Gao et al. (2025) A Review of Urban Flood Disaster Chain Research: Causes, Identification, and Assessment

Identification

• Journal: Water
• Year: 2025
• Date: 2025-11-22
• Authors: Xichao Gao, Pengfei Wang, Zhiyong Yang, Weijia Liang, Wei Lou, Jinjun Zhou
• DOI: 10.3390/w17233344

Research Groups

• State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
• Ministry of Emergency Management Big Data Center, Beijing 100013, China
• State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
• College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China

Short Summary

This review synthesizes existing research on urban flood disaster chains, focusing on their formation mechanisms, identification methods, and risk assessment approaches. It highlights the need for unified quantitative frameworks and integrated models to enhance urban resilience against cascading flood impacts.

Objective

• To provide a comprehensive review of existing research on urban flood disaster chains, focusing on the latest advances in their formation mechanisms, identification methods, and risk assessment techniques, and to outline future research directions.

Study Configuration

• Spatial Scale: Global urban areas (reviewing concepts and methods applicable to cities worldwide).
• Temporal Scale: Historical disaster events to future perspectives (reviewing past research and proposing future directions).

Methodology and Data

• Models used: Event Tree Analysis (ETA), Complex Network Analysis, Bayesian Network (BN) modeling, Agent-Based Models (ABM), System Dynamics Models (SDM), 1D/2D coupled shallow-water solvers, physically based rainfall–runoff models, integrated hydrology-hydrodynamics platforms, Random Forest, Graph Convolutional Neural Network (GCN), Spiking Neural Network (SNN), Support Vector Machines (SVM), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM).
• Data sources: Historical disaster observations, documentary records, expert knowledge, social media posts, news reports, remote sensing images, government bulletins, field investigation reports, IoT sensor data, hydrometeorological observations, disaster loss statistics, topographic data, land use data, infrastructure networks.

Main Results

• Urban flood disaster chains are complex, cascading failures driven by rapid urbanization and climate change, involving interactions among hazard-inducing factors, disaster-formative environments, and disaster receptors.
• Formation mechanisms are categorized into disaster chain triggering (e.g., rainstorm-induced waterlogging, river overflow, structural instability, mountain floods) and transmission (physical, functional, informational pathways).
• Identification methods include qualitative experiential reasoning (e.g., ETA), semantic data-driven approaches (NLP, knowledge graphs), structural model inference (complex networks, Bayesian networks), and behavioral simulation modeling (ABM).
• Risk assessment approaches comprise historical disaster analysis, indicator-based models, uncertainty models (Bayesian networks, probabilistic graphical models), numerical simulation models (hydrodynamic models), and intelligent algorithm models (machine learning, AI).
• Current research lacks a unified quantitative framework for dynamic identification and assessment, and struggles with data scarcity and heterogeneity, particularly regarding spatiotemporal continuity and intrinsic coupling mechanisms.

Contributions

• Provides a systematic synthesis of existing knowledge on urban flood disaster chains, clarifying causal linkages and summarizing advances in identification and modeling techniques.
• Categorizes formation mechanisms into triggering and transmission processes, detailing their characteristics and dependencies.
• Classifies identification methods into four paradigms, offering a structured overview of current approaches.
• Categorizes risk assessment methods into five types, comparing their advantages and limitations.
• Outlines critical future research directions, emphasizing the need for integrated mathematical paradigms, multisource data fusion, causal reasoning, and hybrid models for real-time risk assessment.

Funding

• National Key R&D Program of China (grant number 2022YFC3090603)
• National Natural Science Foundation of China (grant number 52209044)
• Open Research Fund Program of State key Laboratory of Hydroscience and Engineering (grant number sklhse-2024-C-03)
• China Yangtze Power Co., Ltd. Research Project (grant number Z242302045)

Citation

@article{Gao2025Review,
  author = {Gao, Xichao and Wang, Pengfei and Yang, Zhiyong and Liang, Weijia and Lou, Wei and Zhou, Jinjun},
  title = {A Review of Urban Flood Disaster Chain Research: Causes, Identification, and Assessment},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17233344},
  url = {https://doi.org/10.3390/w17233344}
}