Aquifer Management Research Articles

Assessment of Supervised Machine Learning Techniques for Predicting Groundwater Availability in Mexican Aquifers

Groundwater overexploitation is a global problem. In Mexico, 653 aquifers provide 39.1% of water for consumptive use. The National Water Commission manages this resource, but predicting aquifer availability is challenging, and the number of aquifers in deficit has increased. Physical models can address this issue but require extensive resources, whereas supervised learning algorithms offer a less resource-demanding alternative. This study evaluates four machine learning techniques for groundwater availability prediction: support vector machine regression, M5' model trees, random forests (RFR), and artificial neural networks. The models were trained using climatological, land use, and concession data from 1997 to 2015 and tested with data from 2018 and 2020. Random forests performed the best, showing a high correlation coefficient and low RMSE errors. The prediction accuracy for the availability state was 81.24% for 2018 and 76.79% for 2020. Thus, RFR can effectively predict short-term water availability, aiding sustainable aquifer management.

Hydrogeochemical assessment of groundwater in transboundary aquifers along the US-Mexico border and drinking water quality implications for Texas Colonias

The groundwater resources in transboundary aquifers are crucial to the development of involved nations. A unified governance strategy based on scientific data is essential for the management of transboundary aquifers. This study presents a comprehensive geochemical analysis of the groundwater quality of private wells along the US-Mexico border in Texas across three transboundary aquifer types. The concentrations of dissolved major ions, trace, and rare earth elements (REE) were measured, and the dissolved organic matter was spectroscopically characterized to evaluate the prevailing water-rock interactions, biogeochemical reactions, and anthropogenic contamination in the groundwater and their susceptibility to future contamination. Groundwater samples were collected from private wells (n = 22) from four representative counties within a section of the Texas-Mexico border (Maverick, Kinney, Dimmit, and Webb Counties). Three distinct aquifer types were revealed from the major ion composition, e.g., a carbonate aquifer within Kinney County (a part of Edwards-Trinity aquifer), an alluvial aquifer with prevailing reducing conditions within Dimmit and Webb Counties (a part of Carrizo-Wilcox aquifer), and an evaporite-rich aquifer within Maverick County. The concentrations of trace elements in the groundwater of each aquifer type were below the MCL of USEPA, however, excessive Sr concentrations were evident mostly in the evaporite-rich aquifer. Our results indicate dissolution of evaporites, and possible anthropogenic contamination may be responsible for the degradation of groundwater quality in Maverick County, raising concern for the viability of the aquifer in the future.

Using archive hydrogeological data to enhance the hydrodynamic knowledge of hardrock aquifers in Western Africa

In the Kara River Watershed (KRW, northern Togo), drinking water is mainly supplied by groundwater flowing through fissures in the metamorphic formations of the Dahomeyides belt. The study was based on the use and valorisation of hydrogeological archive dataset from 1970s to 2021. The database comprises 1,389 boreholes, but only 710 are considered after pre-treatment, and provides information mainly on total depth, weathering thickness, discharge at drilling, piezometric level, specific discharge, transmissivity, and the nature of the lithology tapped. The methodological approach involved statistical analysis of data, characterization of the fissured horizon, establishment of relationships between hydrodynamic parameters and satellite image processing. The results showed that the aquifers structure is close to those observed worldwide in hardrock context and they provide operational details on the hydrogeological functioning of these environments in the West African particular context. From the surface downwards, aquifers show a layer of saprolite (thickness between 0.2 and 37.3 m) acting as a storage level, a fissured layer whose permeability depends on the number and connectivity of the fissures, and then a level of very low permeability, unfissured bedrock. In the study area, the depth of the useful fissured medium is 52 m with an average useful discharge of 7.1 m3.h-1. The coefficients of variation for specific discharge and transmissivity are greater than 100%, reflecting the structural heterogeneity of the study area. Analysis of the discharges measured in the boreholes as a function of lithology shows that the metasediments are more productive than other geological formations. Finally, all the geological, hydrogeological, and hydrodynamical data have been used to propose a preliminary conceptual model of the watershed's hardrock aquifers. These results will serve as decision-making tools for water managers and will facilitate the policy of integrated management of groundwater resources at the scale of the KRW. The developed methodology also shows how archive data should be used to achieve optimized management of aquifers without major investments.

Transboundary Aquifer Management Across the Americas: Hydro-Diplomacy as an Accelerator of Adaptive Groundwater Governance Amid Climate Change Challenges

The management of transboundary aquifers across the Americas faces significant challenges, especially as climate change and population growth intensify groundwater stress. Groundwater use has increased to support domestic, industrial, and agricultural demands but has been extracted through unregulated withdrawals, leading to the severe degradation of aquifer health and transboundary frictions. This study focuses on how hydro-diplomacy can accelerate the adaptive governance of shared groundwater resources in three key regions: Canada–USA, USA–Mexico, and Mexico–Guatemala–Belize. We utilized a mixed methodology by integrating a transect approach, borrowed from ecology, into the field of geopolitics. To compare the hydro-diplomatic relations and groundwater governance across a continental gradient in the Americas, we conducted a literature review and employed the TWINS conflict–cooperation matrix to evaluate governance frameworks and hydro-diplomatic interactions across time. Our findings demonstrate that hydro-diplomacy plays a pivotal role in expediting agreements, fostering transboundary data sharing, and supporting participatory governance models. In particular, the presence of supranational bodies such as the International Joint Commission (IJC) between Canada and the USA has been effective in maintaining long-term collaboration through social learning and technical cooperation. Meanwhile, in regions like Mexico–Guatemala–Belize, the absence of robust institutions has hindered progress, with limited financial and knowledge-sharing networks. This study highlights the need for improved cross-border cooperation mechanisms and the establishment of common monitoring protocols to better manage aquifer resources under the pressures of climate change. The results support the development of more adaptive transboundary groundwater management strategies aligned with Sustainable Development Goal (SDG) 6.5.2 and call for broader geopolitical cooperation to address the complexities of groundwater governance.

Saltwater intrusion simulations in coastal karstic aquifers related to climate change scenarios

Coastal zones are crucial ecosystems supporting significant biodiversity and pertinent socio-economic activities. However, anthropogenic development contributes to socio-environmental complexities, particularly public water supply threats caused by climate change. This research presents a case study on the north-western coast of Yucatan, Mexico, which models potential saltwater intrusion in groundwater for multiple projections of sea level rise and recharge change due to climate change and its implications for the public water supply of the regional population and ecosystem. For this purpose, a previously calibrated and validated numerical model is employed, adapting its boundary conditions, keeping its calibrated hydrogeologic parameters, and considering the 2040 and 2100 climate change projections. Simulation results show that under these projections, significant saltwater intrusion may occur, reducing freshwater thickness due to increased salinity in groundwater and a loss of freshwater sources resulting from brackish-saline wedge intrusion. These scenarios are of particular concern as freshwater in this coastal region is the main source for public water supply and for freshwater input in coastal ecosystems. Moreover, this study underscores the susceptibility of karstic aquifers to salinization, especially in the face of rising sea levels, given their unique hydrogeological characteristics and substantial responsiveness to marine forcings. In spite of the uncertainties in global climate change predictions, this study enhances our understanding of the dynamics of these unique aquifers, and presents future saltwater intrusion projections that offer valuable technical insights to design and implement pertinent and resilient coastal aquifer management strategies.

Machine learning framework for predicting water quality classification

ABSTRACT Groundwater serves as the source for nearly half of the world's drinking water, yet understanding of global groundwater resources remains incomplete, and management of aquifers falls short, particularly concerning groundwater quality. This research offers insights into the groundwater quality in 242 stations of Maharashtra and Union Territory of Dadra and Nagar Haveli and nine parameters (pH, TDS, TH, Calcium (Ca2+), Magnesium (Mg2+), Chloride (Cl−), Sulphate (SO42−), Nitrate (NO3−), Fluoride (F−)) were considered for computing the Water Quality Index (WQI) and hence Water Quality Classification (WQC) based on Water Quality Index (WQI). This research introduces the utilisation of Machine Learning (ML) models, specifically, Random Forest, Adaptive Boosting (AdaBoost), Gradient Boosting, XGBoost, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) model for predicting WQC and models are tested. Grid search method as a hyperparameter tuning of parameters is utilized to achieve the best possible performance of ML models. The performance metrics that are used for evaluating and reporting the performance of classification models are Accuracy, Precision, Recall or Sensitivity, F1 Score. SVM achieved the highest performance in predicting WQC. With accurate predictions of WQC, these findings have the potential to enhance NEP concerning water resources by facilitating ongoing improvements in water quality.

Evaluating Aquifer Management Objectives against Socioeconomic Indicators to Understand Differential Impacts on Communities across Multiple Scales

Evaluating Aquifer Management Objectives against Socioeconomic Indicators to Understand Differential Impacts on Communities across Multiple Scales

Can Enhanced Information Systems and Citizen Science Improve Groundwater Governance? Lessons from Morocco, Portugal and Spain

New information and communication technologies have a significant potential to increase the transparency of aquifer management and improve groundwater governance. This research experiments the introduction of a mobile application that allows users to transfer and share information about their groundwater extractions and receive agroclimatic information and groundwater data. It takes place in three different aquifers in Morocco, Portugal, and Spain, each with varied institutional frameworks. This research tests and evaluates the potential of enhanced information systems and citizen science applied to groundwater management and aims to identify some factors that facilitate or hinder their adoption and implementation. To do this, the researchers developed a 4-year plan based on surveys, semi-structured interviews, meetings, participatory workshops, and public round tables with local actors. The main lesson learned from the comparative analysis of these three experiences is that the use of enhanced information systems is more positively perceived, accepted, and adopted when an appropriate social and institutional framework exists and that the more consolidated this framework is, the easier they will be to implement and develop enhanced information systems.

Hegemony and Colonialization in the Water Management Sector: Issues and Lessons for IWRM

Water resources management and the broad concept of Integrated Water Resources Management (IWRM) attract varied perspectives about their effectiveness and equity as they address diverse needs across sectors and contextual situations. Managers in the water sector generally support their current governance models, while anti-poverty advocates seek more equity in the distribution of resources. Another group of stakeholders claims a lack of inclusivity in decision-making, leading to inequitable outcomes due to hegemony and colonialization of the water management domain by sector experts, officials, and other actors. IWRM focuses on reforms in water governance to achieve greater participation and sharing of power by all sectors of society in decision-making. It can facilitate the involvement of all groups of stakeholders, including those who may in some cases need to engage in social action to address water issues. This paper reviews the claims about the validity of IWRM and analyzes them according to management scenarios where water is a connector among sector issues. The scenarios show that participation in utility and local government decisions is the main pathway for urban water, wastewater, and stormwater management, while the same pathway is more difficult to organize in dispersed situations for domestic supply and irrigation in rural areas, some cases of aquifer management, and management of sprawling flood risk zones. The body of knowledge about participation in water resources management is robust, but organizational and financial capacities among existing entities pose barriers. Water resources management and IWRM do involve hegemony, and the field of practice has been colonialized, but the existential issues and complexity of the decisions and systems involved challenge society to manage successfully while assuring equity and participation through governance reform. The debates over hegemony and colonialization in water management provide an opportunity to continue improving the norms of practice and water resources education.

Mapping of irrigated vineyard areas through the use of machine learning techniques and remote sensing

Effective and sustainable management of aquifers in regions with intensive groundwater use for irrigation requirements accurate mapping or irrigated areas to control water resource exploitation and plan rational water usage. This study proposes a cost-effective methodology based on satellite images to identify irrigated areas utilizing surface water and groundwater resources. The methodology integrates soil moisture estimations, environmental variables, and variables that affect to retention of water soil, that join a ground truth dataset, to estimate irrigated surface through a machine learning method during the irrigation period of 2021. Spectral data derived parameters and crop morphology, along with official data on agricultural parcels, were utilized to define vineyard irrigation areas at the plot scale within the Requena-Utiel aquifer in Eastern Spain. A machine learning classification technique was applied,yielding a remarkable precision of 91.8 % when compared to ground truth data.Discrepancies between the remote sensing-based irrigated area estimation and official data are highlighted. This study represents the most accurate plot-scale irrigation mapping of woody crops in the region to date.

Geo-electrical prognosis of aquifer protectivity, corrosivity, and vulnerability via index-based models within a major coastal milieu

This work was carried out to investigate the protective capacity, vulnerability, and corrosivity within a major coastal milieu in Southern Nigeria with the use of index-based geo-electrical modeling methods. Vertical electrical soundings were undertaken at twenty locations with the aid of Schlumberger array having a maximum electrode spacing of 400 m. The results indicated that the lithology comprised four subsurface layers having variable values of resistivity and thickness. The Dar-Zarrouk parameter, the Aquifer Vulnerability Index (AVI), and the GOD (Groundwater occurrence G, Overlying lithology O and Depth to aquifer D) models were employed to appraise measures of aquifer protectivity and vulnerability to contamination. The longitudinal conductance values ranged from 0.0071–1.95 mhos with a mean of 0.32 mhos, indicating moderate protectivity. AVI values ranged from 1.73–4.10 with a mean of 3.03, indicating moderate aquifer vulnerability. The GOD indices ranged from 0.35–0.63 with a mean of 0.49, indicating moderate aquifer vulnerability. Corrosivity was also computed based on topsoil resistivity values which ranged from 12.7 to 664.2 Ωm with a mean of 168.17 Ωm, indicating moderate corrosivity, and demonstrating the unsuitability of corrosive locations for laying underground pipes. All the index-based models gave similar interpretations, indicating moderate aquifer protectivity and susceptibility. These results were corroborated by 2D electrical resistivity tomography surveys conducted at four stations. This work has therefore delineated important aquifer geo-hydraulic properties with index-based geo-electrical modeling techniques. The results obtained are critical for effective aquifer management, conservation, and sustainability.

Climate change impact on groundwater resources in sandbar aquifers in southern Baltic coast

Shallow coastal aquifers are vulnerable hydrosystems controlled by many factors, related to climate, seawater-freshwater interactions and human activity. Given on-going climate change, sea level rise and increasing human impact, it is especially true for groundwater resources situated in sandbars. We developed numerical models of unsaturated zone water flow for two sandbars in northern Poland: the Vistula Spit and the Hel Spit using HYDRUS-1D. The simulations were performed for three types of land use: pine forest, grass cover and bare soil, for 2024–2100 based on weather data and sea level rise forecasts for two emissions scenarios (RCP 4.5 and RCP 8.5). The results present prognosis of groundwater recharge, water table level and water content changeability in near-term (2023–2040), mid-term (2041–2060), and long-term period (2081–2100). Expected sea level rise and decreasing hydraulic gradient of the sandbar aquifers will probably cause in-land movement of the freshwater–saltwater interface, leading to significant decrease or complete salinization of groundwater resources. The study shows that holistic monitoring including groundwater level and salinization, sea level rise, and metheorological data (precipitation amount and variability, temperature) is crucial for sustainable management of vulnerable aquifers located in sandbars.

Reinforcement learning for watershed and aquifer management: a nationwide view in the country of Mexico with emphasis in Baja California Sur

Reinforcement Learning (RL) is a method that teaches agents to make informed decisions in diverse environments through trial and error, aiming to maximize a reward function and discover the optimal Q-learning function for decision-making. In this study, we apply RL to a rule-based water management simulation, utilizing a deep learning approach for the Q-learning value function. The trained RL model can learn from the environment and make real-time decisions. Our approach offers an unbiased method for analyzing complex watershed scenarios, providing a reward function as an analytical metric while optimizing decision-making time. Overall, this work underscores RL’s potential in addressing complex problems, demanding exploration, sequential decision-making, and continuous learning. External variables such as policy shifts, which are not readily integrated into the model, can substantially influence outcomes. Upon establishing a model with the requisite minimal states and actions, the subsequent learning process is relatively straightforward, depending on the selection of appropriate RL model algorithms. Its application depends on the specific problem. The primary challenge in this modeling approach lies in model definition, specifically in devising agents and actions that apply to complex scenarios. Our specific example was designed to address recent decision-making challenges related to constructing dams due to water scarcity. We present two examples: one from a nationwide perspective in Mexico and the other focused on Baja California Sur, the state with the highest water stress. Our results demonstrate our capability to prioritize watersheds effectively for the most significant benefits, particularly dam construction.

Groundwater Flow Model Calibration Using Variable Density Modeling for Coastal Aquifer Management

The paper investigates the mechanism of seawater intrusion and the performance of free and open-source codes for the simulation of variable density flow problems in coastal aquifers. For this purpose, the research focused on the Marathon Watershed, located in the northeastern tip of Attica, Greece. For the simulation of the groundwater system, MODFLOW, MT3DMS and SEAWAT codes were implemented, while sensitivity analysis and calibration processes were carried out with UCODE. Hydraulic head calibration was performed on the MODFLOW model, and TDS concentration was validated in the SEAWAT model. The calibrated parameters of the MODFLOW model were obtained for the variable density flow simulation with SEAWAT. The MODFLOW and SEAWAT hydraulic head outputs were analyzed and compared to one another. The outcome of this analysis is that SEAWAT produced slightly better results in terms of the hydraulic heads, concluding that parameter transferability can take place between the two models. For the purpose of the seawater intrusion assessment, the use of the SEAWAT code revealed that the aquifer is subjected to passive and passive–active seawater intrusion during wet and dry seasons, respectively. Finally, an irregular shape of a saltwater wedge is developed at a specific area associated with the hydraulic parameters of the aquifer.

History matching for geological carbon storage using data-space inversion with spatio-temporal data parameterization

History matching based on monitoring data will enable uncertainty reduction, and thus improved aquifer management, in industrial-scale carbon storage operations. In traditional model-based data assimilation, geomodel parameters are modified to force agreement between flow simulation results and observations. In data-space inversion (DSI), history-matched quantities of interest, e.g., posterior pressure and saturation fields conditioned to observations, are inferred directly, without constructing posterior geomodels. This is accomplished efficiently using a set of O(1000) prior simulation results, data parameterization, and posterior sampling within a Bayesian setting. In this study, we develop and implement (in DSI) a deep-learning-based parameterization to represent spatio-temporal pressure and CO2 saturation fields at a set of time steps. The new parameterization uses an adversarial autoencoder (AAE) for dimension reduction and a convolutional long short-term memory (convLSTM) network to represent the spatial distribution and temporal evolution of the pressure and saturation fields. This parameterization is used with an ensemble smoother with multiple data assimilation (ESMDA) in the DSI framework to enable posterior predictions. A realistic 3D system characterized by prior geological realizations drawn from a range of geological scenarios is considered. A local grid refinement procedure is introduced to estimate the error covariance term that appears in the history matching formulation. Extensive history matching results are presented for various quantities, for multiple synthetic true models, using the new DSI framework. Substantial uncertainty reduction in posterior pressure and saturation fields is achieved in all cases. The framework is also applied to efficiently provide posterior predictions for a range of error covariance specifications. Such an assessment would be very expensive using a traditional model-based approach.

The Effect of Discounting on the Formulation of an Aquifer Management Policy Based on Groundwater Trading

Optimal groundwater management is a necessary condition for achieving the objective of sustainable development, which is directly linked to issues of intergenerational equity. Thus, groundwater management policy shaping has to consider such issues, in particular through the implementation of appropriate discounting methods. The existing literature in the field of groundwater management focuses on a single discount function (DF), the exponential one, without considering the impact of different DFs on the results obtained. At the same time, tradable water rights (TWR) systems have been suggested as policy instruments for more efficient and sustainable water use. This paper focuses on the impact that different DFs have on the formulation of groundwater management policies based on TWR. To this end, a dynamic model is formulated, which concerns groundwater pumping from an aquifer by two groups of users participating in a TWR system and four different DFs are considered to calculate the present value of social welfare: no discounting, exponential, hyperbolic and Gamma discounting. The results of simulations based on data for an aquifer in Northern Greece show that there is a high sensitivity of the results to the DF, which has a direct effect on social welfare from groundwater consumption, aquifer’s hydrological behavior and TWR system intertemporal economic efficiency.

Coupling of WetSpass-M and MODFLOW models for groundwater flow assessment

Recharge is considered a key parameter in groundwater models for sustainable management of aquifers, which is influenced by factors such as land use, soil and weather. The present study was conducted to couple WetSpass-M and MODFLOW models in the Neyshabur–Rokh Basin. To this aim, the simulated recharge by the WetSpass-M model was applied as an input into MODFLOW to assess the groundwater balance. The hydrodynamic coefficients were determined by calibrating the model, and the model sensitivity to the hydraulic conductivity coefficient, specific yield and recharge was evaluated. The results indicated that the annual average of surface runoff, actual evapotranspiration, interception and recharge during 1991–2017 equalled 18, 36, 7.6 and 42.6% of the average annual precipitation in the basin, respectively. The accurate alignment of simulated and observed water levels, along with the achievement of suitable evaluation criteria values in both steady and transient states, demonstrates the WetSpass-M model's precision in estimating recharge and successfully integrating the two models. The groundwater balance assessment revealed a significant deficit in the aquifer, with the model demonstrating greater sensitivity to the hydraulic conductivity coefficient and providing valuable insights for the sustainable management of the Neyshabur aquifer. Thematic collection: This article is part of the Monitoring the aquifers collection available at: https://www.lyellcollection.org/topic/collections/monitoring-the-aquifers

Worldwide Distribution, Health Risk, Treatment Technology, and Development Tendency of Geogenic High-Arsenic Groundwater

The presence of high concentrations of geogenic arsenic (As) in groundwater poses a serious threat to the health of millions of individuals globally. This paper examines the research progress of groundwater with high concentrations of geogenic As through a comprehensive literature review and analysis, covering distribution, health risks, in situ remediation, regulatory technologies, and development trends, to establish a reference for future research. The global distribution of geogenic high-As groundwater is mainly in inland basins and river deltas of countries in South Asia, East Asia, and South America. High-As risk areas can be modeled using hydrogeologic data and field measurements. This modeling approach allows for assessing and measuring potential areas of high-As groundwater. In order to provide safe drinking water promptly and effectively to areas affected by high-As groundwater, in situ rapid detection and remediation techniques have been given significant attention. This paper introduces household- or community-scale As removal technologies, including flocculant–disinfectant, bucket treatment units, use of activated alumina, use of nano zero-valent iron, aquifer iron coating technology, and bioremediation, summarizing the basic mechanisms of arsenic removal for each technology. Guaranteeing the sustainability of site-scale remediation technologies, reasonable aquifer management, and exploring alternative water sources are crucial for combating high-As groundwater contamination. Future studies should aim to elucidate the mechanisms of As’s coexistence with other pollutants in groundwater, effectively treating As-containing wastes or sludge produced during the treatment process and exploring better treatment options.

Machine Learning and Deep Learning Guided Assessment of Groundwater Reservoir Hydrodynamic Parameters: A Case Study of The El Haouz Aquifer

The Plio-Quaternary aquifer in the EL-Haouz-Mejjate region of Morocco is critical for water supply, necessitating accurate characterization for sustainable management. This study pioneers machine learning (ML) and deep learning (DL) techniques to elucidate the aquifer’s properties. Supervised algorithms, including random forest, regression, support vector machines, Gaussian process regression and neural networks, are trained on available hydrogeological data. Diverse features capture complex input-output relationships to predict key hydrodynamic factors like hydraulic conductivity and transmissivity fields. Aquifer architecture attributes, including substratum depth, thickness, and height, are also estimated. Model outputs are validated with field measurements, demonstrating promising accuracy. Enhanced hydrodynamic insights improve the conceptual model and groundwater flow modeling confidence. Uncertainties are reduced through this data-driven approach, enabling optimized aquifer management. Overall, this work shows how useful it is to combine ML and DL with traditional hydrogeology in order to get a better understanding of complicated aquifer systems. The techniques pioneered provide a pathway for sustainable management of this vital water resource.

Understanding salinity evolution in the Moroccan Ghiss-Nekor aquifer using differential mapping technique

Assessing spatiotemporal groundwater hydrochemistry evolution is crucial for sustainable aquifer management. The Ghiss-Nekor aquifer, located in a semi-arid coastal region, requires a comprehensive grasp of hydrochemical shifts for proactive degradation mitigation. Accordingly, the differential mapping technique (DMT) has been employed to assess the salinity evolution in the study area. In 2015 and 2022, 48 and 52 groundwater samples were respectively analyzed. As a result, an overall increase in salinity was observed within the Ghiss-Nekor aquifer, particularly near the shoreline, where in some areas the TDS variations (ΔTDS) exceeded +7000 mg/L. ΔCl- reached up to +129 meq/L near the coast. Similarly, ΔNa+, ΔSO42-, ΔMg2+, ΔCa2+, ΔK+, and ΔHCO3-, showed values up to +94 meq/L, +22 meq/L, +17 meq/L, +10 meq/L, +1 meq/L, and +5 meq/L, respectively, along the coast. In contrast, a decline in the levels of these ions was noted across most of the plain, especially in the southern portion. Positive major ions levels near the sea indicate ongoing freshwater degradation due to seawater intrusion. DMT approach showed its effectiveness in assessing the spatiotemporal changes occurring within aquifers. These findings advocate for broader DMT application in safeguarding aquifers impacted by salinization, promoting sustainable development.