Coastal Groundwater Research Articles

Seasonal coastal groundwater dynamics in Lahaina beaches, Hawai'i: Implications for contaminant transport in a post-wildfire setting.

Coastal aquifers are essential sources of freshwater for domestic, agricultural, and industrial use in many regions worldwide. However, these critical resources are increasingly threatened by seawater intrusion, a hydrogeological process exacerbated by excessive groundwater extraction, population growth, and climate change. A comprehensive understanding of the dynamics and research evolution of this phenomenon is vital to support the sustainable management of coastal water resources. This study presents a bibliometric analysis of global research on seawater intrusion in coastal aquifers from 2014 to 2024. Data were retrieved from the Scopus database and analysed using the Bibliometrix package in R, complemented by visualization techniques developed in Python. The analysis examines publication trends, identifies influential authors, institutions, and journals, explores international collaboration networks, and maps the thematic evolution of research topics. By applying key bibliometric indicators and established laws such as those of Bradford and Lotka, the study provides an integrated perspective on the structure, dynamics, and development of this research field.The findings contribute to a deeper understanding of global scientific efforts addressing seawater intrusion and offer insights to guide future research, technological innovation, and policy initiatives for the sustainable management of coastal groundwater resources.

Nutrient fluxes from rivers and groundwater into an urban bay of the New York-New Jersey Harbor Estuary.

Diffuse nitrate (NO₃−) contamination is a critical environmental concern threatening the quality of coastal groundwater resources, particularly in regions undergoing agricultural intensification and rapid land use changes. This study presents an explainable deep learning framework for predicting nitrate concentrations and identifying areas at risk of elevated contamination. The framework integrates key hydrochemical parameters electrical conductivity (EC), chloride (Cl−), organic matter (OM), and fecal coliforms (FC) with remote-sensing derived indicators, including the Normalized Difference Vegetation Index (NDVI) and land use/land cover (LU/LC). Two deep learning models were evaluated in this study: a Multilayer Perceptron (MLP) and TabNet, a novel attention-based architecture for interpretable tabular data. TabNet outperformed MLP, achieving an overall accuracy of 81.60% and a Macro-averaged recall of 84.13%, while providing transparent feature attribution. LASSO regression identified FC (0.52) and EC (0.48) as dominant predictors, highlighting the combined influence of domestic wastewater and agricultural runoff on nitrate contamination. The output risk maps revealed spatially heterogeneous contamination patterns, with hotspots concentrated in agricultural and peri-urban areas. This research highlights the importance of integrating explainable AI with geospatial analysis to guide targeted groundwater monitoring and management strategies. This approach is transferable to other vulnerable coastal aquifers, supporting sustainable groundwater governance under diffuse pollution conditions.

Microbial community dynamics across salinity gradients in coastal aquifers: Linking hydrogeochemical variability to prokaryotic diversity in a seawater-intruded aquifer of the Pearl River Delta, China.

Impact of the 2023 floods on microplastic contamination in coastal groundwater: a comparative analysis of chennai’s pre- and post-flood conditions

Nitrate Dynamics in Coastal groundwater and rivers: Insights into Oligotrophication Management

Seawater intrusion angle controls colloidal chromium migration across coastal groundwater interfaces.

Seawater intrusion changes the chemical composition of fresh water in coastal groundwater and surface water sources, leading to the formation of additional brominated and iodinated disinfection byproducts (DBPs) in drinking water. In this study, impact of seawater intrusion on trihalomethanes (THMs) and haloacetic acids (HAAs) concentrations was assessed through mixing 0.0 to 2.0% seawater (SW) to groundwater (GW). Human exposure and risks were predicted for selected DBPs, which were then used to estimate the loss of disability-adjusted life years (DALY). The initial concentrations of bromide and iodide ions in GW without seawater were 42.5µg/L and non-detectable (ND), respectively. For 2.0% SW, the concentrations were increased up to 1100 and 2.1µg/L respectively. For 0.0% SW, averages of regulated THMs, HAAs and iodinated THMs (I-THMs) were 30.4, 27.9 and 0.13µg/L respectively while these concentrations were 106.3, 72.9 and 1.6µg/L in the samples with 2.0% SW respectively. From 0.0% to 2.0% SW, bromoform (TBM) and iodoform levels were increased up to 94.3 and 1.02µg/L respectively. For HAAs, tribromoacetic acid was increased from 2.0 to 43.7µg/L. The overall cancer risk from selected DBPs was 3.09 × 10⁻5 for 0.0% SW, while at 1.0% and 2.0% SW, the risks were 4.88 × 10⁻5 and 4.11 × 10⁻5 respectively. The loss of DALY was increased for up to 1.0%SW (77.30 at 0.0%SW and 122.0 at 1.0%SW), which was decreased to 102.8 at 2.0%SW. Future study is warranted to better control emerging DBPs in the coastal regions.

Tidal pumping controls transport of foodborne microbial pathogens between coastal groundwater and seawater.

Coastal lowlands are increasingly vulnerable to threats from sea-level and associated groundwater rise. This study introduces a categorical modeling framework that redefines groundwater depth estimation as a classification problem rather than a continuous prediction task. By dividing groundwater occurrence into multiple depth thresholds (0.9-2.0 m), the approach explicitly quantifies prediction uncertainty through Type I (false positive) and Type II (false negative) errors. A national-scale ensemble model developed at 100 m resolution using the Random Forest algorithm was trained on New Zealand's comprehensive depth-to-water database. Thirty-seven predictor variables, derived via PCA (97.5% variance retained) from 199 base predictors, were incorporated to capture the complex interactions influencing groundwater depth. The model demonstrates strong performance, with ROC-AUC values ranging from 0.823 to 0.962, and accuracy improves with increasing depth. This categorical framework addresses challenges associated with data imbalance and enhances uncertainty quantification compared to traditional regression methods. Probabilistic predictions allow stakeholders to set customizable risk thresholds and manage acceptable error levels based on specific coastal management contexts. By bridging the gap between advanced numerical modeling and practical adaptation planning, the approach provides a robust tool for evidence-based decision making in the face of rising sea levels.

Stable strontium (δ88/86Sr) and calcium (δ44/40Ca) isotope fractionation in coastal groundwater and its implications for the transport of dissolved cations to the ocean

A hybrid approach for identifying the seasonal variation of groundwater quality, source apportionment and health risk in a coastal area driven by natural and anthropogenic factors.

Controls of paleosedimentary environments and anthropogenic activities on coastal groundwater salinization: A case study of Laizhou Bay, China

Reliable modeling of saltwater intrusion (SWI) into freshwater aquifers is essential for the sustainable management of coastal groundwater resources and the protection of water quality. This study evaluates the performance of four Bayesian-optimized gradient boosting models in predicting the SWI wedge length ratio (L/La) in coastal sloping aquifers with underground barriers. A dataset of 456 samples was generated through numerical simulations using SEAWAT, incorporating key variables such as bed slope, hydraulic gradient, relative density, relative hydraulic conductivity, barrier wall depth ratio, and distance ratio. The dataset was divided into 70% for training and 30% for testing. Model performance was assessed using both visual and quantitative metrics. Among the models, Light Gradient Boosting (LGB) achieved the highest predictive accuracy, with RMSE values of 0.016 and 0.037 for the training and testing sets, respectively, and the highest coefficient of determination (R²). Stochastic Gradient Boosting (SGB) followed closely, while Categorical Gradient Boosting (CGB) and eXtreme Gradient Boosting (XGB) showed slightly higher error rates. SHapley Additive exPlanations (SHAP) analysis identified relative barrier wall distance and bed slope as the most influential features affecting model predictions. To support practical application, an interactive graphical user interface (GUI) was developed, allowing users to input key variables and easily estimate L/La values. Finally, the best-performing model was validated against the Akrotiri coastal aquifer in Cyprus, a realistic benchmark case derived from numerical simulations. The model's predictions showed strong agreement with reference results, achieving an RMSE of 0.04, thereby confirming its practical applicability. This study highlights the potential of interpretable, optimized ML models to enhance SWI prediction and support informed decision-making in coastal aquifer management.

Excessive geogenic ammonium (NH4+-N), derived from the mineralization of naturally nitrogen-containing organic matter (OM), has gained increasing attention, particularly in coastal aquifers. However, the mechanisms linking groundwater NH4+-N enrichment to salinity and its interaction with dissolved organic matter (DOM) and soluble organic matter (SOM) remain unclear. In this study, the molecular characteristics of DOM and SOM in the Pearl River Delta aquifer systems were analyzed by using ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry. Comparative incubation experiments were conducted to investigate DOM/SOM degradation pathways, their contribution to NH4+-N enrichment, and effect of salinity on DOM/SOM degradation. Results showed that DOM and SOM degradation involved the progressive degradation of aliphatic compounds, highly unsaturated compounds (HUC), and polyphenols (PPE) with lower O/C to HUC and PPE with higher O/C. Four major deamination reactions (hydrolytic deamination, oxidative deamination, reductive deamination, and ammonia elimination) were identified, and both DOM and SOM contributed similar to NH4+-N enrichment. Notably, salinity was found to influence the degradation pathways, facilitating the deamination of DOM/SOM by altering the molecular composition and microbial community structure. This study enhances the understanding of geogenic NH4+-N enrichment mechanisms in coastal groundwater and underscores the potential pollution risks associated with groundwater salinization.

Hydrodynamic and stratigraphic evaluation of marine saline intrusion into the coastal groundwater systems of a major crude oil production region in Southern Nigeria

This work addresses important problems with groundwater sustainability in coastal farming areas, where seawater intrusion compromises the quality of irrigation water. The study gives real-world examples of how hydrochemical levels change with the seasons, uses the usual AICRP categorization for irrigation appropriateness, and identifies areas in Thiruchendur taluk that are at high risk. Its GIS-based technique and block-by-block analysis give policymakers and farmers useful information on how to use climate-resilient water management methods. The results are very important for semi-arid areas around the world that are experiencing salinity problems because of climate change and excessive extraction. The groundwater quality was assessed by collecting 43 ground water samples during pre as well as post monsoon season and suitability of irrigation water was established based on AICRP classification. All the groundwater samples were analysed for pH, EC, anions (CO3, HCO3, Cl-, SO42-) and cations (Ca2+, Mg2+, K+ and Na+). The pH of the groundwaters of Thiruchendur taluk indicated that there was no much variation in pH of water samples collected during pre and post monsoon seasons. However, there was a wide variation noticed in EC values of groundwater, between blocks and seasons. The well waters of Thiruchendur and Udangudi blocks were of Na-Mg-Ca and Cl- SO4- HCO3 whereas, the groundwaters of Alwarthirunagari block were of Na- Ca-Mg and Cl- HCO3 - - SO4 type. In higher salinity range, sodium was associated mainly with chloride and sulphate. It is concluded that most of the well waters in Thiruchendur and Udangudi blocks are intruded with sea water and are of poor in quality. The well waters Alwarthirunagari block are likely to be vulnerable to intrusion. The well waters of Thiruchendur and Udangudi blocks should be considered seriously since the quality parameters are nearer to the composition of sea water in sodium chloride and magnesium sulphate combination. The water samples of Alwarthirunagari block fall near composition of sea water in sodium chloride combination and they may get affected by sea water intrusion after sometime. According to the AICRP suitability classification, during the pre-monsoon season, 51%, 23%, 19%, and 7% of the water samples from the entire study area were categorized as good, high SAR saline, marginally saline, and saline, respectively. In contrast, during the post-monsoon season, 65% of the samples were classified as good.

Abstract. Coastal zones are increasingly acknowledged as dynamic yet fragile components of global ecosystems amidst escalating anthropogenic activities and complex land–ocean interactions. Understanding the interactions between groundwater and the ocean is crucial for managing submarine groundwater discharge (SGD) and seawater intrusion (SWI), vital for coastal ecosystem preservation and water resource management. This research proposes an integrated modeling approach that couples groundwater flow and physical oceanographic models to accurately simulate coastal-ocean–groundwater interactions. In this work, a TELEMAC-3D-based three-dimensional hydrodynamic model was initially developed to capture marine conditions with variable salinity and temperature. A MODFLOW 6 groundwater model was subsequently constructed. The models were efficiently coupled using FloPy and TelApy, enabling precise co-simulation of hydrodynamic and groundwater systems. Validation of the coupled model against empirical data confirmed its high fidelity, with errors within acceptable ranges. This coupled model employs dynamic boundary conditions, overcoming the limitations of traditional coastal groundwater models that assume constant salinity. This enhancement significantly improves the accuracy and practicality of simulating SGD processes in the coastal ocean. The bidirectional feedback mechanism within the coupled model strengthens the analysis of interactions between the ocean and groundwater systems. It accounts for variations in the seawater boundary under tidal influence and the reciprocal impact of groundwater dynamics on the hydrodynamic conditions of nearshore waters. This holistic enhancement bolsters the model's hydrological simulation capabilities, providing a more comprehensive depiction of the intricate water–salt exchange mechanisms in coastal systems.

Is Kerala’s coastal groundwater getting salinized?