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<p>In the 21st century, coastal megacities and port cities are crucial for economic growth but face increasing flood risks from multiple drivers. With over half of the earth’s inhabitants residing within 100&#xa0;km of the coastline, flood poses a significant and critical threat to lives, capital, and industries. Compound flood events result from driving factors, i.e., weather, oceanic conditions, hydrologic and meteorological indicators, as well as unplanned human development, and this risk is anticipated to rise in how often it occurs and how serious it becomes. Compound flood has intricate mechanisms and severe consequences. This study reviews the primary disaster mechanisms in coastal areas, discusses numerical models, statistical models, copula functions, machine learning approaches, and driver dependencies for inundation simulation, and highlights the distinct features of these approaches based on existing research. The uncertainties of the assessment methods are also reviewed. Future research should prioritize understanding the model characteristics and uncertainties to enhance comprehension and model development and compound flood resilience strategies in coastal areas.</p>

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Compound flooding: A review on assessment framework

  • 期刊论文

  • 发表日期：

    2025年11月12日

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  • Aysha Akter,
  • S. M. Ataullah

摘要

In the 21st century, coastal megacities and port cities are crucial for economic growth but face increasing flood risks from multiple drivers. With over half of the earth’s inhabitants residing within 100 km of the coastline, flood poses a significant and critical threat to lives, capital, and industries. Compound flood events result from driving factors, i.e., weather, oceanic conditions, hydrologic and meteorological indicators, as well as unplanned human development, and this risk is anticipated to rise in how often it occurs and how serious it becomes. Compound flood has intricate mechanisms and severe consequences. This study reviews the primary disaster mechanisms in coastal areas, discusses numerical models, statistical models, copula functions, machine learning approaches, and driver dependencies for inundation simulation, and highlights the distinct features of these approaches based on existing research. The uncertainties of the assessment methods are also reviewed. Future research should prioritize understanding the model characteristics and uncertainties to enhance comprehension and model development and compound flood resilience strategies in coastal areas.