Geophysical investigation for seawater intrusion in the high-quality coastal aquifers of India: a review

Multiple proxies to investigate the submarine groundwater discharge into the Arabian Sea, Southwest coast, India: integration of biogeochemical, geophysical, and remote sensing techniques

Submarine Groundwater Discharge (SGD) constitutes a pivotal mechanism for the transference of freshwater, nutrients, and pollutants from terrestrial to marine environments, exerting a profound influence on coastal water quality and ecosystem dynamics. In this investigation, we executed an extensive field sampling campaign along the 650 km coastal expanse of southwest India, employing a 10-km sampling interval, to discern and validate the probable zones of SGD. We have utilized a transect-based methodology for the systematic collection of groundwater, porewater, and seawater samples, employing a suite of proxies to scrutinize SGD). This multifaceted approach encompassed biogeochemical, geophysical, and remote sensing techniques. The in situ physio-chemical parameters, encompassing electrical conductivity (EC), total dissolved solids (TDS), pH, dissolved oxygen (DO), temperature, and salinity, facilitated the delineation of prospective SGD sites. Adjacent continuous probable SGD sites were amalgamated into nine potential SGD zones spanning the 650 km coastal stretch. Comprehensive analyses of major ions and nutrients revealed maximum observed seawater concentrations of nitrate, phosphate, and silica at 22.11 µM/L, 12.5 µM/L, and 11.69 µM/L, respectively, underscoring the SGD signatures and the subsequent transference of nutrients from terrestrial sources to the ocean via subsurface pathways. Furthermore, geophysical investigations employing Electrical Resistivity Tomography (ERT) at the nine potential SGD zones substantiated the groundwater signatures, elucidating subsurface lithology, delineating the aquifer system, and determining the extent of the saline-freshwater interface, including discharge depth. All ERT profiles were meticulously calibrated against available lithological data. Additionally, we executed a comprehensive evaluation of Landsat-8 satellite imagery within the thermal infrared spectral domain (10.6-11.19 μm) to monitor variations in sea surface temperature (SST) and sea surface anomalies across three stratified thermal ranges (21-28 °C, 25-33 °C, and 11-23 °C) encompassing the entire study area. The visual correlation observed between lower SST values and the identified SGD probable zones further substantiates supplementary validation. Ultimately, the verification of these nine prospective SGD zones was reinforced through a meticulous comparison with groundwater level data, which ranged from 0 to 41 m above mean sea level (MSL). This extensive investigation represents the inaugural comprehensive identification and confirmation of SGD zones along the southwest coast of India, spanning a 650-km stretch, resulting in a more precise demarcation of the area into nine SGD probable zones where multiple proxies are mutually corroborative.

The growing trend of saltwater intrusion and its impact on coastal agriculture: Challenges and opportunities

Saltwater intrusion (SWI) into coastal agricultural lands represents a growing threat to agricultural productivity, ecosystem stability, and local economies. This phenomenon, affecting coastal surface and ground waters, is driven by intensified natural processes and anthropogenic factors under a changing climate. Here, we provide a comprehensive review of the drivers and trends of SWI and their impacts on the transition of coastal agricultural systems. We emphasize the importance of developing salt-tolerant crop varieties and implementing controlled environment agriculture to maintain agricultural productivity in affected coastal regions. Additionally, we discuss the role of marsh migration (i.e., allowing marshes to migrate into agricultural lands) in enhancing biodiversity and ecological resilience, and protecting remaining farmlands from SWI. This review highlights the urgent need for multidisciplinary research, strategic policy frameworks, and community engagement to ensure the sustainability of future coastal agriculture in the face of increasing SWI challenges.

Delineation of groundwater potential zones and its extent of contamination from the hard rock aquifers in west-Bengal, India

This study evaluates the groundwater potential and quality in the parts of Chhotanagpur Gneissic Complex situated in the East Indian Shield. The region has faced groundwater development challenges for several decades. Therefore, in the study area, it is crucial to address the depletion of both groundwater quality and quantity, as this facilitates the identification of potential uncontaminated groundwater zones. The present study interprets the groundwater potential zones (GWPZ) utilizing an analytical hierarchical process (AHP) integrated with hydrogeochemical analysis. Several thematic maps were prepared to delineate the GPWZ. It has been found that ∼0.6% of the study area has a very good potential zone, 14.4% has good, 52% has moderate, and approximately 32% and 0.9% have low to very low prospective groundwater resources, respectively. The authentication of results was found to be excellent (91.4%) with the Area Under Curve (AUC). Analysis of hydrogeochemical data suggests that Mixed Ca-Na-HCO3, Mixed Ca-Mg-Cl, Ca-HCO3, and Na-Cl are the dominant water types in the study area. The principal component analysis suggests that Na+, Mg2+, Cl-, NO3-, and SO42- significantly contribute to groundwater chemistry. The K-means clustering and hierarchical cluster analysis classified groundwater samples into three clusters based on the hydrogeochemical characteristics. It is inferred that silicate weathering and reverse ion reactions through rock-water interaction control geogenic processes for groundwater chemistry. It is also inferred that regions with poor to unsuitable water quality indexes also have low GWPZ. Further, groundwater for irrigation is also accessed and found unsuitable at some locations. This research contributes to comprehending groundwater characteristics in analogous geological regions globally. Additionally, it assists in implementing preventive actions to mitigate groundwater contamination, consequently lowering health risks and formulating sustainable plans for the future.

A framework for risk assessment of groundwater contamination integrating hydrochemical, hydrogeological, and electrical resistivity tomography method

Groundwater contamination have been widely concerned. To reliably conduct risk assessment, it is essential to accurately delineate the contaminant distribution and hydrogeological condition. Electrical resistivity tomography (ERT) has become a powerful tool because of its high sensitivity to hydrochemical parameters, as well as its advantages of non-invasiveness, spatial continuity, and cost-effectiveness. However, it is still difficult to integrate hydrochemical, hydrogeological, and ERT datasets for risk assessment. In this study, we develop a general framework for risk assessment by sequentially jointing hydrochemical, hydrogeological, and ERT surveys, while establishing petrophysical relationships among these data. This framework can be used in groundwater-contaminated site and help to delineate the distribution of contaminants. In this study, it was applied to a nitrogen-contaminated site where field ERT survey and borehole information provided valuable measurement data for validating the consistency of contamination and hydrogeological condition. Risk assessment was conducted based on the refined results by the establishment of relationship between conductivity and contaminants concentration withR 2 > 0.84 . The contamination source was identified and the transport direction was predicted with the good agreement ofR 2 = 0.965 between simulated and observed groundwater head, which can help to propose measures for anti-seepage and monitoring. This study thus enhances the reliability of risk assessment and prediction through a thought-provoking innovation in the realm of groundwater environmental assessment.

Prediction of soil salinity in the Upputeru river estuary catchment, India, using machine learning techniques

Soil salinization is a widespread phenomenon leading to land degradation, particularly in regions with brackish inland aquaculture ponds. However, because of the high geographical and temporal fluctuation, monitoring vast areas provides substantial challenges. This study uses remote sensing data and machine learning techniques to predict soil salinity. Four linear models, namely linear regression, least absolute shrinkage and selection operator (LASSO), ridge, and elastic net regression, and three boosting algorithms, namely XGB regressor, LightGBM, and CatBoost regressor, were used to predict soil salinity. Cross-validation was performed by splitting the data into 30% for model testing and 70% for model training. Multiple metrics such as determination coefficient (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) were used to compare the performances of these algorithms. By comparison, the CatBoost regressor model performed better than the other models in both testing (MAE = 0.42, MSE = 0.28, RMSE = 0.53, R2 = 0.92) and training (MAE = 0.49, MSE = 0.36, RMSE = 0.60, R2 = 0.90) phases. Hence, the CatBoost regressor model was recommended for monitoring soil salinity in India's massive inland aquaculture zone.