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Incipient groundwater salinization has been identified in many arid and semi-arid regions where groundwater is increasingly used for irrigation, but the dominant processes at stake in such context are yet uncertain. Groundwater solutes originates from various sources such as atmospheric inputs, rock dissolution and fertilizer residues, and their concentration is controlled by hydrological processes, in particular evapotranspiration. Here, we propose a deconvolution method to identify the sources and processes governing the groundwater Chloride concentration in agricultural catchments, using the relative variations of Sodium and chloride and using a neighbouring pristine catchment as a reference for the release rate of Na by weathering. We applied the deconvolution method to the case of the Kabini Critical Zone Observatory, South India, where groundwater was sampled in 188 farm tubewells in the semi-arid catchment of Berambadi and in 5 piezometers in the pristine catchment of Mule Hole. In Berambadi, groundwater composition displayed a large spatial variability with Cl contents spanning 3 orders of magnitude. The results showed that the concentration factor due to evapotranspiration was on average about 3 times more than in the natural system, with higher values in the valley bottoms with deep Vertisols. Linked with this process, large concentration of Chloride originating from rain was found only in these areas. At the catchment scale, about 60 percent of the Chloride found in groundwater originates from fertilizer inputs. These results show that Potassium fertilization as KCl is an important source of groundwater salinization in semi-arid context, and stress that identifying dominant drivers is crucial for designing efficient mitigation policies.

Global groundwater assessments rank Iran among countries with the highest groundwater depletion rate using coarse spatial scales that hinder detection of regional imbalances between renewable groundwater supply and human withdrawals. Herein, we use in situ data from 12,230 piezometers, 14,856 observation wells, and groundwater extraction points to provide ground-based evidence about Iran’s widespread groundwater depletion and salinity problems. While the number of groundwater extraction points increased by 84.9% from 546,000 in 2002 to over a million in 2015, the annual groundwater withdrawal decreased by 18% (from 74.6 to 61.3 km³/y) primarily due to physical limits to fresh groundwater resources (i.e., depletion and/or salinization). On average, withdrawing 5.4 km³/y of nonrenewable water caused groundwater tables to decline 10 to 100 cm/y in different regions, averaging 49 cm/y across the country. This caused elevated annual average electrical conductivity (EC) of groundwater in vast arid/semiarid areas of central and eastern Iran (16 out of 30 subbasins), indicating “very high salinity hazard” for irrigation water. The annual average EC values were generally lower in the wetter northern and western regions, where groundwater EC improvements were detected in rare cases. Our results based on high-resolution groundwater measurements reveal alarming water security threats associated with declining fresh groundwater quantity and quality due to many years of unsustainable use. Our analysis offers insights into the environmental implications and limitations of water-intensive development plans that other water-scarce countries might adopt.

In this research, the mean monthly groundwater level with a range of 3.78 m in Qoşaçay plain, Iran, is forecast. Regarding three different layers of gated recurrent unit (GRU) structures and a hybrid of variational mode decomposition with gated recurrent unit (VMD-GRU), deep learning-based neural network models are developed. As the base model for performance comparison, the general single-long short-term memory-layer network model is developed. In all models, the module of sequence-to-one is used because of the lack of meteorological variables recorded in the study area. For modeling, 216 monthly datasets of the mean monthly water table depth of 33 different monitoring piezometers in the period April 2002-March 2020 are utilized. To boost the performance of the models and reduce the overfitting problem, an algorithm tuning process using different types of hyperparameter accompanied by a trial-and-error procedure is applied. Based on performance evaluation metrics, the total learnable parameters value and especially the model grading process, the new double-GRU model coupled with multiplication layer (×) (GRU2× model) is chosen as the best model. Under the optimal hyperparameters, the GRU2× model results in an R 2 of 0.86, a root mean square error (RMSE) of 0.18 m, a corrected Akaike's information criterion (AICc) of −280.75, a running time for model training of 87 s and a total grade (TG) of 6.21 in the validation stage; and the hybrid VMD-GRU model yields an RMSE of 0.16 m, an R 2 of 0.92, an AICc of −310.52, a running time of 185 s and a TG of 3.34.

Streamflow prediction is crucial for efficient water resource management, flood forecasting and environmental protection. This is even more important in areas particularly vulnerable to environmental changes such our study area—the Mar Menor basin in the Region of Murcia, Spain—with a specific emphasis on the Albujón watercourse, a significant contributor to the Mar Menor. Utilizing data from stream gauge stations, nearby rain gauge stations, and piezometers, our research forecasts streamflow at two critical points: “La Puebla” and “Desembocadura” along the watercourse. Targeting short-term forecasts of 1, 12, and 24 hours, our study employs Machine and Deep Learning techniques after data preprocessing, which includes station selection, data granularity adjustment, and feature selection. A state-of-the-art data augmentation technique was used to balance periods of low and high streamflow. Results show that Random Forest slightly outperforms LSTM for 1-hour forecasts (NSE > 0.89, MAE < 0.01), while Long Short Term Memory with data augmentation excels for 12 and 24-hour forecasts (NSE > 0.12, MAE < 0.05). This is noteworthy in areas with torrential rains causing rapid streamflow increases, a more challenging yet less studied scenario in forecasting. The findings contribute to addressing the challenges associated with streamflow prediction in vulnerable regions.

The interaction between groundwater and surface water plays a crucial role in determining water quality and ecological health, highlighting the need for a comprehensive understanding to ensure effective water resource management. In this study, multiple methods—seepage meters, piezometers, and the type-curve fitting method using temperature profile data—were employed to estimate fluxes at the groundwater-surface water interface of a small pond (Osong Pond) in South Korea. Measurements were conducted and compared during the wet season of 2020 (July–August 2020). Additionally, temperature data were collected during the dry seasons of 2021 and 2022 (November 2021–March 2022) to assess the applicability of the type-curve fitting method for long-term monitoring. The average vertical velocity measured by seepage meters was the highest (2.67 × 10⁻ 8  m/s), while the type-curve fitting method estimated the lowest average velocity (2.58 × 10⁻ 10  m/s). During the dry seasons of 2021–2022, the type-curve fitting method yielded an average flow velocity of 7.11 × 10⁻ 10  m/s, comparable to the dry season values of 2020. Although the lakebed temperature-based method underestimated vertical velocities in this study area, it can be effective for long-term monitoring. We recommend combining multiple measurement techniques tailored to the geological characteristics (e.g., topography and sediment composition) and climatic conditions of study sites. This integrated approach facilitates a more accurate evaluation of groundwater-surface water interactions and enhances understanding of the broader flow system.

Altering surface chemistry of functional materials is an attractive route to enable large property enhancements without sacrificing overall structural-order, appealing to diverse fields of application sciences; however, the same remains unexplored for organic crystalline materials. Herein, piezoelectricity in pharmaceutical crystals is reported to show colossal surface charges driven by mechanical fracture — where a collection of dipoles arranged in polar head-to-tail fashion generates opposite surface charges on freshly fractured faces — causing them to actuate large distances over 75 µm in milliseconds. Kelvin probe force microscopy is leveraged to show many-fold surface potential enhancement in fractured surfaces relative to the pristine crystals. Further, complementarity of the surface potentials in a pair of fractured crystal shards and asymptotic decay behaviour with time are observed. Newly formed surfaces of the pharmaceutical crystals show long-lasting charges despite their relatively lower piezo-response confirmed by bulk piezometry. To establish the generality of surface phenomena, statistical analyses (≈50 samples) of post-fracture-attraction behaviour of crystals are performed. Finally, the application of fracture-driven surface charges in industrial processes is achieved by investigating flow-property and tablet-strength of bulk pharmaceutical materials. This multiscale approach unveils the symmetry-dependency of surface charges in fractured materials, and probes the same for utilisation in bulk-property engineering. Surface charging in crystalline materials is a complex phenomenon to predict and design. Here the authors show the origin of surface charges in polar piezoelectric pharmaceuticals to predict the outcome of mechanical impact on their bulk properties.

The purpose of the paper is to assess the groundwater quality near the landfill sites using landfill water pollution index (LWPI). In order to investigate the scale of groundwater contamination, three landfills (E, H and S) in different stages of their operation were taken into analysis. Samples of groundwater in the vicinity of studied landfills were collected four times each year in the period from 2004 to 2014. A total of over 300 groundwater samples were analysed for pH, EC, PAH, TOC, Cr, Hg, Zn, Pb, Cd, Cu, as required by the UE legal acts for landfill monitoring system. The calculated values of the LWPI allowed the quantification of the overall water quality near the landfill sites. The obtained results indicated that the most negative impact on groundwater quality is observed near the old Landfill H. Improper location of piezometer at the Landfill S favoured infiltration of run-off from road pavement into the soil-water environment. Deep deposition of the groundwater level at Landfill S area reduced the landfill impact on the water quality. Conducted analyses revealed that the LWPI can be used for evaluation of water pollution near a landfill, for assessment of the variability of water pollution with time and for comparison of water quality from different piezometers, landfills or time periods. The applied WQI (Water Quality Index) can also be an important information tool for landfill policy makers and the public about the groundwater pollution threat from landfill.

An extensive network of multilevel vibrating-wire piezometers (VWP) was recently created to monitor the spatial and temporal variation of pore pressure (and hydraulic head) in the landslide-prone post-glacial marine clay slopes in Québec, Canada. Some of the VWP installations used well-sorted crushed stone as well backfill between bentonite plugs, instead of bentonite pellets or cement-bentonite grout, which could cause a bias in the hydraulic head measurements due to preferential flow within the backfill (i.e., a hydraulic short circuit). This study uses steady-state two-dimensional radial-coordinate numerical models to quantify the extent of this potential bias, and focuses on the relative importance of the following components: hydraulic conductivity of the crushed stone, length of the backfill intervals, length of the bentonite plugs, magnitude and direction of the vertical gradient, and the degree of vertical and horizontal anisotropy within the clay. Simulation results show that the use of crushed stone as backfill results in measurements of hydraulic head that differ from undisturbed conditions by ±0.25 to ±210 cm, regardless of the values assigned to the parameters of interest. In all cases, the cause of this bias is a series of hydraulic short circuits resulting from preferential flow through the crushed stone intervals.

The Buck Creek ultramafic complex is a fragment of oceanic cumulate emplaced into the lower Laurentian continental crust during Ordovician Taconic subduction. We document olivine fabrics in the relatively pristine dunites preserved from peak metamorphic conditions of ∼850°C and 1.0–1.4 GPa confining pressure. Mineral assemblages and microstructures indicate nearly anhydrous conditions at peak metamorphism and the activity of dislocation creep with minor evidence of grain boundary sliding. Grain size piezometry indicates stress conditions of ∼17–25 MPa. Analysis of crystallographic preferred orientations (CPO) and intracrystalline misorientations indicate the primary activity of the [001](010) slip system leading to the development of B‐type olivine fabrics. We suggest that the Buck Creek dunites formed as ocean crust cumulates and were partially subducted beneath the Laurentian continental crust to deformation conditions similar to those in the shallow mantle wedge. We document that deformation at Buck Creek occurred at lower differential stress conditions and lower water content than typically associated with B‐type CPOs, broadening the range of known conditions in which these fabrics may form. Plain Language Summary Olivine is the most abundant mineral in the Earth's upper mantle, and its mechanical behavior influences important processes, including plate tectonics. Previous research finds that when rocks are squeezed deep within the Earth, an alignment of olivine crystal axes, referenced as “fabric,” can be produced that varies with pressure, temperature, water content, and other factors. In this work, we document the fabric in olivine‐rich rocks of the Buck Creek ultramafic complex, a large fragment of oceanic crust exposed in the southern Appalachians. We show the development of a particular crystal fabric style (B‐type) that formed at lower stress conditions and water content than predicted by most experimental work. This finding is important because it expands our knowledge of the olivine deformation style in the natural world, informing our understanding of deep Earth processes. Key Points Cumulate dunites emplaced in Appalachian Taconic subduction preserve B‐type olivine crystallographic preferred orientations Dunites deformed under lower stress and water content than experimental B‐type conditions Olivine textures consistent with dislocation creep and minor grain‐boundary sliding

The impact of climate variability on groundwater storage has received limited attention despite widespread dependence on groundwater as a resource for drinking water, agriculture and industry. Here, we assess the climate anomalies that occurred over Southern Africa (SA) and East Africa, south of the Equator (EASE), during the major El Niño event of 2015–2016, and their associated impacts on groundwater storage, across scales, through analysis of in situ groundwater piezometry and Gravity Recovery and Climate Experiment (GRACE) satellite data. At the continental scale, the El Niño of 2015–2016 was associated with a pronounced dipole of opposing rainfall anomalies over EASE and Southern Africa, north–south of ∼12∘ S, a characteristic pattern of the El Niño–Southern Oscillation (ENSO). Over Southern Africa the most intense drought event in the historical record occurred, based on an analysis of the cross-scale areal intensity of surface water balance anomalies (as represented by the standardised precipitation evapotranspiration index – SPEI), with an estimated return period of at least 200 years and a best estimate of 260 years. Climate risks are changing, and we estimate that anthropogenic warming only (ignoring changes to other climate variables, e.g. precipitation) has approximately doubled the risk of such an extreme SPEI drought event. These surface water balance deficits suppressed groundwater recharge, leading to a substantial groundwater storage decline indicated by both GRACE satellite and piezometric data in the Limpopo basin. Conversely, over EASE during the 2015–2016 El Niño event, anomalously wet conditions were observed with an estimated return period of ∼10 years, likely moderated by the absence of a strongly positive Indian Ocean zonal mode phase. The strong but not extreme rainy season increased groundwater storage, as shown by satellite GRACE data and rising groundwater levels observed at a site in central Tanzania. We note substantial uncertainties in separating groundwater from total water storage in GRACE data and show that consistency between GRACE and piezometric estimates of groundwater storage is apparent when spatial averaging scales are comparable. These results have implications for sustainable and climate-resilient groundwater resource management, including the potential for adaptive strategies, such as managed aquifer recharge during episodic recharge events.