Document Type
Article
Journal/Book Title/Conference
Glacies
Author ORCID Identifier
Kenneth Pomeyie https://orcid.org/0000-0002-3899-5643
Brennan Bean https://orcid.org/0000-0002-2853-0455
Volume
1
Issue
1
Publisher
MDPI AG
Publication Date
8-7-2024
Journal Article Version
Version of Record
First Page
35
Last Page
56
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
Accurately quantifying the threat of collapse due to the weight of settled snow on the roof of a structure is crucial for ensuring structural safety. This quantification relies upon direct measurements of the snow water equivalent (SWE) of settled snow, though most weather stations in the United States only measure snow depth. The absence of direct load measurements necessitates the use of modeled estimates of SWE, which often results in the underestimation of the scale/variance parameter of the distribution of annual maximum SWE. This paper introduces a novel bias correction method that employs a bootstrap technique with regression-based models to calibrate the variance parameter of the distribution. The efficacy of this approach is demonstrated on real and simulated datasets. The findings reveal varied levels of success, with the efficacy of the proposed approach being inherently dependent on the quality of the selected regression-based model. These findings demonstrate that integrating our approach with a suitable regression-based model can produce unbiased or nearly unbiased annual maximum SWE distribution parameters in the absence of direct SWE measurements.
Recommended Citation
Pomeyie, K.; Bean, B. Bootstrap Methods for Bias-Correcting Probability Distribution Parameters Characterizing Extreme Snow Accumulations. Glacies 2024, 1, 35-56. https://doi.org/10.3390/glacies1010004