Compound flood models in coastal areas: a review of methods and uncertainty analysis

Kui Xu, Chenyue Wang and Lingling Bin ()
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Kui Xu: Tianjin University
Chenyue Wang: Tianjin University
Lingling Bin: Tianjin Normal University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 116, issue 1, No 20, 469-496

Abstract: Abstract In the context of climate change and urbanization, flood becomes one of the most important threats to human life, health, and property. Coastal areas gathering large numbers of population, capital, and industries are vulnerable to suffering from the compound floods caused by hydrological and oceanic processes. The disaster mechanisms of compound floods are more complex, and the consequences are even more serious. Based on the existing research results, this article sorts out the main disaster mechanisms of compound floods in coastal areas and explains the main methods, including using statistical models to study the dependence between flood drivers or joint probability and numerical models to simulate compound flood inundation, and presents the characteristics of different methods. We also discuss the advantages and disadvantages of different models and analyze their uncertainties. Current research seldom considers the rainfall-runoff-storm surge compound flood and the effect of climate change. In addition, there are only a few kinds of literature that integrate statistical models and numerical models to investigate compound flood hazard. Uncertainties in compound flood study methods are also less considered. Future investigation should focus on the characteristics and uncertainties of different models and consider the impact of climate change on compound floods. These will help to fully understand compound floods, research models, and provide effective opinions for flood management in coastal areas.

Keywords: Compound flood; Disaster mechanism; Statistical model; Numerical model; Coastal areas (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05683-3

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