  The effectiveness and adequacy of natural hazard warnings hinges on the
availability of data and its transformation into actionable knowledge for the
public. Real-time warning communication and emergency response therefore need
to be evaluated from a data science perspective. However, there are currently
gaps between established data science best practices and their application in
supporting natural hazard warnings. This Perspective reviews existing
data-driven approaches that underpin real-time warning communication and
emergency response, highlighting limitations in hazard and impact forecasts.
Four main themes for enhancing warnings are emphasised: (i) applying
best-practice principles in visualising hazard forecasts, (ii) data
opportunities for more effective impact forecasts, (iii) utilising data for
more localised forecasts, and (iv) improving data-driven decision-making using
uncertainty. Motivating examples are provided from the extensive flooding
experienced in Australia in 2022. This Perspective shows the capacity for
improving the efficacy of natural hazard warnings using data science, and the
collaborative potential between the data science and natural hazards
communities.
