Snow avalanches affect transportation corridors and settlements worldwide. In many mountainous regions, robust records of avalanche frequency and magnitude are sparse or non-existent. However, dendrochronological methods can be used to fill this gap and infer historical avalanche patterns. In this study, we developed a tree-ring-based avalanche chronology for large magnitude avalanche events (size $\ge \sim D\mathrm{3}$) using dendrochronological techniques for a portion of the US northern Rocky Mountains. We used a strategic sampling design to examine avalanche activity through time and across nested spatial scales (i.e., from individual paths, four distinct subregions, and the region). We analyzed 673 samples in total from 647 suitable trees collected from 12 avalanche paths from which 2134 growth disturbances were identified over the years 1636 to 2017 CE. Using existing indexing approaches, we developed a regional avalanche activity index to discriminate avalanche events from noise in the tree-ring record. Large magnitude avalanches, common across the region, occurred in 30 individual years and exhibited a median return interval of approximately 3 years (mean = 5.21 years). The median large magnitude avalanche return interval (3–8 years) and the total number of avalanche years (12–18) varies throughout the four subregions, suggesting the important influence of local terrain and weather factors. We tested subsampling routines for regional representation, finding that sampling 8 random paths out of a total of 12 avalanche paths in the region captures up to 83 % of the regional chronology, whereas four paths capture only 43 % to 73 %. The greatest value probability of detection for any given path in our dataset is 40 %, suggesting that sampling a single path would capture no more than 40 % of the regional avalanche activity. Results emphasize the importance of sample size, scale, and spatial extent when attempting to derive a regional large magnitude avalanche event chronology from tree-ring records.

中文翻译：

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利用树环对大雪崩进行时空分析

雪崩影响了全世界的运输走廊和居民点。在许多山区，雪崩频率和强度的可靠记录稀少或不存在。但是，可以使用树轮年代学方法填补这一空白，并推断历史雪崩模式。在这项研究中，我们针对大雪崩事件（大小$\ge 〜d\mathrm{3}$）在美国北落基山脉的部分地区使用树轮年代学技术。我们使用了战略性抽样设计，研究了穿越时间和跨嵌套空间尺度（即，从单个路径，四个不同的子区域以及该区域）的雪崩活动。我们分析了从12个雪崩路径中收集的647棵合适树木中的673个样本，在1636年至2017年期间，从中识别出2134个生长干扰。使用现有的索引方法，我们开发了区域雪崩活动指数，以将雪崩事件与树年轮记录中的噪声区分开。该地区常见的大规模雪崩发生在30年中，并且平均回报间隔约为3年（平均值 = 5.21年）。在四个次区域中，中值大雪崩返回间隔（3–8年）和雪崩年总数（12–18）各不相同，这表明了当地地形和天气因素的重要影响。我们测试了子采样例程的区域代表性，发现在该区域的总共12条雪崩路径中采样8条随机路径最多可捕获83％的区域年表，而四个路径仅捕获43％至73％。在我们的数据集中，任何给定路径的最大检测概率为40％，这表明对单个路径进行采样将捕获不超过40％的区域雪崩活动。当试图从树年轮记录中推导区域性大雪崩事件年表时，结果强调了样本大小，规模和空间范围的重要性。