Integrated Hydrogeological, Hydrochemical, and Isotopic Assessment of Seawater Intrusion into Coastal Aquifers in Al-Qatif Area, Eastern Saudi Arabia

Abstract

Seawater intrusion (SWI) is the main threat to fresh groundwater (GW) resources in coastal regions worldwide. Early identification and delineation of such threats can help decision-makers plan for suitable management measures to protect water resources for coastal communities. This study assesses seawater intrusion (SWI) and GW salinization of the shallow and deep coastal aquifers in the Al-Qatif area, in the eastern region of Saudi Arabia. Field hydrogeological and hydrochemical investigations coupled with laboratory-based hydrochemical and isotopic analyses (18O and 2H) were used in this integrated study. Hydrochemical facies diagrams, ionic ratio diagrams, and spatial distribution maps of GW physical and chemical parameters (EC, TDS, Cl−, Br−), and seawater fraction (fsw) were generated to depict the lateral extent of SWI. Hydrochemical facies diagrams were mainly used for GW salinization source identification. The results show that the shallow GW is of brackish and saline types with EC, TDS, Cl−, Br− concentration, and an increasing fsw trend seaward, indicating more influence of SWI on shallow GW wells located close to the shoreline. On the contrary, deep GW shows low fsw and EC, TDS, Cl−, and Br−, indicating less influence of SWI on GW chemistry. Moreover, the shallow GW is enriched in 18O and 2H isotopes compared with the deep GW, which reveals mixing with recent water. In conclusion, the reduction in GW abstraction in the central part of the study area raised the average GW level by three meters. Therefore, to protect the deep GW from SWI and salinity pollution, it is recommended to implement such management practices in the entire region. In addition, continuous monitoring of deep GW is recommended to provide decision-makers with sufficient data to plan for the protection of coastal freshwater resources.

Effects of Seawater Intrusion on the Groundwater Quality of Multi-Layered Aquifers in Eastern Saudi Arabia

“…Water samples were collected from observation and production wells. The observation wells tapped the shallow aquifer, which ranged in depth between 5–10 m. Production wells ranged in depth between 70–130 m, producing water from the middle and deep aquifers [ 13 , 25 , 26 ]. Water samples were taken from the wells after the water was pumped until the EC stabilized.…”

“…It has a flat coastal plain, with surface elevations rising westward to a height of approximately 12 m. The study area consists of urban and farmland areas. However, in recent years, urban areas have encroached on croplands [ 13 ]. Water for irrigation on Qatif and Tarout Island is mostly collected from multilayered aquifers, including shallow, middle, and deep aquifers [ 25 , 26 ].…”

“…The SAR indices reflect the relative concentrations of Na + , Ca 2+ , and Mg 2+ ions. Water quality can be classified into four categories based on the SAR values: excellent (<10), good (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), permissible , and unsuitable (>40) [48,49]. The SAR index was computed as follows:…”

“…In coastal regions worldwide, GW resources are at risk of salinization owing to global warming and seawater intrusion (SWI) [ 7 , 8 ]. Rapid population growth, urbanization, industrialization, and agricultural development have all contributed to rising water demand [ 9 , 10 , 11 , 12 , 13 ]. As a result, seawater intrudes into coastal aquifers, changing GW salinity and quality to a level at which water becomes unsuitable for human consumption and agricultural use [ 14 , 15 , 16 , 17 ].…”

Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique

“…These techniques were composed of four non-linear (GPR, SVR, ANFIS, and GRNN) and one classical linear method to inform of ILR. Furthermore, based on different researches depicted in the recent technical literature for simulation, modelling, prediction and forecasting, the AI-based models usually demonstrate higher performance than the classical linear method, as demonstrated by [ 16 , 17 , 18 , 19 ].…”