A comparative assessment of flood mapping methods for urban risk management in data-poor environments

Fenosoa Nantenaina Ramiaramanana (), Jane-marie Muthoni Munyi, Pierre Archambeau and Jacques Teller
Additional contact information
Fenosoa Nantenaina Ramiaramanana: University of Liege
Jane-marie Muthoni Munyi: University of Twente
Pierre Archambeau: University of Liege
Jacques Teller: University of Liege

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 17, No 13, 19836 pages

Abstract: Abstract Flood mapping is essential to urban resilience, but it often relies on traditional hydrological models, which are poorly suited to data-scarce contexts due to their complexity and input requirements. This study, conducted in Antananarivo, Madagascar, evaluates alternative approaches combining remote sensing (Pleiades and Sentinel-1), simplified hydrological modeling (Fast Flood Simulation—FFS), and multicriteria analysis (MCA), with validation from field observations. The results show contrasting performances: FFS (10 cm threshold) detected 45% of flooded zones in the urban center, while Sentinel-1 identified only 3% due to poor performance in dense built-up areas. Pleiades was also affected by structural artifacts, despite its high resolution. MCA identified 40% of the city as being highly vulnerable. In agricultural zones, FFS captured over 80% of flooded areas, compared to 56% for MCA and less than 10% for Sentinel-1. In residential neighborhoods, flooded building detection ranged from under 1% (Sentinel-1) to 27% (FFS) and 37% (MCA). Overall, FFS emerged as the most suitable tool, providing flood depth estimates for operational decision-making, but its reliability depends heavily on input data quality and calibration processes. Satellite imagery offers complementary post-crisis validation but remains limited in dense urban environments. MCA supports strategic planning but cannot model event-specific dynamics. Combined within a GIS platform or interactive dashboard, these tools provide complementary capabilities across risk management phases—prevention (FFS, remote sensing, MCA), crisis response (FFS), and reconstruction (FFS, remote sensing)—offering decision-support solutions adapted to data-poor environments.

Keywords: Flood mapping; Remote sensing; Fast flood simulation; Multicriteria analysis; Data-poor environments; Antananarivo (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-025-07590-9 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:nathaz:v:121:y:2025:i:17:d:10.1007_s11069-025-07590-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-025-07590-9

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().