Abstract
Mountain regions have experienced above-average warming in the 20th century and this trend is likely to continue. These accelerated temperature changes in alpine areas are causing reduced snowfall and changes in the timing of snowfall and melt. Snow is a critical component of alpine areas - it drives hibernation of animals, determines the length of the growing season for plants and the soil microbial composition. Thus, changes in snow patterns in mountain areas can have serious ecological consequences. Here we use 35 years of Landsat satellite images to study snow changes in the Mocho-Choshuenco Volcano in the Southern Andes of Chile. Landsat images have 30 m pixel resolution and a revisit period of 16 days. We calculated the total snow area in cloud-free Landsat scenes and the snow frequency per pixel, here called “snow persistence” for different periods and seasons. Permanent snow cover in summer was stable over a period of 30 years and decreased below 20 km2 from 2014 onward at middle elevations (1,530–2,000 m a.s.l.). This is confirmed by negative changes in snow persistence detected at the pixel level, concentrated in this altitudinal belt in summer and also in autumn. In winter and spring, negative changes in snow persistence are concentrated at lower elevations (1,200–1,530 m a.s.l.). Considering the snow persistence of the 1984–1990 period as a reference, the last period (2015–2019) is experiencing a −5.75 km2 reduction of permanent snow area (snow persistence > 95%) in summer, −8.75 km2 in autumn, −42.40 km2 in winter, and −18.23 km2 in spring. While permanent snow at the high elevational belt (>2,000 m a.s.l.) has not changed through the years, snow that used to be permanent in the middle elevational belt has become seasonal. In this study, we use a probabilistic snow persistence approach for identifying areas of snow reduction and potential changes in alpine vegetation. This approach permits a more efficient use of remote sensing data, increasing by three times the amount of usable scenes by including images with spatial gaps. Furthermore, we explore some ecological questions regarding alpine ecosystems that this method may help address in a global warming scenario.
Highlights
In addition to increasing average temperatures globally, climate change is disrupting and altering seasonal patterns that drive ecosystem function (IPCC, 2021)
Besides snow cover per date, traditionally studied from available cloud-free remote sensing data, here we propose to calculate a pixel based snow frequency measure per season, on called “snow persistence.”
We developed a function in R Core Team. (2020) to calculate snow persistence maps for the entire study area and at three different elevational belts (1,200–1,530, 1,530–2,000, and >2,000 m a.s.l.) using the capabilities of the “raster” package (Hijmans, 2019) to handle large Landsat rasterstacks and R capabilities to take advantage of multi-core processing
Summary
In addition to increasing average temperatures globally, climate change is disrupting and altering seasonal patterns that drive ecosystem function (IPCC, 2021). Mountain regions have been identified as having experienced above-average warming in the 20th century, a trend likely to continue (Beniston et al, 1997; Liu and Chen, 2000). These accelerated changes in alpine areas means that ecosystems are being impacted by both earlier snowmelt and reductions in snowfall. A recent global assessment of mountain regions reported that around 78% of the world’s mountain areas have experienced a snow cover decline between the 2000 and 2018 study period (Notarnicola, 2020)
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