Abstract
Abstract. The lack of spatially distributed snow depth measurements in natural environments is a challenge worldwide. These data gaps are of particular relevance in northern regions such as coastal Labrador where changes to snow conditions directly impact Indigenous livelihoods, local vegetation, permafrost distribution and wildlife habitat. This problem is exacerbated by the lack of cost-efficient and reliable snow observation methods available to researchers studying cryosphere–vegetation interactions in remote regions. We propose a new method termed snow characterization with light and temperature (SCLT) for estimating snow depth using vertically arranged multivariate (light and temperature) data loggers. To test this new approach, six snow stakes outfitted with SCLT loggers were installed in forested and tundra ecotypes in Arctic and subarctic Labrador. The results from 1 year of field measurement indicate that daily maximum light intensity (lux) at snow-covered sensors is diminished by more than an order of magnitude compared to uncovered sensors. This contrast enables differentiation between snow coverage at different sensor heights and allows for robust determination of daily snow heights throughout the year. Further validation of SCLT and the inclusion of temperature determinants is needed to resolve ambiguities with thresholds for snow detection and to elucidate the impacts of snow density on retrieved light and temperature profiles. However, the results presented in this study suggest that the proposed technique represents a significant improvement over prior methods for snow depth characterization at remote field sites in terms of practicality, simplicity and versatility.
Highlights
Snow cover and snow depth are among the Global Climate Observing System’s (GCOS) essential climate variables (Bojinski et al, 2014) and are critical components of global and regional energy balances (Olsen et al, 2011; Pulliainen et al, 2020)
We present results from a novel low-cost technique for snow depth estimation that can be efficiently applied at remote field sites
The results in this study have provided a direct workflow for estimating snow depth from snow characterization with light and temperature (SCLT) data, though the proposed method will require further optimization and refinement
Summary
Snow cover and snow depth are among the Global Climate Observing System’s (GCOS) essential climate variables (Bojinski et al, 2014) and are critical components of global and regional energy balances (Olsen et al, 2011; Pulliainen et al, 2020). The global snow albedo effect influences all humans, but consequences of changing snow conditions for those living in cold climate and alpine regions are especially pronounced (Ford et al, 2019; Lemke et al, 2007). Accurate characterization of snow depth is important for hydroelectric operations, freshwater and land resource availability to communities, and prediction of climate change impacts (Hovelsrud et al, 2011; Mortimer et al, 2020; Sturm et al, 2005; Thackeray et al, 2019; Wolf et al, 2013). Changes to snow depth and snow cover duration in Arctic and alpine tundra caused by enhanced shrub and tree growth can result in warmer ground temperatures, permafrost thaw and further vegetation expansion (Callaghan et al, 2011; Wilcox et al, 2019). As such, standardized measurement of snow remains a challenge in remote regions where existing stations cannot represent the diversity of snow conditions across topography, vegetation and snow wind scouring (Brown et al, 2012, 2003; Derksen et al, 2014)
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