Identifying Risk Transition Pattern of Compound Flooding Using the Copula Integrated Markov Chain

Xiaodi Li (), Ming Zhong (), Xueyou Li (), Jiao Wang (), Lu Zhuo (), Feng Ling (), Lixiang Song (), Xianwei Wang (), Jinhui Li () and Xiaohong Chen ()
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Xiaodi Li: Sun Yat-Sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Geography and Planning
Ming Zhong: Sun Yat-Sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Geography and Planning
Xueyou Li: Sun Yat-sen University, School of Civil Engineering
Jiao Wang: University of Bristol, Department of Civil Engineering
Lu Zhuo: Cardiff University, School of Earth and Environmental Sciences
Feng Ling: Chinese Academy of Sciences, Chinese Academy of Sciences Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei ProvinceInnovation Academy for Precision Measurement Science and Technology
Lixiang Song: Pearl River Water Resources Commission, Pearl River Water Resources Research Institute
Xianwei Wang: Sun Yat-Sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Geography and Planning
Jinhui Li: Harbin Institute of Technology (Shenzhen), Department of Civil and Environmental Engineering
Xiaohong Chen: Sun Yat-sen University, School of Civil Engineering

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 14, No 17, 7727-7748

Abstract: Abstract Compound flood has resulted in severe hazards under changing climate in coastal areas. Here, a novel transition framework integrating the Copula function and improved Markov chain is proposed for estimating the transition probability and identify different transition patterns of compound flooding events. Taking Modaomen waterway of the Pearl River as study area, results show that: (1) Bivariate probability of compound flood variates is computed by Copulas, in which Clayton Copula function is identified as the best fitting function for the bivariate joint distribution; (2) Based on the Kendall return periods, the combined thresholds for compound flooding with return periods of 5, 20, 50, and 100 years are determined through the maximum likelihood method; (3) transition probabilities matrices of multiple drivers in compound flooding are determined by Markov Chain, enhanced pattern and attenuated pattern have been identified, the compounding of storm surges and river floods exhibits the amplification pattern, while river floods and urban floods demonstrate the attenuation patterns in their interactions. This work makes a significant contribution to the advancement of early warning systems for compound flooding through its ability to forecast hazard transitions and support the prompt implementation of mitigation strategies.

Keywords: Compound flooding; Threshold; Transition probability; Copula; Markov chain (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04315-2

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