Groundwater Data Research Articles

Spatial variability of groundwater quality in four mega-cities of India and impact of land use types on groundwater quality

ABSTRACT The deterioration of groundwater quality is a global concern, particularly in densely populated urban areas. This study examines spatial variability in groundwater quality across four major Indian cities – Mumbai, Delhi, Chennai and Kolkata – using water quality index (WQI) and remote sensing techniques. Groundwater data from the 2018 report of the Central Groundwater Board was analyzed using 12 parameters, while Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI) indices were calculated from Landsat imageries to assess land use impacts. Findings indicate significant variability: Delhi shows 44.62% unsuitable samples, Chennai 12.12%, Kolkata 0% and Mumbai 2%. Urbanization (NDBI) negatively affects WQI, while green spaces (NDVI) improve it. NDWI impacts vary, being negative in Mumbai but positive in Kolkata, Chennai and Delhi. Welch’s analysis of variance (ANOVA) and post hoc analysis confirms significant WQI differences across cities. These results highlight the urgent need for sustainable urban infrastructure, increased green spaces and pollutant-free waterbodies to improve groundwater quality in urban India.

Strategic imputation of groundwater data using machine learning: Insights from diverse aquifers in the Chao-Phraya River Basin

Strategic imputation of groundwater data using machine learning: Insights from diverse aquifers in the Chao-Phraya River Basin

Towards an evidence base for groundwater data investments

Towards an evidence base for groundwater data investments

Measurements of groundwater, hydrodynamics, and sand characteristics at a dissipative sea turtle nesting beach

Beach groundwater and nearshore hydrodynamic data were collected during a field experiment along two dissipative beach transects on Galveston Island, Texas, in the fall of 2023. The monitored beaches serve as nesting habitat for the critically endangered Kemp’s ridley sea turtle. Conditions ranged from calm to stormy, with two storms occurring during the experiment, inundating the entire beach up to the dune toe. Collected hydrodynamic data include readings from pressure loggers submerged in the foreshore and mounted in groundwater wells in the backshore, data from two wave buoys about 1.5 km offshore, and GoPro timestacks of the instantaneous waterline (wave runup). Other collected data include bathymetry and topography surveys, subsurface temperature and moisture content readings, and sediment characteristics. This comprehensive dataset can be used to (1) study relevant beach inundation and groundwater processes, including their effect on the local ecosystem (e.g., repeated flooding of sea turtle nests), (2) study the propagation of nearshore hydrodynamic processes into the beach matrix and groundwater table, and (3) validate existing beach groundwater models.

QGIS: an effective tool in assessing the quantity and quality of groundwater resources

ABSTRACT The study demonstrates the utilization of the Quantum Geographic Information System (QGIS) package, an open-source geographic information system (GIS) tool for evaluating the quantity and quality of groundwater resources. This research employed data from public sources pertaining to several sampling stations in the Indian city of Dehradun as its case study and converted this information into a digitized format. The use of QGIS allows for processing and analysis of huge data, leading to the creation of informative maps that depict the spatial distribution of groundwater characteristics. By employing spatial analysis techniques, the study aimed to gain insights into the state of groundwater resources, including their availability, variations in level, and suitability for drinking and irrigation purposes. By visualizing and analyzing the groundwater data using QGIS, the study contributes to the broader goal of managing water resources efficiently and helps policymakers, water resource managers, and other stakeholders make informed decisions regarding groundwater management, allocation, and conservation strategies.

Using random forests to explore the feasibility of groundwater knowledge transfer between the contiguous US and Denmark

Abstract Groundwater is our largest freshwater reservoir, playing an important role in the global hydrologic cycle. Lack of reliable groundwater data restricts the development of global groundwater monitoring systems linking observations with modeling at spatial scales relevant for local decision making. Despite the growing interests in machine learning (ML) for groundwater resource modeling, taking ML models to the global scale is still outstanding due to sparse groundwater data. The contiguous US (CONUS) has extensive groundwater information covering a wide range of hydrogeologic settings. We hypothesize that a ML model trained on the CONUS is transferable to other regions, and thus can be used to produce a global water table depth (WTD) map within the bounds of transferability. To test this hypothesis, we conduct a study on transferring groundwater knowledge between the CONUS and Denmark, using several random forest models trained against ∼30 m resolution long-term mean WTD data. The joint model trained on data from the CONUS and Denmark outperforms the individual models trained separately, implying similarities within global groundwater systems. The largest improvement occurs in Denmark, where the testing Nash-Sutcliffe efficiency rises from 0.68 to 0.95. SHapley Additive exPlanations (SHAP) values are utilized to express the importance of input variables. While annual mean precipitation plays a key role in the joint model and the model for the CONUS, it is the second least important input variable in the model for Denmark where local processes dominate. Moreover, Köppen-Geiger climate classification shows a significant impact on the model testing performance and the importance ranking of input variables, which might be a missing input variable in the applied random forest models. This study provides unique insights into future ML model developments towards global groundwater monitoring and improves our confidence in producing a hyper-resolution global WTD map for sustainable freshwater management.

Groundwater level predictions in the Thames Basin, London over extended horizons using Transformers and advanced machine learning models

This study breaks new ground by using the Temporal Fusion Transformer (TFT) method for groundwater level prediction, addressing the complex dynamics of the Thames Basin aquifer in England. Our research combines extensive hydrological data collected from the Thames Basin with advanced machine learning, where a complex network of rivers and streams substantially affects groundwater dynamics. Unlike previous studies, this research focuses on long-term forecasting with deep learning, offering, for the first time, a 60-day prediction horizon based on daily data. To rigorously examine the model performance and robustness on new, unseen data, we applied the walk-forward validation method and other matrices such as RMSE and R2 coupled with the Holdout technique. The models used were Long Short-Term Memory (LSTM), Attention-based LSTM, LSTM with Bayesian optimisation, Attention-based LSTM with Bayesian optimisation and TFT. They were used on the basin's Chalk, Jurassic Limestone, and Lower greensand aquifers. Whilst both LSTM models were optimised using the Bayesian technique, TFT was applied for its inherent capability in complex time series. Our methodology processed historical groundwater and rainfall data from 2001 to 2023, accounting for the potential lag in aquifer response to the proximity of the river system. The dataset served as training, validation, and holdout for each model, focusing on capturing the dynamic temporal fluctuation. The results clearly showed the superiority of the TFT model in all aquifer types compared to other models across all horizons. The Limestone had the greatest result in the 7-day projections, with an RMSE of 0.02 and R2 of 0.98; Whilst the Chalk and Lower greensand, had RMSEs of 0.03 with R2 values of 0.75 and 0.95, respectively. The Limestone aquifer performed best for the 30-day horizon again (RMSE = 0.06, R2 = 0.85), with the Chalk and Lower greensand aquifer yielding RMSE of 0.04 and 0.12 and R2 values of 0.64 and 0.74, respectively. In the 60 days predictions, the best results were observed in the limestone aquifer with RMSE of 0.09 and R2 of 0.65 in holdout validation. However, in chalk and lower greensand aquifers, the TFT showed RMSEs of 0.05 and 0.15 and R2s of 0.45 and 0.58, respectively. Traditional LSTM models demonstrated limited predictive power compared to the main model TFT, while the attention mechanism slightly improved the accuracy. This study not only sets a new benchmark in hydrological modelling accuracy but also highlights the potential of advanced machine learning in managing complex aquifers and predicting the water table.

Seasonal dynamics of trace metal concentrations and hydrological parameters in the Balu River, Bangladesh

<p>This study investigates the seasonal dynamics of trace metal concentrations and hydrological parameters in the Balu River, Bangladesh, to inform sustainable water management practices and support efforts to meet environmental standards. Water level, discharge, rainfall, and groundwater data were collected from February to August 2019, alongside sediment samples analyzed for Zn, Pb, Cu, Cr, and Cd concentrations. Statistical analyses, including correlation studies, time series modeling, and Kolmogorov-Smirnov tests, revealed significant seasonal variations. Water levels and discharge rates increased dramatically during the wet season, with upstream levels rising by 862.68% and downstream by 752.92%. Trace metal concentrations showed diverse responses: Cd increased by 554.52%, Zn by 11.86%, while Pb, Cu, and Cr decreased by 44.15%, 5.16%, and 32.72% respectively. Strong correlations were observed between certain metals, particularly in wet periods, with notable relationships between Cr-Cu (<em>r</em> = 0.870) and Zn-Cr (<em>r</em> = 0.742). The integrated analysis of hydrological parameters and metal concentrations, using Gower coefficient and partial correlation analyses, suggests complex interactions between seasonal changes and pollutant dynamics. These findings highlight the need for season-specific water management strategies and more frequent monitoring to better understand and mitigate the environmental impacts of trace metal pollution in the Balu River system. The study contributes valuable insights into the interplay between hydrological conditions and trace metal behavior, essential for developing effective pollution control measures and achieving sustainable river management in Bangladesh.<strong></strong></p>

Seasonal dynamics of trace metal concentrations and hydrological parameters in the Balu River, Bangladesh

<p>This study investigates the seasonal dynamics of trace metal concentrations and hydrological parameters in the Balu River, Bangladesh, to inform sustainable water management practices and support efforts to meet environmental standards. Water level, discharge, rainfall, and groundwater data were collected from February to August 2019, alongside sediment samples analyzed for Zn, Pb, Cu, Cr, and Cd concentrations. Statistical analyses, including correlation studies, time series modeling, and Kolmogorov-Smirnov tests, revealed significant seasonal variations. Water levels and discharge rates increased dramatically during the wet season, with upstream levels rising by 862.68% and downstream by 752.92%. Trace metal concentrations showed diverse responses: Cd increased by 554.52%, Zn by 11.86%, while Pb, Cu, and Cr decreased by 44.15%, 5.16%, and 32.72% respectively. Strong correlations were observed between certain metals, particularly in wet periods, with notable relationships between Cr-Cu (<em>r</em> = 0.870) and Zn-Cr (<em>r</em> = 0.742). The integrated analysis of hydrological parameters and metal concentrations, using Gower coefficient and partial correlation analyses, suggests complex interactions between seasonal changes and pollutant dynamics. These findings highlight the need for season-specific water management strategies and more frequent monitoring to better understand and mitigate the environmental impacts of trace metal pollution in the Balu River system. The study contributes valuable insights into the interplay between hydrological conditions and trace metal behavior, essential for developing effective pollution control measures and achieving sustainable river management in Bangladesh.<strong></strong></p>

Temporal shift in groundwater fauna in southwestern Germany

Abstract. Groundwater is an important source of freshwater; drinking water; and service water for irrigation, industrial and geothermal uses. It is also the largest terrestrial freshwater biome in the world. In many areas, this habitat is naturally or anthropogenically threatened. This study uses long-term groundwater data from southwestern Germany to identify shifts in groundwater fauna due to natural or anthropogenic impacts. Comprehensive analysis of metazoan groundwater fauna and abiotic parameters from 16 monitoring wells over 2 decades revealed no overall temporal trends in faunal abundance or biodiversity (in terms of number of species) and no significant large-scale trends in abiotic parameters. While 9 wells out of 16 show stable ecological and hydrochemical conditions at a local level, the remaining wells exhibit shifting or fluctuating faunal parameters. At some locations, these temporal changes are linked to natural causes, such as decreasing dissolved oxygen contents or fluctuating temperatures. A multivariate PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) analysis suggests that, beside the hydrogeological setting, varying contents of sediment and detritus impact faunal abundance. By examining aerial images of the surroundings of individual wells, we found that anthropogenic impacts, such as construction sites and surface sealing, can cause significant shifts in groundwater fauna and changes in the ecological status in positive as well as negative direction. However, variable faunal compositions and abundances were also observed for sites with very stable abiotic conditions in anthropogenically less affected areas such as the Black Forest. These findings indicate that hydro(geo)logical changes and surface conditions, such as land use, should be assessed in line with hydrochemical parameters to better understand changes in groundwater fauna. Accordingly, reference sites for natural conditions in ecological assessment and biomonitoring schemes for groundwater protection should be selected carefully.

Multiple Data Imputation Methods Advance Risk Analysis and Treatability of Co-occurring Inorganic Chemicals in Groundwater.

Accurately assessing and managing risks associated with inorganic pollutants in groundwater is imperative. Historic water quality databases are often sparse due to rationale or financial budgets for sample collection and analysis, posing challenges in evaluating exposure or water treatment effectiveness. We utilized and compared two advanced multiple data imputation techniques, AMELIA and MICE algorithms, to fill gaps in sparse groundwater quality data sets. AMELIA outperformed MICE in handling missing values, as MICE tended to overestimate certain values, resulting in more outliers. Field data sets revealed that 75% to 80% of samples exhibited no co-occurring regulated pollutants surpassing MCL values, whereas imputed values showed only 15% to 55% of the samples posed no health risks. Imputed data unveiled a significant increase, ranging from 2 to 5 times, in the number of sampling locations predicted to potentially exceed health-based limits and identified samples where 2 to 6 co-occurring chemicals may occur and surpass health-based levels. Linking imputed data to sampling locations can pinpoint potential hotspots of elevated chemical levels and guide optimal resource allocation for additional field sampling and chemical analysis. With this approach, further analysis of complete data sets allows state agencies authorized to conduct groundwater monitoring, often with limited financial resources, to prioritize sampling locations and chemicals to be tested. Given existing data and time constraints, it is crucial to identify the most strategic use of the available resources to address data gaps effectively. This work establishes a framework to enhance the beneficial impact of funding groundwater data collection by reducing uncertainty in prioritizing future sampling locations and chemical analyses.

Spatial-temporal graph neural networks for groundwater data.

This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the nonlinearity and non-stationary characteristics of groundwater data. Our study leverages the capabilities of ST-GNNs to address these challenges in the Overbetuwe area, Netherlands. We utilize a comprehensive dataset encompassing 395 groundwater level time series and auxiliary data such as precipitation, evaporation, river stages, and pumping well data. The graph-based framework of our ST-GNN model facilitates the integration of spatial interconnectivity and temporal dynamics, capturing the complex interactions within the groundwater system. Our modified Multivariate Time Graph Neural Network model shows significant improvements over traditional methods, particularly in handling missing data and forecasting future groundwater levels with minimal bias. The model's performance is rigorously evaluated when trained and applied with both synthetic and measured data, demonstrating superior accuracy and robustness in comparison to traditional numerical models in long-term forecasting. The study's findings highlight the potential of ST-GNNs in environmental modeling, offering a significant step forward in predictive modeling of groundwater levels.

Challenges and opportunities for enhancing groundwater data access and usability in low- and middle-income countries: insights and recommendations from WaSH researchers and practitioners

ABSTRACT Groundwater provides more than half of all water for drinking and irrigation worldwide. Monitoring and managing groundwater resources is essential to achieve the Sustainable Development Goals. However, groundwater resources need to be better monitored. We explored practical approaches to improve groundwater monitoring for informed decision-making, focusing on low- and middle-income country settings. Case studies from a 2023 UNC Water and Health Conference side event highlighted innovative approaches, including open-source data modeling, groundwater monitoring in humanitarian contexts, and using in situ sensors to monitor drought resilience. The case studies described field data integration, remote sensing, and technology (e.g., Modflow-USG, ModelMuse, Drought Resilience Impact Platform). Barriers to groundwater data use were identified by side event participants, including inadequate data access, insufficient collaboration and leadership, and challenges in integrating diverse data and technology. Side event recommendations included creating a central groundwater monitoring data repository, building greater hydrogeology capacity in the WaSH sector, and increasing funding for data collection and monitoring. We urge government leaders to develop regulations and build internal capacity for sustainable groundwater management. This comprehensive approach aims to address challenges and promote informed decision-making for the long-term sustainability of groundwater resources.

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.

Urban Water Crisis in Kabul City: Key Challenges and Solutions

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

Multidisciplinary assessment of seasonal ground displacements at the Hatfield Moors gas storage site in a peat bog landscape

The study aims to analyse ground displacement conditions observed over an Underground Gas Storage (UGS) site located at Hatfield Moors (United Kingdom), with a focus on understanding its implications for decarbonization efforts. The location serves as an active onshore storage site and was used as an analogy to assess ground motion implications around Carbon Capture and Storage (CCS) by the British Geological Survey (BGS) as part of the SENSE (Assuring integrity of CO2 storage sites through ground surface monitoring) project. Given the value of continuous and real-time monitoring of ground movements induced by gas storage activities, the study leverages satellite Interferometric Synthetic Aperture Radar (InSAR) data to assess the environmental impact of UGS operations. Using free and open-source Sentinel-1 satellite data, ground motion patterns over Hatfield Moors are analysed, highlighting displacements ranging from − 5.0 to -10.0 mm/year within the peat bog. In addition, the Time Series (TS) of ground displacement from January 2018 to December 2022 reveals a seasonality in ground motion, with uplift observed in late winter and subsidence in late summer, showing a periodicity of approximately 1 year and a magnitude of +/-10.0 mm. Through in-depth analysis, the study highlights the need to understand the underlying causes of ground fluctuations at gas storage sites. This paper shows that InSAR has the versatility to integrate seamlessly with different monitoring tools and methodologies, opening avenues for comprehensive and holistic analyses. Cross-correlation analyses further elucidate temporal relationships between different datasets by evaluating InSAR time series, UGS injection/withdrawal data and piezometric data. This involves decomposing the TS into distinct components, including trend, seasonality and residuals. The case of Hatfield Moors shows a significant discrepancy between the UGS data and the InSAR TS, while also demonstrating a clear correlation between the groundwater data and the InSAR TS. By integrating insights from geology, hydrology and remote sensing technologies, the study navigates the complexities inherent in areas of overlapping phenomena. Accurate interpretation is essential for informed decision making, particularly at sites such as Hatfield Moors, where the convergence of natural peat motion and storage operations highlights the need for interdisciplinary analysis to understand the underlying causes of ground fluctuation.

Radiogenic 32P as a new dating tool to determine groundwater ages of 50–1000 years

Groundwater age has significant hydrogeological implication for groundwater renewal and solute transport. The period between 50 to 1000 years, marked by increased human activity, which is also a gap of groundwater dating. Cosmogenic 32Si with a half-life of 144±11 a, is the only viable dating tool for this range but is seldom used due to complex pretreatment and time-consuming and expensive detection. Radiogenic 32P, as a daughter isotope of 32Si, decays by emitting high-energy β- particles and is easily detected. 32Si and 32P reaches radioactive equilibrium within 3 months, meaning they maintain consistent activity levels in groundwater aged between 50 and 1000 years. Therefore, 32P can be used to date groundwater in the range of 50–1000 a. 32P is enriched and extracted using Mg(OH)2 co-precipitation method. The 32P solution is then mixed with a solvent mixture containing Triton X-100 and Permafluor and detected using an ultra-low background liquid scintillation spectrometer. The counting time ranges from 2500 to 3000 min with an efficiency of 65 %, and the 32P count peak surface is observed between channels 600 and 900. Comparing the 32P ages with the 14C and 32Si ages of groundwater samples from the Jianghan Plain, Central China, reveals that 32P ages and 14C ages exhibit similar trends, and the 32P ages and 32Si ages are approximately consistent in the range of 50–1000 a. Therefore, radiogenic 32P dating groundwater in the range of 50–1000 a is feasible, convenient and reliable.

A Multi-criterian Approach to Identify Groundwater Potential Zones in the Subarnarekha River Basin Using Integrated Analytical Hierarchy Process and Geospatial Technology

The intense extraction of groundwater and changing climate patterns have strained global groundwater supplies. As the demand for drinkable water rises worldwide, assessing groundwater availability and aquifer output becomes increasingly important. Geographic information systems (GIS) are being used more frequently for groundwater exploration due to their speed and provision of preliminary data. This study focuses on mapping groundwater availability in a minor tropical river basin in India, using a combination of GIS and the analytical hierarchy process (AHP). Ten thematic layers were generated and analyzed, including Geology, Geomorphology, Land use and land cover (LULC), Lineament density, Drainage density, Rainfall, Soil type, Slope, Topographic Wetness Index ( TWI), and curvature to delineate groundwater potential zones. AHP was employed to assign weights to each category based on their water retention properties. The resulting map was validated against existing groundwater data, demonstrating an accuracy of approximately 85%. The groundwater potential zones were classified as high, moderate, and, low. The moderate potential zone covered 59% of the basin, while the low and high potential zones constituted 29% and 11% respectively. High and low potential regions were limited to small sections of the basin. In conclusion, this research successfully mapped the groundwater availability in the Rarhu River watershed using GIS and AHP. The findings highlight the distribution of different groundwater potential zones, providing valuable information for sustainable water resource management in the area.

Wastewaters Coproduced with Shale Gas Drive Slight Regional Salinization of Groundwater.

While unconventional oil and gas (UOG) development is changing the world economy, processes that are used during UOG development such as high-volume hydraulic fracturing ("fracking") have been linked with water contamination. Water quality risks include leaks of gas and salty fluids (brines) that are coproduced at wellpads. Identifying the cause of contamination is difficult, however, because UOG wells are often colocated with other contaminant sources. We investigated the world's largest shale gas play with publicly accessible groundwater data (Marcellus Shale in Pennsylvania, U.S.A. with ∼29,000 analyses) and discovered that concentrations of brine-associated barium ([Ba]) and strontium ([Sr]) show small regional increases within 1 km of UOG development. Higher concentrations in groundwaters are associated with greater proximity to and density of UOG wells. Concentration increases are even larger when considering associations with the locations of (i) spill-related violations and (ii) some wastewater impoundments. These statistically significant relationships persist even after correcting for other natural and anthropogenic sources of salts. The most likely explanation is that UOG development slightly increases salt concentrations in regional groundwaters not because of fracking but because of the ubiquity of wastewater management issues. These results emphasize the need for stringent wastewater management practices across oil and gas operations.

Paleo-climatic control on recharge and fresh-salt groundwater distribution in the Red River delta plain, Vietnam

Paleo-climatic induced sedimentation controls present-day recharge and the fresh-salt groundwater distribution in Quaternary delta systems. During sea-level highstands, marine clays with saline pore water were deposited and are interbedded with aquifers of coarse-grained sandy fluvial and shallow marine deposits, laid down during lowstands. The low-permeable marine layers may inhibit recent recharge to deeper aquifers, and thereby limit sustainable use of these freshwater resources. This phenomenon has been investigated in the Red River delta plain, using geophysical borehole logging, transient electromagnetic soundings, groundwater chemistry, stable isotope analysis and 3H and 14C dating of groundwater. Results reveal that marine saline pore water is still present in the Holocene marine clays, implying that fresh water has not entered the clays since their deposition. Therefore, recharge within the delta plain is not occurring and the deeper aquifers are hydraulically disconnected from the upper sandy layers. Today, recharge only occurs from the hinterland. Recharge during the last glacial period has flushed saline pore water from Pleistocene marine clays, but these clays were again affected by saline water during the Holocene transgression. The use of the groundwater resources in the delta plain must be adjusted to the present recharge to be sustainable.