Data-driven recommendations for enhancing real-time natural hazard warnings

Kate Saunders; Owen Forbes; Jess K. Hopf; Charlotte R. Patterson; Sarah A. Vollert; Kaitlyn Brown; Raiha Browning; Miguel A. Canizares; Richard S. Cottrell; Lanxi Li; Catherine J.S. Kim; Tace P. Stewart; Connie Susilawati; Xiang Y. Zhao; Kate J. Helmstedt

doi:10.1016/j.oneear.2025.101274

Data-driven recommendations for enhancing real-time natural hazard warnings

Kate Saunders, Owen Forbes, Jess K. Hopf, Charlotte R. Patterson, Sarah A. Vollert, Kaitlyn Brown, Raiha Browning, Miguel A. Canizares, Richard S. Cottrell, Lanxi Li, Catherine J.S. Kim, Tace P. Stewart, Connie Susilawati, Xiang Y. Zhao, Kate J. Helmstedt

Econometrics & Business Statistics

Research output: Contribution to journal › Article › Research › peer-review

Abstract

The effectiveness of natural hazard warnings relies on transforming the available data into actionable knowledge for the public. Real-time warning communication and emergency response therefore need to be evaluated from a data-driven perspective. However, gaps exist between established data science best practices and their application in supporting natural hazard warnings. This perspective reviews existing data-driven approaches, highlighting limitations in hazard and impact warnings. Four main themes emerge for enhancing warning communication and supporting decision-making: (1) identifying data-barriers to effective warnings, (2) applying best-practice principles in visualizing warnings, (3) utilizing novel data for more localized forecasts and warnings, and (4) improving data-driven decision-making using uncertainty. These themes are illustrated using the extensive flooding in Australia in 2022 as a case study. This perspective reveals opportunities for improving the efficacy of natural hazard warnings using data science, and the collaborative potential between the data science and natural hazards communities.

Original language	English
Article number	101274
Number of pages	15
Journal	One Earth
Volume	8
Issue number	5
DOIs	https://doi.org/10.1016/j.oneear.2025.101274
Publication status	Published - 2025

Keywords

natural hazards
natural disasters
data science
data analytics
data visualization
warnings

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10.1016/j.oneear.2025.101274Licence: CC BY-NC

692997209-oaFinal published version, 4.29 MBLicence: CC BY-NC

ARC Centre of Excellence for Climate Extremes
Pitman, A. J. (Primary Chief Investigator (PCI)), Jakob, C. (Chief Investigator (CI)), Alexander, L. V. (Chief Investigator (CI)), Reeder, M. (Chief Investigator (CI)), Roderick, M. (Chief Investigator (CI)), England, M. H. (Chief Investigator (CI)), Abramowitz, G. (Chief Investigator (CI)), Abram, N. (Chief Investigator (CI)), Arblaster, J. (Chief Investigator (CI)), Bindoff, N. L. (Chief Investigator (CI)), Dommenget, D. (Chief Investigator (CI)), Evans, J. P. (Chief Investigator (CI)), Hogg, A. M. (Chief Investigator (CI)), Holbrook, N. (Chief Investigator (CI)), Karoly, D. J. (Chief Investigator (CI)), Lane, T. (Chief Investigator (CI)), Sherwood, S. C. (Chief Investigator (CI)), Strutton, P. (Chief Investigator (CI)), Ebert, E. (Partner Investigator (PI)), Hendon, H. (Partner Investigator (PI)), Hirst, A. C. (Partner Investigator (PI)), Marsland, S. (Partner Investigator (PI)), Matear, R. (Partner Investigator (PI)), Protat, A. (Partner Investigator (PI)), Wang, Y. (Partner Investigator (PI)), Wheeler, M. (Partner Investigator (PI)), Best, M. J. (Partner Investigator (PI)), Brody, S. (Partner Investigator (PI)), Grabowski, W. (Partner Investigator (PI)), Griffies, S. (Partner Investigator (PI)), Gruber, N. (Partner Investigator (PI)), Gupta, H. (Partner Investigator (PI)), Hallberg, R. (Partner Investigator (PI)), Hohenegger, C. (Partner Investigator (PI)), Knutti, R. (Partner Investigator (PI)), Meehl, G. A. (Partner Investigator (PI)), Milton, S. (Partner Investigator (PI)), de Noblet-Ducoudre, N. (Partner Investigator (PI)), Or, D. (Partner Investigator (PI)), Petch, J. (Partner Investigator (PI)), Peters-Lidard, C. (Partner Investigator (PI)), Overpeck, J. (Partner Investigator (PI)), Russell, J. (Partner Investigator (PI)), Santanello, J. (Partner Investigator (PI)), Seneviratne, S. I. (Partner Investigator (PI)), Stephens, G. (Partner Investigator (PI)), Stevens, B. (Partner Investigator (PI)), Stott, P. A. (Partner Investigator (PI)) & Saunders, K. (Chief Investigator (CI))
Monash University – Internal University Contribution, Monash University – Internal School Contribution, Monash University – Internal Faculty Contribution, University of New South Wales (UNSW), Australian National University (ANU), University of Melbourne, University of Tasmania, Bureau of Meteorology (BOM) (Australia), Department of Climate change, Energy, the Environment and Water (DCCEEW) (New South Wales)
1/01/17 → 31/12/24
Project: Research

Cite this

Saunders, K., Forbes, O., Hopf, J. K., Patterson, C. R., Vollert, S. A., Brown, K., Browning, R., Canizares, M. A., Cottrell, R. S., Li, L., Kim, C. J. S., Stewart, T. P., Susilawati, C., Zhao, X. Y., & Helmstedt, K. J. (2025). Data-driven recommendations for enhancing real-time natural hazard warnings. One Earth, 8(5), Article 101274. https://doi.org/10.1016/j.oneear.2025.101274

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title = "Data-driven recommendations for enhancing real-time natural hazard warnings",

abstract = "The effectiveness of natural hazard warnings relies on transforming the available data into actionable knowledge for the public. Real-time warning communication and emergency response therefore need to be evaluated from a data-driven perspective. However, gaps exist between established data science best practices and their application in supporting natural hazard warnings. This perspective reviews existing data-driven approaches, highlighting limitations in hazard and impact warnings. Four main themes emerge for enhancing warning communication and supporting decision-making: (1) identifying data-barriers to effective warnings, (2) applying best-practice principles in visualizing warnings, (3) utilizing novel data for more localized forecasts and warnings, and (4) improving data-driven decision-making using uncertainty. These themes are illustrated using the extensive flooding in Australia in 2022 as a case study. This perspective reveals opportunities for improving the efficacy of natural hazard warnings using data science, and the collaborative potential between the data science and natural hazards communities.",

keywords = "natural hazards, natural disasters, data science, data analytics, data visualization, warnings",

author = "Kate Saunders and Owen Forbes and Hopf, \{Jess K.\} and Patterson, \{Charlotte R.\} and Vollert, \{Sarah A.\} and Kaitlyn Brown and Raiha Browning and Canizares, \{Miguel A.\} and Cottrell, \{Richard S.\} and Lanxi Li and Kim, \{Catherine J.S.\} and Stewart, \{Tace P.\} and Connie Susilawati and Zhao, \{Xiang Y.\} and Helmstedt, \{Kate J.\}",

year = "2025",

doi = "10.1016/j.oneear.2025.101274",

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AU - Forbes, Owen

AU - Hopf, Jess K.

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AU - Brown, Kaitlyn

AU - Browning, Raiha

AU - Canizares, Miguel A.

AU - Cottrell, Richard S.

AU - Li, Lanxi

AU - Kim, Catherine J.S.

AU - Stewart, Tace P.

AU - Susilawati, Connie

AU - Zhao, Xiang Y.

AU - Helmstedt, Kate J.

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AB - The effectiveness of natural hazard warnings relies on transforming the available data into actionable knowledge for the public. Real-time warning communication and emergency response therefore need to be evaluated from a data-driven perspective. However, gaps exist between established data science best practices and their application in supporting natural hazard warnings. This perspective reviews existing data-driven approaches, highlighting limitations in hazard and impact warnings. Four main themes emerge for enhancing warning communication and supporting decision-making: (1) identifying data-barriers to effective warnings, (2) applying best-practice principles in visualizing warnings, (3) utilizing novel data for more localized forecasts and warnings, and (4) improving data-driven decision-making using uncertainty. These themes are illustrated using the extensive flooding in Australia in 2022 as a case study. This perspective reveals opportunities for improving the efficacy of natural hazard warnings using data science, and the collaborative potential between the data science and natural hazards communities.

KW - natural hazards

KW - natural disasters

KW - data science

KW - data analytics

KW - data visualization

KW - warnings

U2 - 10.1016/j.oneear.2025.101274

DO - 10.1016/j.oneear.2025.101274

M3 - Article

SN - 2590-3330

VL - 8

JO - One Earth

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IS - 5

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ER -

Data-driven recommendations for enhancing real-time natural hazard warnings

Abstract

Keywords

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Projects

ARC Centre of Excellence for Climate Extremes

Cite this