Advances and challenges in predicting wave runup in coastal regions: A scoping review of empirical, numerical, and AI-based approaches

Coastal regions, home to critical infrastructure and diverse ecosystems, are increasingly vulnerable to short-wave (incident-band) runup-induced hazards, including coastal erosion, flooding, and infrastructure damage. Accurately predicting short-wave runup is essential for effective coastal management, flood risk assessment, and climate adaptation strategies. This study presents a comprehensive scoping review of short-wave runup prediction methodologies, systematically evaluating empirical formulas, numerical models, and artificial intelligence (AI)-based approaches. We critically analyze their theoretical foundations, computational frameworks, and predictive capabilities under diverse coastal conditions. The review integrates a global wave runup dataset, providing a data-driven comparison of model performance across varying beach morphologies and hydrodynamic conditions. A key contribution of this review is conducting a cross-methodological evaluation, providing a structured assessment of the trade-offs between accuracy, computational demand, and real-world applicability across the three approaches. Furthermore, we examine the implications of climate change on wave runup prediction methodologies, emphasizing the effects of rising sea levels, changing storm characteristics, and changing wave energy on predictive reliability. The findings underscore the need for integrated modeling techniques to enhance predictive accuracy and support adaptive coastal management. By identifying research gaps and future directions, this review serves as a foundation for advancing wave runup prediction science, with direct applications in coastal engineering, risk mitigation, and climate resilience planning.