A low-cost method for monitoring snow characteristics at remote field sites

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.