Citation

Cass E, Shao W, Hao F, Moradkhani H, Yeates E. Int. J. Disaster Risk Reduct. 2023; 93: e103752.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.ijdrr.2023.103752

PMID

unavailable

Abstract

Coastal regions such as the U.S. Atlantic and Gulf Coasts are highly vulnerable to extreme coastal hazards such as tropical cyclones and major hurricanes. The effects of these hazards pose a threat now and are expected to increase in the future, which highlights the need for coastal communities to receive and understand information regarding risks involved with these hazards. Through this study, we identify points of improvement in the tools used to communicate the short and long-term risk associated with hurricane hazards through three surveys in Mobile, AL, Savannah, GA, and Houston TX. These surveys identify public response to hurricane descriptions, Cone of Uncertainty graphics, and long-term trend graphics. Analysis of trends in responses to these communication tools identifies relationships between risk perceptions and existing factors in each study location. Further, public response to these tools is identified and analyzed using structural equation models for each location with a "response" latent variable containing information from endogenous variables in the survey. Response was measured as action intent, concern for the scenario, reported evacuation likelihood, and interpretation of long-term trends. We identify points of improvement for all three communication tools to aid in public comprehension of the information provided as well as to increase response to hurricane hazards by more effectively communicating risk information. These would help to improve comprehension and increase different responses to tropical storm and hurricane damage from high winds and storm surge with the intent to improve resident response to hazards along the U.S. Atlantic and Gulf Coasts.

Language: en

Keywords

Climate change; Hazard mitigation; Hurricane communication; Hurricane graphics; Structural equation modeling