Data-Driven Stochastic Approach for Assessing Future Risk of Wave Overtopping in Coastal Defense Structures

Abstract

Rising sea levels due to climate change pose a significant threat to coastal regions, especially through wave overtopping, which can lead to hazardous flooding for coastal communities and emphasize the urgent need for advanced protection measures. This study presents a data-driven stochastic modeling framework combined with reliability analysis to improve risk assessments for coastal infrastructure facing uncertain future sea-level rise and climate impacts. The study’s contributions are twofold. First, it introduces a data-driven stochastic model to predict future sea levels at critical coastal sites, enabling early planning and timely intervention. Second, it presents a quantitative risk assessment of wave overtopping, establishing a reliability-based forecasting and warning system crucial for developing strategic coastal protection measures. In addition, seawall deterioration factors, such as area subsidence and seawall settlement, are examined to assess their impact on structural reliability. The Galveston Seawall in Texas is used as the testbed for this study to evaluate the model performance. The framework offers valuable insights for the design and evaluation of resilient coastal protection infrastructures, ensuring structural integrity and safeguarding public safety under uncertain future conditions posed by climate change.