Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Author:

Sun Hai123ORCID,Zhang Xiaowei1,Ruan Xuejing4,Jiang Hui5,Shou Wenchi3

Affiliation:

1. College of Engineering, Ocean University of China, Qingdao 266100, China

2. Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, Qingdao 266100, China

3. School of Engineering, Design and Built Environment, Western Sydney University, Sydney 2745, Australia

4. College of Civil Engineering and Architecture, Qingdao Agricultural University, Qingdao 266109, China

5. Guangdong Provincial Seismological Bureau, Guangzhou 510070, China

Abstract

Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response to this research gap, we conducted a comprehensive bibliometric analysis and thorough visualization and mapping of studies of compound flooding risk in coastal cities over the period 2014–2022, using VOSviewer and CiteSpace to analyze 407 publications in the Web of Science Core Collection database. The analytical results reveal two persistent research topics: the way to explore the return periods or joint probabilities of flood drivers using statistical modeling, and the quantification of flood risk with different return periods through numerical simulation. This article examines critical causes of compound coastal flooding, outlines the principal methodologies, details each method’s features, and compares their strengths, limitations, and uncertainties. This paper advocates for an integrated approach encompassing climate change, ocean–land systems, topography, human activity, land use, and hazard chains to enhance our understanding of flood risk mechanisms. This includes adopting an Earth system modeling framework with holistic coupling of Earth system components, merging process-based and data-driven models, enhancing model grid resolution, refining dynamical frameworks, comparing complex physical models with more straightforward methods, and exploring advanced data assimilation, machine learning, and quasi-real-time forecasting for researchers and emergency responders.

Funder

National Natural Science Foundation of China

Qingdao Natural Science Foundation

Chinese Government Scholarship

Key R&D projects of Shandong Province

Publisher

MDPI AG

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