Aquifer Salinization Research Articles

Hydrochemical Characteristics and Spatial Variability in Coastal Aquifers Southern IraqUtilizing a GIS Technique

The present study investigates water quality regarding drinking and irrigation supplies by gathering and analyzing twelve groundwater samples south of Basrah, south Iraq. Many wells in the area concerned are exploited mainly for various industrial and agricultural activities, impacting the ground water quality. The standard guidelines recommended by the Iraqi Quality Standard limits and the World Health Organization have been used to determine the suitability of drinking. The physicochemica analyses showed that all sampled waters are unsuitable for the drinking supply due to high total dissolved solids (TDS) levels reaching 12925 mg/L. Four valuable indices have been applied for estimating the irrigated water quality, namely, the sodium adsorption ratio (SAR), the soluble sodium percentage (SSP%), the magnesium hazard (MH%), and Kelly’s ratio (KR%). The results of water class as regards the percent of samples showed: SAR results, 25% excellent water, 41.7% good water, and 33.3% doubtful water; SSP% results, 16.7 % good water, 75% permissible water, and 8.3 % doubtful water; MH% results, 100% suitable irrigated water; and KR% results show 41.7% suitable water and 58.3 % unsuitable water. The USSL salinity diagram (Wilcox) result revealed 83.3 % of samples fall in very high salinity-very high sodium hazard class C4S4, and 16.7 % of samples fall in very high salinity-high sodium hazard class C4S3 and very high salinity-medium sodium hazard class C4S2, respectively. Increasing salinity of coastal aquifers has impacted sources such as seawater intrusion and intensive irrigation, and usages of excessive fertilizers and herbicides can largely pollute the groundwater quality.

Beyond the Wedge: Impact of Tidal Streams on Salinization of Groundwater in a Coastal Aquifer Stressed by Pumping and Sea‐Level Rise

AbstractSaltwater intrusion (SWI) is a well‐studied phenomenon that threatens the freshwater supplies of coastal communities around the world. The development and advancement of numerical models has led to improved assessment of the risk of salinization. However, these studies often fail to include the impact of surface waters as potential sources of aquifer salinity and how they may impact SWI. Based on field‐collected data, we developed a regional, variable‐density groundwater model using SEAWAT for east Dover, Delaware. In this location, major users of groundwater from the surficial aquifer are the City of Dover and irrigation for agriculture. Our model includes salinized marshland and tidal streams, along with irrigation and municipal pumping wells. Model scenarios were run for 100 years and included changes in pumping rates and sea‐level rise (SLR). We examined how these drivers of SWI affect the extent and location of salinization in the surficial aquifer by evaluating differences in chloride concentration near surface waters and the subsurface freshwater‐saltwater interface. We found the presence of the marsh inverts the typical freshwater‐saltwater wedge interface and that the edge of the interface did not migrate farther inland. Additionally, we found that tidal streams are the dominant pathways of SWI at our site with salinization from streams being exacerbated by SLR. Our results also show that spatial distribution of pumping affects both the magnitude and extent of salinization, with an increase in concentrated pumping leading to more intensive salinization than a more widely distributed increase of the same total pumping volume.

A comparison of sea-level rise and storm-surge overwash effects on groundwater salinity of a barrier island

Coastal fresh groundwater is threatened by salinization due to both sea-level rise and storm-surge overwash. Most studies of subsurface saltwater intrusion focus on sea-level rise, but storms that are becoming more frequent and intense with climate change could have more imminent and permanent effects on aquifer salinity. Few studies directly compare saltwater intrusion due to sea-level rise with saltwater intrusion due to storm-surge overwash, and no studies address this issue on barrier islands. In this study, a hydrological model simulating coupled, variable-density, surface and subsurface flow and salt transport was developed and calibrated to water level and specific conductance data at Assateague Island, MD. The calibrated model was used to calculate the mass of salt and the total volume of aquifer salinized in 2080 due to both sea-level rise and more frequent storm surges. The results suggest that the total mass of salt that intrudes into the aquifer due to changes in the 2-year storm surge height is approximately equal to that of sea-level rise (∼300 kg), and the surges salinize nearly the entire aquifer, exceeding the volume salinized by sea-level rise (∼80 %). The influence of aquifer properties and unsaturated zone thickness was investigated by reducing aquifer hydraulic conductivity, resulting in greater salinization from storm-surge overwash (100 % of aquifer volume) than from sea-level rise (∼20 %). Higher storm surges are expected to overtop the dunes on Assateague by 2080 causing a 75 % decline in areas above the 2-year storm event while sea-level rise will only inundate ∼ 40 % of the land area on Assateague. This analysis suggests that groundwater is likely more vulnerable to storm-surge overwash than to sea-level rise induced salinization, even in systems where sea-level rise is expected to be most severe.

Optimizing Irrigation Systems for Water Efficiency and Groundwater Sustainability in the Coastal Nile Delta

This study investigates the replacement of traditional surface irrigation methods with modern irrigation systems (MIS) including horizontal sprinkler, central pivot, surface drip, and subsurface drip aimed at improving water efficiency in the Nile Delta, Egypt. The primary objectives were to determine the optimal agricultural area for implementing MIS and to assess the effects of these systems on groundwater quantity and quality in the region. To achieve this, the LINDO software was employed to optimize land allocation for each irrigation method. At the same time, the SEAWAT code was utilized to simulate saltwater intrusion (SWI) in the Nile Delta aquifer. The transition from traditional surface irrigation to MIS resulted in significant water savings, reaching 2.15 × 10^9 m³. However, groundwater modeling indicated a decrease in groundwater levels, leading to an 8 % increase in aquifer salinity due to reduced infiltration of recharge water. These findings underscore the urgent need to revise outdated irrigation practices and enhance water management strategies in the Nile Delta to mitigate salinity issues in coastal aquifers. This research's outcomes are crucial for decision-makers and stakeholders in selecting appropriate irrigation methods, particularly in arid and semi-arid regions, to ensure sustainable water use and agricultural productivity.

Checklist and distribution of the groundwater crustacean fauna from Sicily, Italy

Owing to the “Racovitzan impediment”, the groundwater fauna of most biogeographical regions is currently inadequately known, thus hampering our understanding of subterranean biodiversity and its protection. Based on an extensive bibliographical review accompanied by fieldwork to localize occurrence sites, a checklist of crustacean taxa reported to date from Sicilian groundwater is provided, and their distribution is described. Among the 63 taxa recorded to date, 43 belong to the class Copepoda (orders Cyclopoida and Harpacticoida), 15 to the class Malacostraca (orders Amphipoda, Bathynellacea, Isopoda, and Thermosbaenacea), and 5 to the class Ostracoda (order Podocopida). Conversely, to date, no representatives of the copepod order Calanoida nor species of the class Branchiopoda have been recorded from groundwater habitats on the island. Several taxa require accurate taxonomic revision or are yet to be formally described and are thus at present left in open nomenclature. Finally, the date of publication of several copepod and amphipod taxa is amended. A high incidence of stygobites (i.e., obligate groundwater dwellers) has been observed in malacostracans, whereas nearly half of the recorded copepods were non-stygobites. This pattern is mirrored by the incidence of endemic species, which is higher in malacostracans than in copepods. The only non-stygobitic crustacean species endemic to Sicily observed in the frame of present review is the asellid isopod Proasellus montalentii. The paucity of information currently available on the Sicilian groundwater ostracods prevents us from drawing conclusions regarding this crustacean group. The origin and composition of Sicilian groundwater crustacean fauna can be explained by considering three major faunal assemblages: the presence of ancient paleoendemic taxa, likely of Miocene origin, the colonization of the groundwater of the island during late Pliocene and Pleistocene land connections with peninsular Italy, and the direct colonization of these environments from the sea; no species of African origin have been discovered to date. Based on currently available data, the groundwater of southeastern Sicily hosts the highest species richness and some of the most biogeographically interesting taxa. Unfortunately, a progressive lowering and salinization of the local aquifers possibly due to climate change and its overexploitation threats this fauna, and several taxa are disappearing even before their discovery and description.

Incorporating hydraulic gradient and pumping rate into GALDIT framework to assess groundwater vulnerability to salinity in coastal aquifers: a case study from Urmia Plain, Iran.

The critical role of groundwater in meeting diverse needs, including drinking, industrial, and agricultural, highlights the urgency of effective resource management. Excessive groundwater extraction, especially in coastal regions including Urmia Plain in NW Iran, disrupts the equilibrium between freshwater and saline boundaries within aquifers. Influential parameters governing seawater intrusion-groundwater occurrence (G), aquifer hydraulic conductivity (A), the height of groundwater level above the mean sea level (L), distance from the shore (D), impact of the existing status of seawater intrusion (I), and thickness of the saturated aquifer (T)-merge to shape the GALDIT vulnerability index for coastal aquifers. This study enriches the GALDIT framework by incorporating two additional hydrogeological variables: hydraulic gradient (i) and pumping rate (P). This expansion produces seven distinct vulnerability maps (GALDIT, GAiDIT, GAiDIT-P, GALDIT-i, GALDIT-iP, GALDIT-P, GAPDIT). In the Urmia Plain, the traditional GALDIT index reveals vulnerability values ranging from 2 to 8.1, categorized into six classes from negligible to very high vulnerability. However, the modified indices, GAiDIT and GAiDIT-P, yield a three-class categorization, ranging from low to high vulnerability. The introduction of the "i" and "P" parameters in GALDIT-i and GALDIT-iP enhances the precision of vulnerability mapping, altering class distribution and intensifying vulnerability ratings. The eastern, central, and coastal areas of the Urmia Plain demonstrate high to very high vulnerability levels, in contrast to the lower vulnerability observed in the western regions. Both the GALDIT-P (r = 0.82) and GALDIT-iP (r = 0.81) indices show strong correlations with Cl concentration, thereby improving mapping accuracy over the traditional GALDIT index (r = 0.72). A sensitivity analysis highlights the critical influence of the "i" parameter, suggesting its weighting should be revised. Parameter recalibration serves to amplify the significance of "G," "L," "D," and "i" parameters, while diminishing others. The integration of multiple hydrogeological variables considerably enhances the precision of groundwater vulnerability assessments.

Groundwater salinity modeling and mapping using machine learning approaches: a case study in Sidi Okba region, Algeria.

The groundwater salinization process complexity and the lack of data on its controlling factors are the main challenges for accurate predictions and mapping of aquifer salinity. For this purpose, effective machine learning (ML) methodologies are employed for effective modeling and mapping of groundwater salinity (GWS) in the Mio-Pliocene aquifer in the Sidi Okba region, Algeria, based on limited dataset of electrical conductivity (EC) measurements and readily available digital elevation model (DEM) derivatives. The dataset was randomly split into training (70%) and testing (30%) sets, and three wrapper selection methods, recursive feature elimination (RFE), forward feature selection (FFS), and backward feature selection (BFS) are applied to train the data. The resulting combinations are used as inputs for five ML models, namely random forest (RF), hybrid neuro-fuzzy inference system (HyFIS), K-nearest neighbors (KNN), cubist regression model (CRM), and support vector machine (SVM). The best-performing model is identified and applied to predict and map GWS across the entire study area. It is highlighted that the applied methods yield input variation combinations as critical factors that are often overlocked by many researchers, which substantially impacts the models' accuracy. Among different alternatives the RF model emerged as the most effective for predicting and mapping GWS in the study area, which led to the high performance in both the training (RMSE = 1.016, R = 0.854, and MAE = 0.759) and testing (RMSE = 1.069, R = 0.831, and MAE = 0.921) phases. The generated digital map highlighted the alarming situation regarding excessive GWS levels in the study area, particularly in zones of low elevations and far from the Foum Elgherza dam and Elbiraz wadi. Overall, this study represents a significant advancement over previous approaches, offering enhanced predictive performance for GWS with the minimum number of input variables.

Study on the Removal Effect of 3DEPs on Microplastics in Groundwater and Desalination Mechanism

Owing to water scarcity, groundwater quality has become an important constraint on sustainable regional development, especially in coastal areas. Coastal groundwater is subject to natural conditions and intense human activities, and the evolution of water quality is extremely complex, with the possibility of seawater intrusion into the groundwater, leading to the salinisation of freshwater aquifers. At present, the commonly used methods for removing groundwater pollution include coagulation, adsorption, biological method, membrane technology, etc., among which the coagulation method is more commonly used. At present, three-dimensional electrode electrochemical technology is widely used in high salinity organic wastewater and many organic wastewaters, but there are few studies related to microplastic removal by three-dimensional electrode electrochemical technology using metallic aluminium particles as particle electrodes. The treatment of microplastics in coastal groundwater is a relatively novel issue, and with the increasing application of three-dimensional electrochemical technology in the study of desalination, the three-dimensional electrode electrochemical technology will be promising due to the high salinity of coastal groundwater, the increasingly serious pollution of microplastics, and the fluctuation of water quality and quantity.

Natural metal contents and influence of salinization in deep Canadian Shield groundwater: Base level versus mineral deposit enrichment halos

Groundwater interacts with mineral deposits, making this secondary environment a medium of choice for geochemical exploration. With increasing depth, groundwater in Precambrian shields typically matures and increases in salinity through prolonged water–rock interactions and mixing with geologically old saline fluids. As the focus of mineral exploration is gradually shifting toward deeper targets, the influence of salinity on pathfinder background levels and anomalous thresholds must be considered. We compiled a deep background database (Deep Abitibi Groundwater database; DAGW) containing groundwater samples between depths of 0 m and 690 m from barren exploration boreholes in the Abitibi subprovince, Quebec, to evaluate the natural elemental levels at depth. The DAGW provides valuable knowledge of the base levels of a wide spectrum of elements and of the changes in these base levels along with the increasing salinity in bedrock aquifers of the deep Canadian Shield. This can serve to estimate background values during hydrogeochemical exploration campaigns. The regional Abitibi base level for Zn (median of 7.5 μg/L), a key pathfinder for VMS mineralization, is unaffected by the increasing salinity levels down to depths of 690 m. Hence, down to this depth, the Zn concentrations measured may be used directly for mineral exploration, and no further correction for the effects of salinization is necessary. We conducted a case study near an undisturbed VMS-type deposit (Daniel 25) and a Zn enrichment halo was observed. Within the halo, Zn concentrations increased with depth, indicating that Zn mobility was positively affected by the geochemical conditions of the deeper aquifer. Anomalous Zn thresholds representing the proximal (50–600 m from the mineralization) and direct contact (within 50 m of the mineralization) footprints of the Daniel 25 deposit were estimated at 50 μg/L and 380 μg/L, respectively. An anomalous threshold was also estimated for Co, a secondary pathfinder, at 0.5 μg/L. Unlike Zn, several other elements, which could be used to trace mineral deposits, are affected by salinization. As a final step, we applied a correction to the Daniel 25 data, based on elemental baseline trends observed in the DAGW. The aim was to remove the effects of salinity and enhance the statistical correlations caused by the presence of the mineralized body. This correction led to the emergence of new spatial correlations, notably with Na and Sr, that are coherent with the increasing hydrothermal alteration in proximity to the ore.

Cation exchange and leakage as dominant processes in controlling salinity and strontium in sandy and argillaceous coastal aquifer: Insights from hydrochemistry and multi-isotopes

Coastal groundwater is crucial for supporting ecosystems and meeting the water needs of coastal communities. However, over-exploitation of groundwater resources, including fresh water and deep brine, leads to groundwater depletion and exacerbates saline intrusion, posing significant challenges to sustainable development. This study focuses on the south bank of Laizhou Bay, a typical area threatened by saline intrusion due to the salt industry’s extraction of deep brine and over-exploitation of fresh groundwater. To assess the impact on salinization processes, we employed hydrochemistry and multi-isotope techniques to study strontium (Sr) distribution and salinization in coastal groundwater, unraveling their origins and explaining hydrochemical processes. The hydrochemical analysis revealed that evaporite dissolution primarily contributes to salinity, especially in deep brine and saline water (TDS > 30 g/L). The relationship between 87Sr/86Sr and chlor-alkali index (CAI) and saturation index (SI) indicated distinct sources of Sr. In fresh and brackish water (<10 g/L), Sr originates from mineral weathering and dissolution (SIs of albite and calcite increase with salinity), while in deep brine and saline water, Sr is influenced by cation exchange (with CAI-Ⅰ and CAI-Ⅱ greater than 0). Additionally, the interplay of anthropogenic input (nitrate), Sr, and EC highlights the differential anthropogenic impact on shallow groundwater, emphasizing the relatively closed environment of deep brine. Multi-isotope analysis showed that the average 87Sr/86Sr of shallow groundwater samples in the cone depression is 0.7112, between that of upstream surface water and shallow groundwater (0.7119) and deep brine and saline water (0.7101), indicating groundwater extraction induces deep brine leakage into shallow groundwater, elevating salinity and Sr concentrations. This poses a direct threat to local water security, including irrigation suitability and health risks of drinking water.In conclusion, we categorized the relationships between 87Sr/86Sr and Sr under different hydrochemical processes into different end-members based on their origins. This offers valuable insights in comparable regions for identifying groundwater salinization and saline intrusion, and the use of Sr and its isotopes can also help trace cation exchange and deep brine leakage in coastal aquifers.

Integrated geophysical exploration, water quality mapping and salinization of the coastal aquifers in Bonaberi, Douala, Cameroon

Integrated geophysical exploration, water quality mapping and salinization of the coastal aquifers in Bonaberi, Douala, Cameroon

Application of water quality index and statistical-hydrochemical techniques in groundwater assessment of the Quaternary aquifer, southwest Nile Delta of Egypt

The objective of the current study is to investigate the hydrogeochemistry of the Quaternary groundwater and evaluating its suitability for drinking and irrigation needs using statistical analysis, water quality indicators and physicochemical parameters, in west of the Nile Delta, Egypt. The study area has high population growth and agricultural activities, which require groundwater protection, and predicting probable environmental problems. For these reasons, 54 groundwater samples were collected during April 2021. Hierarchical cluster analysis, Pearson, and factor analysis used for statistical analysis. Biplots, Gibbs and Piper diagrams were used to infer the geochemical processes controlling groundwater chemistry. The groundwater is affected by silicate weathering, reveres ion exchange, dissolution of CO2, and recharge from the Nile. According to the WQICCME drinking value, the groundwater ranged between fair to marginal water quality. The distribution of integrated-weight water quality index of the samples, showed that it is excellent for irrigation (< 25). The nine physicochemical parameters sodium percent (Na%), permeability index, sodium adsorption ratio, Kelley Index, residual sodium carbonate, magnesium hazard, Potential Salinity, Corrosive Ratio and Chloro-alkaline Indices revealed that most of the groundwater are of good quality and can be safely used for agricultural activities, albeit few samples due the west needs some treatment. It is recommended to minimize the fertilizers and nutrients use to decrease the anthropogenic impact on the groundwater. The reuse of irrigation water without treatment should be limited. As well, the pumping rates should be controlled to avoid aquifer salinization.

Experimental-based groundwater salinization from the carbonate aquifer of eastern Saudi Arabia: Insight into machine learning coupled with meta-heuristic algorithms

Groundwater (GW) salinization of coastal aquifers has become a serious problem for attaining sustainable water resource management in Saudi Arabia and other parts of the world. Therefore, it is crucial to assess the extent of this salinization to protect and manage our water resources effectively. This research proposed real fieldwork GW samples at several locations supported with experimental based on chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS) to analyze several GW physical, chemical, and hydro-geochemical elements. In this study, we model GW salinization with machine learning algorithms such as support vector regression, gaussian process regression, artificial neural networks, and least squares ensemble boosting regression tree. The performance of the standalone models was optimized with metaheuristic optimization-based algorithms such as fuzzy hybridized genetic algorithm (ANFIS-GA) and particle swarm optimization (ANFIS-PSO). The outcomes based on three variable input combinations were validated using several performance indicators and graphical methods. The quantitative analysis indicated that GPR-Combo1(MAE = 0.006 mg/L), Ensm- Combo2 (MAE = 0.025 mg/L), and GPR- Combo3 (MAE = 0.078 mg/L) proved merit among the standalone combinations. Where combo 1, 2, and 3 stand for model combinations derived from feature selection. The cumulative probability function (CPF) demonstrated that heuristic optimization ANFIS-GA (MAE = 0.0025 mg/L, MAPE = 0.19183) and ANFIS-PSO (MAE = 0.0018 mg/L, MAPE = 0.0723) outperformed the standalone error accuracy and served reliable approach. Both the standalone models and heuristic algorithms used for GW salinization modeling have demonstrated promising results in accurately predicting salinity. This approach could aid in effectively managing the GW resources for sustainable development.

Multidisciplinary approach to understand the salinization of fractured crystalline aquifers in semi-arid region

Multidisciplinary approach to understand the salinization of fractured crystalline aquifers in semi-arid region

Quantifying salinity in heterogeneous coastal aquifers through ERT and IP: Insights from laboratory and field investigations

The lithological and stratigraphical heterogeneity of coastal aquifers has a great influence on saltwater intrusion (SI). This makes it difficult to predict SI pathways and their persistence in time. In this context, electrical resistivity tomography (ERT) and induced polarization (IP) methods are receiving increasing attention regarding the discrimination between saltwater-bearing and clayey sediments. To simplify the interpretation of ERT data, it is commonly assumed that the bulk conductivity mostly depends on the conductivity of pore-filling fluids, while surface conductivity is generally disregarded in the spatial and temporal variability of the aquifers, particularly, once the aquifer is affected by the presence of saltwater. Quantifying salinities based on a simplified petrophysical relationship can lead to misinterpretation in aquifers constituted by clay-rich sediments. In this study, we rely on co-located data from drilled boreholes to formulate petrophysical relationships between bulk and fluid conductivity for clay-bearing and clay-free sediments. First, the sedimentary samples from the drilled wells were classified according to their particle size distribution and analyzed in the lab using spectral IP in controlled salinity conditions to derive their formation factors, surface conductivity, and normalized chargeability. Second, the deduced thresholds are applied on the field to distinguish clay-bearing sediments from brackish sandy sediments. The results are validated with logging data and direct salinity measurements on water samples. We applied the approach along the Luy River catchment and found that the formation factors and surface conductivity of the different unconsolidated sedimentary classifications vary from 4.0 to 8.9 for coarse-grained sand and clay-bearing mixtures, while normalized chargeability above 1.0 mS.m−1 indicates the presence of clay. The clay-bearing sediments are mostly distributed in discontinuous small lenses. The assumption of homogenous geological media is therefore leading to overestimating SI in the heterogeneous clay-bearing aquifers.

Uncertainty quantification in the Henry problem using the multilevel Monte Carlo method

We investigate the applicability of the well-known multilevel Monte Carlo (MLMC) method to the class of density-driven flow problems, in particular the problem of salinisation of coastal aquifers. As a test case, we solve the uncertain Henry saltwater intrusion problem. Unknown porosity, permeability and recharge parameters are modelled by using random fields. The classical deterministic Henry problem is non-linear and time-dependent, and can easily take several hours of computing time. Uncertain settings require the solution of multiple realisations of the deterministic problem, and the total computational cost increases drastically. Instead of computing of hundreds random realisations, typically the mean value and the variance are computed. The standard methods such as the Monte Carlo or surrogate-based methods are a good choice, but they compute all stochastic realisations on the same, often, very fine mesh. They also do not balance the stochastic and discretisation errors. These facts motivated us to apply the MLMC method. We demonstrate that by solving the Henry problem on multi-level spatial and temporal meshes, the MLMC method reduces the overall computational and storage costs. To reduce the computing cost further, parallelization is performed in both physical and stochastic spaces. To solve each deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion.

An integrated approach based on HFE-D, GIS techniques, GQISWI, and statistical analysis for the assessment of potential seawater intrusion: coastal multilayered aquifer of Ghaemshahr-Juybar (Mazandaran, Iran).

The overexploitation of coastal aquifers is one of the important reasons for the salinity of groundwater due to seawater intrusion (SWI). This study assesses the hydrochemical changes of the Ghaemshahr-Juybar (GH.-J.) plain. For this purpose, specific statistical methods, modified Piper diagram groundwater quality indicators ([Formula: see text] and [Formula: see text]), groundwater quality index specific to seawater intrusion ([Formula: see text]), and hydrochemical facies evolution diagram (HFE-D) along with GIS (Geographic Information System) techniques were applied to identify the spatiotemporal changes of salinity in coastal multilayer alluvial aquifer. The results show that the chemical composition in the GH.-J. aquifer is basically controlled by three main factors: (1) Caspian SWI and fossil saltwater penetration from an underlying layer, (2) reverse cation exchange process, and (3) feeding by domestic sewage, agricultural activities, and use of nitrate chemical fertilizers. The investigation of the hydrogeochemical facies evolution process shows that due to the reduction of extraction from wells, saltwater infiltration has significantly decreased. Therefore, according to the geological and lithological conditions of the aquifer and exposure to seawater, it is possible to prevent the entry of saltwater from the confined aquifer into the unconfined aquifer and the saltwater intrusion by developing well optimal operation policies in order to control withdrawal from semi-deep wells and the elimination of deep wells. This practical approach to managing the salinity of coastal aquifers is suitable for the allocating groundwater resources and for use in the development of aquifer simulation models.

FlowSOM clustering – A novel pattern recognition approach for water research: Application to a hyper-arid coastal aquifer system

Monitoring and understanding of water resources have become essential in designing effective and sustainable management strategies to overcome the growing water quality challenges. In this context, the utilization of unsupervised learning techniques for evaluating environmental tracers has facilitated the exploration of sources and dynamics of groundwater systems through pattern recognition. However, conventional techniques may overlook spatial and temporal non-linearities present in water research data. This paper introduces the adaptation of FlowSOM, a pioneering approach that combines self-organizing maps (SOM) and minimal spanning trees (MST), with the fast-greedy network clustering algorithm to unravel intricate relationships within multivariate water quality datasets. By capturing connections within the data, this ensemble tool enhances clustering and pattern recognition. Applied to the complex water quality context of the hyper-arid transboundary Caplina/Concordia coastal aquifer system (Peru/Chile), the FlowSOM network and clustering yielded compelling results in pattern recognition of the aquifer salinization. Analyzing 143 groundwater samples across eight variables, including major ions, the approach supports the identification of distinct clusters and connections between them. Three primary sources of salinization were identified: river percolation, slow lateral aquitard recharge, and seawater intrusion. The analysis demonstrated the superiority of FlowSOM clustering over traditional techniques in the case study, producing clusters that align more closely with the actual hydrogeochemical pattern. The outcomes broaden the utilization of multivariate analysis in water research, presenting a comprehensive approach to support the understanding of groundwater systems.

Assessing coastal aquifer vulnerability to seawater intrusion using the "GALDIT" method: Application to the Dradère-Souiere coastal aquifer (Northern Morocco)

In terms of land-use planning, and in an attempt to anticipate and preserve what can be preserved, the prevention of salinization of coastal aquifers is an important step to which researchers are devoting more and more effort, notably by studying their vulnerability to seawater intrusion. GALDIT is a method for "index mapping" the vulnerability of coastal aquifers, dedicated to the risks of seawater intrusion. The present study involves assessing and mapping the vulnerability of the Dradère-Souiere coastal aquifer (Northern Morocco) to seawater intrusion using this method.

Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development

ABSTRACT A backpropagation neural network (BPNN) was used to predict salinity levels in the Chott Djerid shallow aquifers. A set of 51 water samples was collected from the Chott Djerid plio-quaternary aquifers for geochemical analysis. Major elements and nitrates were ascertained by using high performance liquid-ion chromatography. The BPNN was trained on a dataset of 51 water samples with variable geochemical parameters. Our results indicated a high accuracy when applying a model with 13 inputs, 1 hidden layer (6 neurons) and 1 output (TDS in mg/L). The collected data were split into 80% for training the model and 20% for testing and cross validation. The result was evaluated using various statistical performance criteria (i.e., MSE, RMSE, R2, SSE, SD, Accuracy, Sensitivity, specificity, and Kappa test); it showed that BPNN model properly predicted the salinity of the Chott Djerid plio-quaterny water samples (RMSE = 0.0402; R2 = 0.9721 and SSE = 0.0146). The BPNN was able to capture the complex relationship between salinity levels and other aquifer parameters. The potential application of BPNNs for predicting salinity levels in shallow aquifers was crucial in supporting decision-makers for water management; it provided valuable insights into the salinity fluctuation of the studied shallow aquifers.