Gholizadeh Hossein, Clement T Prabhakar, Green Christopher T, Tick Geoffrey R, Plattner Alain M, Zhang Yong
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, 35487, USA.
Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, 35487, USA.
Sci Rep. 2025 Jul 1;15(1):21699. doi: 10.1038/s41598-025-06613-6.
Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. The long short-term memory network and wavelet analysis were used to assess vertical aquifer vulnerabilities, revealing that the shallow part of the Coastal lowlands aquifer system (CL1) in the southern Baldwin County region is more susceptible to sea level rise and groundwater extraction than deeper aquifers. Based on these findings, a cross-sectional numerical model (physics approach) for the CL1 aquifer was developed to evaluate tidal and storm surge effects, using Tropical Storm Claudette (June 2021) as a case study. Results showed that tidal fluctuations had a minimal impact on the saltwater-freshwater interface location, whereas storm surges caused substantial inland movement, with effects lasting for nine months. The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. The 3D physical model was also applied to a smaller area to assess human impact on the saltwater interface due to two groundwater pumping scenarios (± 50% of the baseline pumping rate). Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama's shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.
海水入侵威胁着沿海地区的地下水资源，包括阿拉巴马州鲍德温县南部，该地区淡水与咸水界面的动态变化仍知之甚少。为了填补这一空白，本研究使用基于物理和机器学习的组合模型来量化由自然（风暴潮）和人为（人类活动）扰动引起的海水入侵。采用长短期记忆网络和小波分析来评估含水层的垂直脆弱性，结果表明，鲍德温县南部沿海低地含水层系统（CL1）的浅层比深层含水层更容易受到海平面上升和地下水开采的影响。基于这些发现，以2021年6月的热带风暴克劳德特为例，开发了CL1含水层的横截面数值模型（物理方法）来评估潮汐和风暴潮的影响。结果表明，潮汐涨落对咸淡水界面位置的影响最小，而风暴潮导致大量海水向内陆移动，影响持续九个月。三维（3D）物理模型的稳态版本预测了整个区域的海水入侵情况，基于卷积神经网络的建模进一步验证了模型结果。3D物理模型还应用于一个较小的区域，以评估两种地下水抽取情景（±基线抽取率的50%）对咸水界面的人类影响。结果显示，地下水抽取量增加50%会导致海水向内陆推进约320米，而减少50%则会导致海水后退约270米。本研究强调了阿拉巴马州浅层沿海含水层因风暴潮和人类活动而容易受到海水入侵的影响，并表明将基于物理的模型与机器学习方法相结合可以改善地下水预测，但其准确性取决于特定地点数据的可用性。
