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Many engineering construction projects entail excavations into water bearing substrates. The authors explain the drainage techniques required to lower groundwater sufficiently to allow projects to be undertaken with confidence.

Predictions of groundwater fluctuations in space and time are important for sustainable water resource management. Infiltration variability on monthly to decadal timescales leads to fluctuations in the water tables and thus groundwater resources. However, connections between global-scale climate variability and infiltration patterns and groundwater are often poorly understood because the relationships between groundwater conditions and infiltration tend to be highly nonlinear. In addition, understanding is further hampered because many groundwater records are incomplete and groundwater tables are often anthropogenically influenced, which makes identifying the effects of infiltration variability difficult. Previous studies that have evaluated how infiltration variability controls groundwater are based on a limited number of point measurements. Here, we present a global assessment of how infiltration variability is expected to affect groundwater tables. We use an analytical solution derived from Richards' equation to model water level responses to idealized periodic infiltration variability with periods that range from months to decades, to approximate both the effects of short-term and long-term climate variability and thus infiltration patterns. Our global-scale assessment reveals why infiltration variability would lead to periodicity in groundwater recharge in particular regions. The vadose zone strongly dampens short-term (seasonal and shorter) variations in infiltration fluxes throughout most of Earth's land surface, while infiltration cycles exceeding 1 year would yield transient recharge, except in more arid regions. Our results may help forecasting long-term groundwater tables and could support improving groundwater resource management.

Air–water interfacial adsorption represents a major source of retention for many per‐ and poly‐fluoroalkyl substances (PFAS). Therefore, transient hydrological fluxes that dynamically change the amount of air–water interfaces are expected to strongly influence PFAS retention in their source zones in the vadose zone. We employ mathematical modeling to study how seasonal groundwater table (GWT) fluctuations affect PFAS source‐zone leaching. The results suggest that, by periodically collapsing air–water interfaces, seasonal GWT fluctuations can lead to strong temporal variations in groundwater concentration and significantly enhance PFAS leaching in the vadose zone. The enhanced leaching is more pronounced for longer‐chain PFAS, coarser‐textured porous media, drier climates, and greater amplitudes of fluctuations. GWT fluctuations and lateral migration above the GWT introduce a downgradient persistent secondary source zone for longer‐chain PFAS. However, the enhanced leaching and the secondary source zone are greatly reduced when subsurface heterogeneity is present. In highly heterogeneous source zones, GWT fluctuations may even lead to overall slower leaching due to lateral flow (in the GWT fluctuation zone and above the GWT) moving PFAS into local regions with greater retention capacities. Model simplification analyses suggest that the enhanced source‐zone leaching due to GWT fluctuations may be approximated using a static but shallower GWT. Additionally, while vertical 1D models underestimate source‐zone leaching due to not representing lateral migration, they can be revised to account for lateral migration and provide lower‐ and upper‐bound estimates of PFAS source‐zone leaching under GWT fluctuations. Overall, our study suggests that representing GWT fluctuations is critical for quantifying source‐zone leaching of PFAS, especially the more interfacially active longer‐chain compounds. Plain Language Summary Per‐ and poly‐fluoroalkyl substances (PFAS) are contaminants that are now widespread in the environment. Many PFAS are interfacially active and tend to accumulate at solid surfaces and air–water interfaces. A growing body of field data at PFAS‐contaminated sites has shown that significant amounts of PFAS have accumulated in soils, posing great risks to the groundwater underneath. Because the accumulation at air–water interfaces is a primary mechanism that retains PFAS in soils, any transient water fluxes that dynamically collapse air–water interfaces may accelerate the downward movement of PFAS. One important type of temporal change to air–water interfaces can be caused by the fluctuation of groundwater, such as seasonal groundwater table fluctuations (GWT). We use mathematical models to study the impact of GWT on enhancing the downward movement of PFAS from soils to groundwater. Our analyses suggest that seasonal GWT fluctuations can significantly enhance PFAS downward movement by periodically collapsing air–water interfaces in soils. They also lead to seasonal variations in groundwater PFAS concentrations that may have important implications for groundwater sampling and risk assessment. Overall, representing groundwater table fluctuations is critical for quantifying groundwater contamination risks of PFAS, especially the more interfacially active compounds with longer carbon chains. Key Points Groundwater table (GWT) fluctuations enhance source‐zone poly‐fluoroalkyl substances (PFAS) leaching, but the enhanced leaching is reduced in heterogeneous source zones GWT fluctuations and lateral migration above the GWT lead to a downgradient persistent secondary source zone for longer‐chain PFAS Develop simplified modeling strategies to estimate the lower‐ and upper‐bounds of PFAS source‐zone leaching under GWT fluctuations

Groundwater resources in alluvial plains play an irreplaceable role in ensuring drinking water safety, supporting agricultural and industrial production, and maintaining ecosystem stability. However, increasing heavy metal contamination and aquifer heterogeneity pose significant challenges to groundwater protection, requiring a deeper understanding of contaminant migration in heterogeneous porous media. To address this, permeability and dispersion tests were conducted on silty clay-sand mixed soils with varying sand contents (0%, 5%, 10%, 20%, and 40%) using distilled water and hexavalent chromium (Cr(VI)) solutions to analyze the migration characteristics of Cr(VI) in heterogeneous soils. Using numerical analysis software (COMSOL Mutiphysics 6.0), the migration behaviour of Cr(VI) was simulated under three scenarios: a homogeneous aquifer (Condition I), a heterogeneous aquifer (Condition II), and a heterogeneous aquifer with groundwater table fluctuations (Condition III). The results indicate that the hydraulic conductivity significantly increases with higher sand content, rising from 8.23 × 10 − 6 cm/s at 10% sand content to 1.76 × 10 − 4 cm/s at 40%, a nearly 22-fold increase. Under Cr(VI) solution infiltration, the hydraulic conductivity exhibited a maximum growth rate of 36% compared to distilled water infiltration, and its logarithmic value follows an approximately linear relationship with sand content for both infiltration types. The dispersion coefficient increases with increasing sand content, while the retardation factor decreases. Notably, both the logarithm of the dispersion coefficient and the retardation factor exhibit an approximately linear relationship with the sand content. Numerical simulations reveal breakthrough times of 9.83, 8.57, and 7.13 years for Conditions I, II, and III, respectively, with contamination plume areas following the trend: Condition II > Condition I > Condition III. Contaminant concentration and plume area exhibit cyclical variations with groundwater table fluctuations, increasing during rising groundwater tables and decreasing during falling tables.

In coal mining areas with high groundwater tables, surface subsidence has emerged as a non-negligible phenomenon, stemming from long-term coal mining activities. Employing the Huainan mining area as an exemplar, this research meticulously examines the temporal and spatial attributes of ecosystem service value (ESV) across distinct timeframes of 2005, 2010, 2015, and 2020, utilizing the refined equivalent factor approach in conjunction with spatial analysis methodologies. To delve into the primary forces driving the observed changes, the optimal parameter-based geographical detector (OPGD) model is subsequently utilized as a tool for analysis. Lastly, the study delves into the trade-offs and synergies existing between four exemplary services at the grid level, utilizing Spearman correlation coefficient and bivariate spatial autocorrelation. The findings suggest that: (1) From 2005 to 2020, the total ESV in the Huainan mining area demonstrated a general increasing tendency, primarily attributed to the increase in waters. (2) Throughout the research period, the ecosystem service functions in the coal mining area all exhibited relatively significant hydrological regulation and waste treatment capabilities. (3) Vegetation factors significantly influenced the ESV in the Huainan mining area. (4) The Huainan mining area predominantly exhibited synergistic effects among ecosystem services, with the most pronounced synergy occurring between cultural services (CS) and regulating services (RS). All services were transitioning towards an enhanced trend of synergistic effects. (5) Significant spatial variations are present in the observed trade-offs and synergies among diverse ecosystem services. The aforementioned research findings will provide scientific theoretical guidance for rational mining activities and ecological environmental governance in coal mining areas.

Scarcity of groundwater is a severe problem in this region due to over exploitation of groundwater from unconfined hard rock aquifers. The main objectives of this study are to analyse the spatiotemporal variability and fluctuation of groundwater table and to predict the location of groundwater depression pockets. Total 21 consecutive years (1996–2017) groundwater monitoring well data (pre- and post-monsoon) have been collected from CGWB, Government of India. The nonparametric Mann–Kendall trend analysis and standardized precipitation index (SPI) have been applied to detect the trend of groundwater level and rainfall variability, respectively. Exponential smoothing has also been fitted for future prediction. The pre- and post-monsoon results (1996–2017) showed that around 77% (22 stations) and 78% (23) monitoring stations were indicating declining trend of groundwater table at the rate of −0.006 to −0.205 m/year and −0.005 to −0.192 m/year, respectively. Similarly, future (2040) groundwater depression result predicted that around 75% (21) stations, the groundwater table will be depleted above 5 m during pre-monsoon while about 53% (16 stations) monitoring wells, the groundwater table will be fallen above 5 m during post-monsoon. Consequently, around 52% (15) and 50% (14) stations are being faced groundwater drought in the recurrent interval of above 2 years during pre-monsoon and post-monsoon, respectively. Driving factors of water table depletion are huge withdrawal of groundwater for dry farming and reduction of recharge areas due to rapid land use modification. The uniqueness of this study exhibits the nature of declining trend of groundwater table and identification of depression pockets.

Land-use practices can alter shallow groundwater and salinity, further impacting greenhouse gas (GHG) emissions, particularly in the hydrologically dynamic riparian zones of wetlands. Emissions of CO₂, CH₄, and N₂O were estimated in soil cores collected from two prairie pothole region (PPR) sites with three adjacent land-use practices (i. e., annual crop = AC, pasture = PA, and short rotation willow = SRW) and treated with declining water table depths (2 to 26 cm), and salinity (S0 = control, S1 = 6 mS cm⁻¹, and S2 = 12 mS cm⁻¹) in a microcosm experiment. Land-use practices significantly (p < 0.001) affected GHG emissions in soils from both sites in the order of PA > AC = SRW. Compared to the control, emissions of CO₂ and CH₄ were significantly lower under higher salinity treatments (i.e., S1 and S2), while N₂O was significantly higher (p < 0.05). Emissions under declining groundwater table depths were significantly (p < 0.001) variable and specific to each gas, indicating the impacts of shifted soil moisture regime. Overall, the CO₂ and CH₄ emissions increased up to week four and then decreased with declining water table depths, whereas N₂O emission increased up to a maximum at week six. The soils from SRW had considerably lower global warming potential compared to AC and PA. Groundwater salinity in soils from contrasting land-use in the PPR has significant impacts on GHG emissions with potential for crucial climate feedback; however, the magnitude and direction of the impacts depend on hydrology.

In the present study, a new hybridization strategy for predicting the groundwater table (GWT) and drought analysis is presented. Therefore, a hybrid of the bi-directional long short-term model (BLSTM) and the Harris hawk optimization (HHO) algorithm, namely the BLSTM–HHO algorithm, is applied. In this algorithm, the lagged data of the GWT are used as the input, whereas the current GWT data are used as the output. Additionally, the standalone BLSTM, the long short-term model (LSTM), artificial neural networks (ANN), Seasonal Autoregressive Integrated Moving Average (SARIMA), and the Autoregressive Integrated Moving Average (ARIMA) are employed as benchmark simulating algorithms. The results show that the BLSTM–HHO algorithm has more accuracy than the other investigated simulating algorithms based on the different evaluation criteria such as relative root mean squared error (RRMSE), Nash–Sutcliffe coefficient (NSE), and refined Willmott index (dr). The prediction results (from 2018 to 2022) in all three investigated aquifers show the decline of the GWT (−5.40 m for Brojen aquifer, −7.23 m for Javanmardi aquifer, and −5.81 m for Shahrekord aquifer). Accordingly, the drought analysis by the ground resource index (GRI) in the investigated areas shows that drought is expected to be continued for the next 5 years with an increasing magnitude of severity.

Accurate representation of groundwater table depth (GWTD) is crucial for simulating hydrological cycling in Earth system models (ESM). Nevertheless, there is a notable gap in the literature regarding the validation of GWTD simulations in ESMs and their subsequent impact on downstream hydrological components. This study explores the calibration of parameterization of global GWTD using machine learning within the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Despite achieving significant gains in simulating GWTD through calibration, offline ELM simulations unexpectedly show that these improvements do not translate to substantial enhancements in model performance for other key hydrological variables, including soil moisture (SM), runoff, groundwater contribution to runoff or base flow index (BFI), and evapotranspiration and its partitioning. The performance in SM and runoff was even degraded in some regions, while BFI was mostly overestimated. Although there is significant improvement in GWTD within the critical range of 1–5 m, where groundwater traditionally influences land surface energy fluxes, these improvements occurred mostly in humid areas where the impact of GWTD on surface processes is minimal. Although the impacts of model calibration are generally small in offline ELM simulations, coupled land‐atmosphere simulations exhibit much stronger responses to GWTD calibration, highlighting the role of land‐atmosphere feedbacks in Earth system modeling. These findings underscore the need for integrated calibration strategies that simultaneously optimize multiple hydrological variables. However, if a single‐variable approach is necessary, it is crucial to establish clear priorities for calibration, identifying the most critical variables that have the greatest impact on overall model performance. Plain Language Summary Accurately modeling the groundwater table depths is crucial for understanding how water moves through the environment. However, there's a research gap in evaluating how well the Earth system models perform in water table depths and how they affect other important hydrological variables like SM and runoff. In this study, machine learning was used to improve the modeling of groundwater table depth in an Earth System Model. While the improvements in water table depth were notable, they didn't consistently improve other variables. In some cases, the model's performance even worsened for certain variables. However, the improvements had a much greater impact in the coupled land‐atmosphere simulations. This suggests that feedback between land and atmosphere is important for capturing the full impact of groundwater on the climate system. Overall, the study underscores the need for a more holistic approach that considers multiple hydrological variables, rather than focusing on a single target at a time. Key Points Machine learning techniques can be used to effectively calibrate groundwater table depth (GWTD) in the E3SM Land Model Enhancements in GWTD calibration do not significantly improve surface runoff and energy fluxes in offline simulations The amplified effects of GWTD calibration in coupled versus offline simulations underscore the importance of coupled model calibration

Groundwater is a vital factor affecting the stability of water-rich slope of open-pit mine, and the distribution of groundwater inside water-rich slope is always uncertain. To investigate the uncertainty of groundwater distribution inside the water-rich slope, based on the engineering background of DEZIWA open-pit mine, this paper adopts the research method of field investigation and test (single and multiple borehole pumping tests), numerical simulation (Visual MODFOW), and theoretical analysis (nonlinear fitting theory, distribution fitting theory, and one-sample Kolmogorov–Smirnov test) to study the uncertainty of the distribution of groundwater table line within the water-rich slope of open-pit mine. Results obtained from the above research indicate that, Visual MODFLOW is an effective tool for obtaining groundwater distribution information inside the water-rich slope of open-pit mine. When the open-pit mine excavated to the final boundary, it is found that within multiple cross-sections of the southern and western regions of water-rich slope of the open-pit mine, groundwater table line can all be delineated by a series of 3-term Fourier equations, which can be characterized by identical equation forms but varying fitting coefficients. Furthermore, based on the findings of distribution fitting and one-sample Kolmogorov–Smirnov test, it becomes evident that the probability distribution of the fitting coefficients of the aforementioned 3-term Fourier equations can all be described by the normal distribution models. Use the established normal distribution models in this paper, uncertainty of the groundwater distribution within the water-rich slope of DEZIWA open-pit mine can be described indirectly and quantitatively, and the research methods of this paper can provide a meaningful reference to the slope engineering with similar conditions.