Supplementary files for "Impact of Snow Accumulation on Structural Integrity: Present and Future Perspectives"

Evaluating the impact of weight exerted by settled snow (i.e., snow load) on structures poses numerous statistical challenges, including missing data, biased distribution parameters, and the influence of climate change. This dissertation aims to address challenges related to the use both direct and indirect measurements of snow load (or equivalently, snow water equivalent), as well as the anticipated impact of climate change on future extreme snow loads. The first paper within this dissertation investigates short-term snow loads by comparing various techniques for estimating extreme values of short-term snow accumulations. Additionally, the first paper includes a comparative analysis of short-term and long-term snow accumulations, revealing significant differences in snow load accumulation patterns across geographical regions. The second paper focuses on bias correction in the scale parameter of the generalized extreme value distribution describing extreme snow loads in situations where the snow load is estimated indirectly using snow depth data. The bias correction is accomplished using bootstrap techniques when some of the snow load data is only approximated, rather than directly measured. We demonstrate the effectiveness of our approach in correcting scale parameter bias, as evidenced by simulation studies and real-life snow data. In the third paper, we incorporate the effects of climate change in the snow load estimation process and discuss the implications of considering the effects of climate change in snow load design. Our findings indicate that most locations in the United States have a reduced risk of snow-induced structural failure in a future climate. However, other locations appear to have an increased risk of structure failure, though there is no agreement among climate models as to which areas are at increased risk. Together, these interconnected papers refine methods for characterizing extreme snow accumulations and address the statistical complexities of estimating design snow loads for both current and future conditions.

评估雪荷载（snow load）对建筑结构施加的自重影响，面临诸多统计学挑战，包括数据缺失、分布参数存在偏倚以及气候变化的影响。本学位论文旨在解决与雪荷载（或等效的雪水当量（snow water equivalent））的直接、间接测量相关的各类挑战，以及气候变化对未来极端雪荷载的潜在影响问题。本论文的第一篇研究聚焦短期雪荷载，通过对比多种估算短期积雪极值的技术展开分析。此外，该研究还包含短期与长期积雪的对比分析，揭示了不同地理区域间雪荷载累积模式的显著差异。第二篇研究针对利用积雪深度数据间接估算雪荷载的场景，聚焦描述极端雪荷载的广义极值分布（generalized extreme value distribution）的尺度参数偏差校正问题。当部分雪荷载数据仅为近似值而非直接测量值时，本研究采用bootstrap（自助法）技术完成偏差校正。通过模拟研究与实际积雪数据验证，本研究证明了所提方法在校正尺度参数偏差方面的有效性。第三篇研究将气候变化的影响纳入雪荷载估算流程，并探讨了在雪荷载设计中考虑气候变化影响的现实意义。研究结果显示，未来气候情境下，美国多数地区由积雪引发的结构失效风险有所降低；但部分地区的结构失效风险却有所上升，不过当前气候模型尚未就哪些区域的风险会升高达成一致。综上，这三篇相互关联的研究完善了极端积雪特征的描述方法，并解决了当前及未来情境下设计雪荷载估算中的统计学复杂性问题。