Harnessing risk-informed data for disaster and climate resilience

Bapon Fakhruddin*, Jenty Kirsch-Wood, Dev Niyogi, Li Guoqing, Virginia Murray, Nina Frolova

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Disaster and climate risks result from a complex interaction between hazard, exposure, and vulnerability in a broad context defined by socioeconomic, political, and ecological factors. To better understand the risk and manage it more effectively, we need to collect, store, analyse, and use risk-informed data. We identified challenges and opportunities for harnessing risk-informed data for disaster and climate resilience. The framework is inspired by the FAIR (findable, accessible, interoperable and reusable) and CARE (collective, authority to control, responsibility and ethics) principles to discuss opportunities how data could be available to inform risk-informed decision-making in climate and disaster risk management. Looking ahead, data could be developed and integrated with societal needs and participation. The use of data for risk management necessitates a common definition of risk to ensure a comparable research and development process. The world is shifting from a “for-profit” to a “for-benefit” operating model, which needs a Fifth Industrial Revolution driven by and for data for the benefit of society.

Original languageEnglish
Article number100254
JournalProgress in Disaster Science
Publication statusPublished - Dec 2022

Bibliographical note

Funding Information:
The authors would like to thank Tonkin + Taylor in partnership with Integrated Research on Disaster Risk (IRDR), the Committee on Data (CODATA) of the ISC for organising the webinar and the WORLD-FAIR project of EU to review supports. DN acknowledges discussions with NASA CyGNSS project at the University of Texas at Austin. Thanks to Dr. Neeraj Shankar from Tonkin + Taylor for providing editorial support.

Publisher Copyright:
© 2022 The Authors


  • Climate change
  • Data-driven
  • Disaster risk reduction
  • FAIR data
  • Risk management


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