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 Canizares, Richard S. Cottrell, Lanxi Li, Catherine J. S. Kim, Tace P. Stewart, Connie Susilawati, Xiang Zhao, Kate J. Helmstedt
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.