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Climate drivers of large magnitude snow avalanche years in the U.S. northern Rocky Mountains.

ABSTRACT: Large magnitude snow avalanches pose a hazard to humans and infrastructure worldwide. Analyzing the spatiotemporal behavior of avalanches and the contributory climate factors is important for understanding historical variability in climate-avalanche relationships as well as improving avalanche forecasting. We used established dendrochronological methods to develop a long-term (1867-2019) regional avalanche chronology for the Rocky Mountains of northwest Montana using tree-rings from 647 trees exhibiting 2134 avalanche-related growth disturbances. We then used principal component analysis and a generalized linear autoregressive moving average model to examine avalanche-climate relationships. Historically, large magnitude regional avalanche years were characterized by stormy winters with positive snowpack anomalies, with avalanche years over recent decades increasingly influenced by warmer temperatures and a shallow snowpack. The amount of snowpack across the region, represented by the first principal component, is shown to be directly related to avalanche probability. Coincident with warming and regional snowpack reductions, a decline of ~ 14% (~ 2% per decade) in overall large magnitude avalanche probability is apparent through the period 1950-2017. As continued climate warming drives further regional snowpack reductions in the study region our results suggest a decreased probability of regional large magnitude avalanche frequency associated with winters characterized by large snowpacks and a potential increase in large magnitude events driven by warming temperatures and spring precipitation.

SUBMITTER: Peitzsch EH

PROVIDER: S-EPMC8113570 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under "space-for-time substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996-2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales.

Dataset Information

Climate drivers of large magnitude snow avalanche years in the U.S. northern Rocky Mountains.

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