Spatiotemporal urban flood expansion and propagation by the percentage of nodes in flood assessment (PNFA)

Journal Article (2026)
Author(s)

Ha Do Minh (IHE Delft Institute for Water Education, Ha Noi Architectural University)

Gerald Augusto Corzo Perez (IHE Delft Institute for Water Education)

Wilmer Barreto (Catholic University of Temuco)

Chris Zevenbergen (TU Delft - Architecture and the Built Environment, IHE Delft Institute for Water Education)

Research Group
Urban Design
DOI related publication
https://doi.org/10.2166/nh.2026.046 Final published version
More Info
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Publication Year
2026
Language
English
Research Group
Urban Design
Journal title
Hydrology Research
Issue number
3
Volume number
57
Pages (from-to)
383-406
Downloads counter
14
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Abstract

Urban pluvial flooding is influenced by rainfall intensity, drainage capacity, and urban infrastructure. While prior studies mapped flood extents, the spatiotemporal dynamics of drainage saturation and overflow remain underexplored. This study introduces the percentage of node in flood assessment (PNFA) and flood expansion rate (FER) to quantify flood spread within drainage networks. Using EPA SWMM, three historical storms (2008, 2019, and 2020) and seven return-period scenarios were simulated in Do Lo, Hanoi, Vietnam. The results show that despite rainfall variability, spatiotemporal flood patterns remain stable, indicating predictable propagation pathways. FER values ranged from 0.17 (slow) to 0.902 (rapid), reflecting flood expansion speed under light to heavy rainfall. Spatiotemporal analysis produced hazard maps, capturing both spatial and temporal dimensions, revealing not only a persistent critical hotspot near AH13 but also consistent flood evolution patterns across various scenarios. These findings shift perspectives on flood preparedness by emphasizing timing, sequence, and hotspot stability. This study advances urban flood risk assessment through spatiotemporal hazard mapping, offering insights essential for mitigation, urban planning, and drainage resilience. Incorporating time-sensitive models is crucial for managing flood risks amid increasing extreme weather events.