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A taxonomy-based understanding of community flood resilience

Bibliographic Data

ID4929144
AuthorsDipesh Chapagain (0000-0002-2418-6343), Stefan Hochrainer‐stigler (0000-0002-9929-8171), Stefan Hochrainer-Stigler, Stefan Velev (0009-0009-7096-1468), Adriana Keating (0000-0002-4016-067X), Jung Hee Hyun (0000-0001-6960-9277), Naomi Rubenstein, Reinhard Mechler (0000-0003-2239-1578)
Year2024
Volume29
Issue4
Publication date2024-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueConservation Ecology (JOURNAL)
Journal identifiersISSN: 1195-5449 • E-ISSN: 1708-3087
PublisherResilience Alliance (PUBLISHER • CA)
DOI10.5751/es-15654-290436
OpenAlexW4405317054
LanguageEN
Citations received1

Reducing disaster risk and enhancing resilience are major global societal challenges. To inform this challenge, understanding resilience at the community level is especially important because the impact of disasters and the potential for resilient development are particularly acute at this scale. The last decade has seen a surge in efforts in measuring resilience to a variety of hazards, yet measurement frameworks lack empirical validation and widespread application. To bridge this information gap, we provide analysis into an unprecedented dataset: a standardized, empirically validated approach to community flood resilience measurement, applied in over 290 communities across 20 developing countries. The analysis is based on the Flood Resilience Measurement for Communities (FRMC) framework and tool designed to provide a holistic approach to measuring community flood resilience and to support implementation of resilience-strengthening interventions. Our analysis starts with an assessment of the validity and reliability of the data and leads into querying whether and how to organize the wealth of information of community contexts into a discrete set of clusters. Although we appreciate that fostering resilience has to be strongly context-aware, we also present a taxonomy related to flood risk and socioeconomic community characteristics, which, using multinomial and random forest methods, leads us to identifying five distinct community clusters based on their resilience profiles and capital scores. This clustering taxonomy provides a way to group communities by similarities and differences between absolute and distributional resilience levels and socioeconomic community characteristics. These clusters may serve as a resource for further examining efforts for building resilience, analyzing resilience dynamics over time, and informing policy options across the world

Biology · Community resilience · Environmental planning · Environmental resource management · Flood myth · Geography · Computer Science · Disaster Management and Resilience · Environmental Science · Flood Risk Assessment and Management · Ecology

  • Realized resilience after community flood events

    Open Access•Dipesh Chapagain, Stefan Hochrainer‐stigler et al.•International Journal of Disaster…•2025

Unique citing works1
Citations per year1
Citation span2025 - 2025 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 1

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