10B.1 Effectiveness of Impact-Based Warnings: Publics' Perceptions, Beliefs, and Intended Actions

Thursday, 26 January 2017: 8:30 AM
613 (Washington State Convention Center )
Sally Potter, GNS Science, Lower Hutt, New Zealand; and P. Kreft, P. Milojev, C. Noble, B. E. Montz, and S. Gauden-Ing

We conducted an online survey with members of the New Zealand public (n=1364) in late 2015 to investigate the differences in perceptions, beliefs, and intended preventative actions of recipients of impact-based and phenomenon-based weather warnings. The results indicate that while impact-based warnings may be more effective than phenomenon-based warnings in influencing the recipients’ perceptions and beliefs about a weather-related hazard, there was no significant difference in many of their intended preventative actions. Phenomenon-based severe weather warnings (which are warnings based on wind speed, for example, regardless of the potential effects of the event) have traditionally been used by National Meteorological and Hydrological Services (NMHSs). Impact-based severe weather warnings (that is, warnings based on the potential impacts of the weather event) are becoming increasingly used to reduce disaster risk. Meteorological Service of New Zealand Ltd (New Zealand’s NMHS) invited the participation of a social scientist and Civil Defence and Emergency Management agencies to investigate the effectiveness of various types of weather warnings for stakeholders and the public. In 2015, we held workshops with stakeholders and conducted a public survey. Data were statistically tested for differences between the two types of warnings for various factors, including the participants’ understanding of the possible effects of a hypothetical strong wind event, levels of threat and concern about the event, credibility of the message, stated intended preventative actions, and demographic variables.  The results of this research are informing the review of severe weather warnings issued by MetService. Details from the preliminary analysis of the data will be presented.
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