Advancing coastal flood risk assessment: a systematic review of global research trends, hazard processes, modelling approaches, and key uncertainties

Abstract

Coastal flood risk is intensifying globally due to accelerated sea level rise (SLR), stronger storms, and rapid coastal development. To provide an updated global perspective, this review synthesises research published between 2010 and 2024 through bibliometric analysis and full-text evaluation, examining the evolution of scientific priorities, modelling frameworks, and persistent challenges in coastal flood risk assessment. The results reveal rapidly increasing research output, expanding international collaboration, and growing conceptual sophistication; however, geographic disparities remain, particularly across highly vulnerable coastal regions in Africa and South America. The review indicates that coastal flood risk assessment has evolved from hazard identification toward more integrated and decision-oriented frameworks. Core drivers, particularly SLR, tides, and storm surges, are relatively well represented in current assessments, while increasing attention is being given to compound flooding, urban exposure, and adaptive planning. Modelling approaches have progressed from simplified representations to coupled hydrodynamic and integrated risk frameworks supported by high-resolution geospatial and socioeconomic datasets. Validation practices increasingly combine remote sensing, field observations, and social or crowdsourced information, although real-time and high-quality ground observations remain limited in many regions. Significant uncertainties persist, including the limited representation of land–sea hydrological coupling, the incomplete treatment of geomorphic feedbacks and human–environment interactions, uneven regional modelling capacity, and the growing algorithmic uncertainty associated with AI-driven approaches. Future progress will depend on advancing compound hazard modelling, integrating dynamic and context-specific environmental and socioeconomic data, strengthening multi-source validation, and developing adaptive risk assessment frameworks that better align model complexity with data availability and decision-making needs.