Assessment of the impact of greenhouse rainwater harvesting managed aquifer recharge on the groundwater system in the southern Jeju Island, South Korea: Implication from a numerical modeling approach

Managed aquifer recharge (MAR) is increasingly being adopted worldwide to mitigate groundwater depletion and ensure the sustainability of water resources. Rainwater harvesting (RWH)-MAR can augment aquifer storage and reduce flood damage in rural areas with dense greenhouse facilities. This study has assessed the feasibility of greenhouse RWH-MAR in Namwon agricultural areas in the southern part of Jeju Island, South Korea, by considering the injection rate and location of MAR using a numerical model. The model results showed that groundwater level increases were directly related to the infiltration rate, although spatial differences in head rise were observed owing to the spatial variability of hydraulic conductivity. In addition, installing the RWH-MAR in highland areas (>100 masl) enhanced the water level rise when compared to the expected values, indicating that a higher hydraulic gradient and thick unsaturated zone facilitated more effective MAR outcomes than in lowland areas. To optimize the contribution of source water to the agricultural water demand, placing the RWH-MAR near the pumping well improved the availability of injected rainwater to agricultural wells. This study highlights the importance of designing RWH-MAR schemes considering MAR objectives and the topographic and hydrogeological characteristics of the site.

Comprehensive two-dimensional analytical modeling of groundwater levels in bi-directional sloping heterogeneous aquifers under variable recharge conditions

This paper develops a comprehensive two-dimensional (2D) groundwater model that surpasses traditional one-dimensional approaches by incorporating extensive hydrological and geological data typically acquired through well drilling. The primary objective of this research is to explore and characterize the complex dynamics of groundwater flow within sloping heterogeneous aquifers subject to rainfall-induced recharge events. To achieve this, the study utilizes the 2D Boussinesq equation, which is enhanced with meticulously defined boundary conditions to more accurately reflect real-world scenarios. The Integral Transform Technique (ITT) is employed to derive an analytical solution that integrates the effects of variable rainfall recharge, aquifer inclination angles, and inherent heterogeneity. This analytical model effectively captures varying recharge dynamics, both spatially and temporally, contributing to a better understanding and management of groundwater systems. The paper presents a new analytical framework, offering deeper insights into how natural factors impact porous medium behavior, thereby providing strategic references for sustainable water resource management.

Determining the Effective Meteorological Parameters on Potato Yield in Niğde Province and Yield Prediction with Machine Learning and Deep Learning Models

In this study, we explore the impact of meteorological parameters on potato yield in Nigde province, a key agricultural region in Turkey for potato production. Understanding the relationship between weather conditions and crop yield is crucial for optimizing agricultural practices and ensuring food security, especially in regions susceptible to climate variability. The study includes all meteorological parameters observed and recorded in Nigde Directorate of Meteorology, covering the period between 1990 and 2023. Through the analysis of historical weather data and potato yield records, we aim to identify the most influential meteorological factors affecting potato production. Then, artificial intelligence techniques such as Random Forest, Gradient Boost, Convolutional Neural Networks and Recurrent Neural Networks are utilized to predict the potato yield in order to provide valuable insights into how different weather patterns influence crop performance. The findings suggest that potato yield is correlated with meteorological parameters to some degree and AI techniques can predict the potato yield but lacks the precision. The reason is that potato production in Nigde is mostly done with irrigation. However, this further increases the risk that could result from the changes in groundwater levels and pollution in irrigation ponds for agricultural practices in Nigde.

Apprentice of Ground Water Level Fluctuation using AI Models in Jammu District of Jammuand Kashmir

Apprentice of Ground Water Level Fluctuation using AI Models in Jammu District of Jammuand Kashmir

Investigation of groundwater level change and its impact on slope stability in the Sector-E of Afsin-Elbistan Open-Pit Lignite Mine in Turkey

Investigation of groundwater level change and its impact on slope stability in the Sector-E of Afsin-Elbistan Open-Pit Lignite Mine in Turkey

Urban Water Crisis in Kabul City: Key Challenges and Solutions

Water is an essential human need for survival. However, billions globally wake up daily with accessible and affordable clean water. Rapid population growth, urbanization, climate change, precipitation regime changes, industrial development, and environmental degradation increase pressure on urban water resources. As a result, water demand is continuously rising, leading to prominent shortages in many cities in developing and developed countries, regardless of their developmental condition. One such city facing significant water scarcity is Kabul, the capital of Afghanistan, where rapid urbanization has outpaced local water supply infrastructure, resulting in unsustainable exploitation of groundwater resources. This directly threatens the well-being of millions of residents in this city. In anticipation of the exhaustion of local water sources, Kabul will soon need to explore alternative water supply methods, such as inter-basin water transfers, to meet the growing demand. This paper aims to offer a broad overview of urban water crises, evaluating the key drivers of water shortages, exploring the specific water crisis facing Kabul, and analyzing previous research, reports, papers, flow data, groundwater data, maps/charts, field observations, surveys, GIS data, and statistical analysis as the methods for this work. So, to combat declining groundwater levels, a sustainable groundwater management approach is crucial. The approach includes water conservation methods, the implementation of efficient irrigation techniques, and the adoption of water pricing mechanisms.

Groundwater Level Prediction Using Machine Learning and Geostatistical Interpolation Models

Given the vulnerability of surface water to the direct impacts of climate change, the accurate prediction of groundwater levels has become increasingly important, particularly for dry regions, offering significant resource management benefits. This study presents the first statewide groundwater level anomaly (GWLA) prediction for Arizona across its two distinct aquifer types—unconsolidated sand and gravel aquifers and rock aquifers. Machine learning (ML) models were combined with empirical Bayesian kriging (EBK) geostatistical interpolation models to predict monthly GWLAs between January 2010 and December 2019. Model evaluations were based on the Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) metrics. With average NSE/R2 values of 0.62/0.63 and 0.72/0.76 during the validation and test phases, respectively, our multi-model approach demonstrated satisfactory performance, and the predictive accuracy was much higher for the unconsolidated sand and gravel aquifers. By employing a remote sensing-based approach, our proposed model design can be replicated for similar climates globally, and hydrologically data-sparse and remote areas of the world are not left out.

Infiltration of secondary treated wastewater into an oxic aquifer: Hydrochemical insights from a large-scale sand tank experiment

To mitigate groundwater level decline, managed aquifer recharge (MAR) with secondary treated wastewater (STWW) is increasingly considered and implemented. However, the effectiveness and potential risks of such systems need evaluation prior to implementation. In this study, we present a large-scale sand tank experiment to analyse processes related to the infiltration of real STWW through the vadose zone and subsequent mixing with oxic native groundwater. The varying composition of STWW from 15 infiltration cycles over six months of operation and the retention times were the main drivers of the observed processes, which were characterized by a wide range of analytical techniques such as in situ high-resolution oxidation–reduction potential (ORP) measurements, closed mass balances of solutes, characterization of dissolved organic carbon (DOC), stable nitrate isotopes analysis, as well as numerical flow and transport modelling. Depending on the composition and infiltration rates of the STWW, both nitrification and denitrification could be observed, even simultaneously at different locations in the tank. Furthermore, due to the variability of the real STWW we observed enhanced arsenic mobilisation during times of elevated phosphate concentrations of the infiltrating STWW. Additionally, uranium was mobilised in our experimental system via carbonate mineral dissolution caused by the infiltrating STWW which was undersaturated of calcite for all infiltration cycles. Overall, our results showed the importance of conducting studies with waters of complex matrix, such as real STWW, and considering mixing with groundwater to assess the full range of possible processes encountered at MAR field sites.