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<p>Floods pose a significant global challenge, endangering lives, property, and ecological stability, particularly in urban environments. Assessing flood exposure requires integrating hydrodynamic, demographic, property, and infrastructure data, which vary in source and resolution, prompting questions about the necessity of high-resolution hydrodynamic modeling for effective flood exposure assessments. This study examines how spatial resolution impacts the accuracy and reliability of flood exposure assessments in urban areas, where complex interactions between floods and urban topographies complicate resolution effect analyses. A novel framework is proposed to evaluate spatial resolution effects on urban flood exposure, balancing accuracy with operational efficiency to enhance disaster prevention and emergency response. The framework combines direct statistical analysis and fuzzy logic to assess the quantitative and qualitative impacts of hydrodynamic modeling resolutions. Results indicate that while finer resolutions provide detailed flood dynamics and precise inundation estimates, coarser resolutions can suffice for exposure assessments, improving forecast efficiency for urban flood management. Fuzzy logic enhances the interpretation of flood risks across resolutions, offering flexibility in modeling choices. Additionally, integrating machine learning techniques, such as random forests, with traditional hydrodynamic models improves predictive accuracy, addressing computational and data constraints. This study advances the understanding of urban flood exposure assessments and provides a scalable approach to optimize flood management strategies.</p>

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Towards More Efficient Urban Flood Assessment: Issue of Spatial Resolution in Urban Flood Hydrodynamic Modeling from Flood Exposure Perspective

  • 期刊论文

  • 发表日期：

    2025年6月21日

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  • Yun Xing,
  • Dong Shao,
  • Qigen Lin,
  • Irfan Ullah,
  • Junsheng Wang,
  • Yuezhu Wang

摘要

Floods pose a significant global challenge, endangering lives, property, and ecological stability, particularly in urban environments. Assessing flood exposure requires integrating hydrodynamic, demographic, property, and infrastructure data, which vary in source and resolution, prompting questions about the necessity of high-resolution hydrodynamic modeling for effective flood exposure assessments. This study examines how spatial resolution impacts the accuracy and reliability of flood exposure assessments in urban areas, where complex interactions between floods and urban topographies complicate resolution effect analyses. A novel framework is proposed to evaluate spatial resolution effects on urban flood exposure, balancing accuracy with operational efficiency to enhance disaster prevention and emergency response. The framework combines direct statistical analysis and fuzzy logic to assess the quantitative and qualitative impacts of hydrodynamic modeling resolutions. Results indicate that while finer resolutions provide detailed flood dynamics and precise inundation estimates, coarser resolutions can suffice for exposure assessments, improving forecast efficiency for urban flood management. Fuzzy logic enhances the interpretation of flood risks across resolutions, offering flexibility in modeling choices. Additionally, integrating machine learning techniques, such as random forests, with traditional hydrodynamic models improves predictive accuracy, addressing computational and data constraints. This study advances the understanding of urban flood exposure assessments and provides a scalable approach to optimize flood management strategies.