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

Yun Xing, Dong Shao (), Qigen Lin, Irfan Ullah, Junsheng Wang and Yuezhu Wang
Additional contact information
Yun Xing: Nanjing University of Information Science & Technology
Dong Shao: Chinese Academy of Sciences
Qigen Lin: Nanjing University of Information Science & Technology
Irfan Ullah: Hohai University
Junsheng Wang: Dalian Maritime University
Yuezhu Wang: Dalian Maritime University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 12, No 29, 6683-6700

Abstract: Abstract 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.

Keywords: Flood exposure; Hydrodynamic modelling; Urban flood; Fuzzy logic method (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-025-04267-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:39:y:2025:i:12:d:10.1007_s11269-025-04267-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-025-04267-7

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().