The influence of exposure and vulnerability on designing impact-based warning thresholds: Results of a New Zealand risk modelling experiment

Impact-based warnings are changing the way warnings are designed and communicated. Impact-based thresholds have been proposed to require the inclusion of exposure and vulnerability data for a more ‘risk-based’ approach. However, the influence of this inclusion on assigning warning levels has not been tested, and recent research has identified a concern that exposure may exacerbate the ‘urban-rural bias’. This means that rural areas may be under-warned due to having fewer people exposed, and therefore a ‘lower risk’ compared to an urban area with more people exposed to the same hazard. Thus, how warning levels change based on exposure levels needs to be explored.

Our research sought to investigate the difference in the number of people who would receive the highest level ‘red’ weather warning when it was triggered by:

• the hazard (wind speed thresholds),
• exposure to the hazard (buildings and people exposed to categories of wind severity), and
• the overall risk (building damage caused by the wind), in both relative and absolute terms.

To do this, we used a high-resolution meteorological model of ex-tropical Cyclone Cook that was a real event and was hypothetically shifted onto Auckland city in New Zealand. Within the RiskScape risk modelling software we were able to calculate the expected impacts to buildings in this scenario. We then experimented with various warning thresholds based on hazard, exposure, and risk. We will describe the methods used in this research and give an overview of the results, which show that there are differences in the number of people warned when different methods are applied

This research demonstrates how incorporating exposure of buildings and people and risk in the design of a warning system influences the number of people who would receive warnings, in comparison to a traditional hazard-based warning. Our findings will help to inform decisions around the urban-rural bias and reinforces the need to consider who the key audiences are for impact-based warnings. This research has implications on designing impact-based warning systems globally.