A user perspective on the avalanche danger scale – insights from North America

Author:

Morgan Abby,Haegeli PascalORCID,Finn Henry,Mair Patrick

Abstract

Abstract. Danger ratings are used across many fields to convey the severity of a hazard. In snow avalanche risk management, danger ratings play a prominent role in public bulletins by concisely describing existing and expected conditions. While there is considerable research examining the accuracy and consistency of the production of avalanche danger ratings, far less research has focused on how backcountry recreationists interpret and apply the scale. We used 3195 responses to an online survey to provide insight into how recreationists perceive the North American Public Avalanche Danger Scale and how they use ratings to make trip planning decisions. Using a latent class mixed-effect model, our analysis shows that 65 % of our study participants perceive the avalanche danger scale to be linear, which is different from the scientific understanding of the scale, which indicates an exponential-like increase in severity between levels. Regardless of perception, most respondents report avoiding the backcountry at the two highest ratings. Using conditional inference trees, we show that participants who recreate fewer days per year and those who have lower levels of avalanche safety training tend to rely more heavily on the danger rating to make trip planning decisions. These results provide avalanche warning services with a better understanding of how recreationists interact with danger ratings and highlight how critical the ratings are for individuals who recreate less often and who have lower levels of training. We discuss opportunities for avalanche warning services to optimize the danger scale to meet the needs of these users who depend on the ratings the most.

Funder

Mitacs

Publisher

Copernicus GmbH

Subject

General Earth and Planetary Sciences

Reference88 articles.

1. Aitsi-Selmi, A., Blanchard, K., and Murray, V.: Ensuring science is useful, usable and used in global disaster risk reduction and sustainable development: a view through the Sendai framework lens, Palgrave Communications, 2, 1–9, https://doi.org/10.1057/palcomms.2016.16, 2016.

2. Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974.

3. Avalanche Canada: Forecast Archive, https://avalanche.ca/forecasts/archives, last access: 21 December 2021.

4. Avalanche Canada: Danger Ratings, https://avysavvy.avalanche.ca/danger-ratings, last access: 23 July 2022.

5. Bloom, B. S.: Taxonomy of educational objectives, McKay, New York, Vol. 1: Cognitive domain, 2–24, 1956.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3