The role of snow algae in snowmelt dynamics in mountain forests

Snow algae (SA) significantly influence snowmelt dynamics and biogeochemical cycles by reducing snow albedo and modulating concentration of some ions, thereby accelerating snow melt. Conversely, SA proliferate during snowmelt when sufficient liquid water is present in the snowpack, and adequate solar radiation fuels photosynthesis. This study investigates the diversity of SA with focus on forest species and their role in hydrological regimes within forested high mountain regions and examines their connection to climate change impacts.

We analysed a historical database of SA bloom occurrences (since 1976) in the Krkonoše Mountains (NE Czechia), correlating these events with meteorological conditions – such as daily temperature sums over 3 to 5 days, snow depth, and solar radiation – to identify the predictors of SA bloom onset and development. Our findings suggest that SA blooms require prolonged melting periods to develop, and ongoing climate change, characterized by shorter winters and earlier, more frequent melting periods, may significantly affect their occurrence.

To further explore the relationship between snowmelt timing and SA occurrence, we established a study plot in the Labský důl Valley in the Krkonoše Mountains. Beginning in early March 2024, earlier than in previous seasons due to warm and rainy weather, we conducted weekly to bi-weekly sampling. Analyses included snow chemistry (pH, conductivity, major ions, total phosphorus and nitrogen, dissolved organic carbon) and ITS2 rDNA metabarcoding combined with light microscopy to monitor seasonal development of SA taxonomic composition and life cycle stages.

The overall objectives of this project are to evaluate the relationships between SA blooms, snow cover dynamics, and microclimate data; correlate SA occurrence with microclimatic conditions across a broader geographical scale; and develop models to predict SA distribution below the timberline in Central Europe. Our findings will enhance the understanding of the interplay between SA and snowmelt, contributing to predictive models of SA distribution. This knowledge will inform conservation strategies and improve hydrological forecasting in mountainous environments.