Data-driven recommendations for enhancing real-time natural hazard warnings, communication, and response

TL;DR

This paper reviews how data science can improve real-time natural hazard warnings by enhancing visualization, impact forecasting, localization, and decision-making under uncertainty, with examples from Australian floods.

Contribution

It identifies key data-driven strategies to improve hazard warning systems and highlights gaps between data science practices and their application in natural hazard management.

Findings

Enhanced visualization techniques improve hazard communication.

Data opportunities enable more accurate impact forecasts.

Localised data use improves warning relevance.

Abstract

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.