The Impact of Climate Change and Urbanization on Compound Flood Risks in Coastal Areas: A Comprehensive Review of Methods

被引:1
|
作者
Ruan, Xuejing [1 ,2 ]
Sun, Hai [2 ,3 ]
Shou, Wenchi [2 ]
Wang, Jun [2 ]
机构
[1] Qingdao Agr Univ, Coll Civil Engn & Architecture, Qingdao 266109, Peoples R China
[2] Western Sydney Univ, Sch Engn Design & Built Environm, Sydney 2745, Australia
[3] Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 21期
基金
中国国家自然科学基金;
关键词
climate change; urbanization; compound flood; numerical simulations; machine learning; SEA-LEVEL RISE; STORM-SURGE; LAND SUBSIDENCE; FUTURE; MODELS; INUNDATION; PROJECTION; RAINFALL; PATTERN; CITIES;
D O I
10.3390/app142110019
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Many cities worldwide are increasingly threatened by compound floods resulting from the interaction of multiple flood drivers. Simultaneously, rapid urbanization in coastal areas, which increases the proportion of impervious surfaces, has made the mechanisms and simulation methods of compound flood disasters more complex. This study employs a comprehensive literature review to analyze 64 articles on compound flood risk under climate change from the Web of Science Core Collection from 2014 to 2024. The review identifies methods for quantifying the impact of climate change factors such as sea level rise, storm surges, and extreme rainfall, as well as urbanization factors like land subsidence, impervious surfaces, and drainage systems on compound floods. Four commonly used quantitative methods for studying compound floods are discussed: statistical models, numerical models, machine learning models, and coupled models. Due to the complex structure and high computational demand of three-dimensional joint probability statistical models, along with the increasing number of flood drivers complicating the grid interfaces and frameworks for coupling different numerical models, most current research focuses on the superposition of two disaster-causing factors. The joint impact of three or more climate change-driving factors on compound flood disasters is emerging as a significant future research trend. Furthermore, urbanization factors are often overlooked in compound flood studies and should be considered when establishing models. Future research should focus on exploring coupled numerical models, statistical models, and machine learning models to better simulate, predict, and understand the mechanisms, evolution processes, and disaster ranges of compound floods under climate change.
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页数:22
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