Reply on RC1

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, operational forecasting, and as input for hydrological models. Recent advances in unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions up to a few centimeters. Here, we study the spatiotemporal variability of snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability of snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS-SfM surveys in the winter of 2018/2019 and a snow-free bare ground survey after snowmelt. Due to poor sub-canopy penetration with the UAS-SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in-situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS-SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to -16 cm in coniferous forests and -32 cm in peatland during the melt period. This highlights the poor representation of point measurements even on the sub-catchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth variability (5&ndash;95 percentiles) within different land cover types increased from 17 cm to 42 cm in peatlands and from 33 cm to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its variability were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high spatial resolution data, we found a systematic increase (2&ndash;20 cm), then a decline of snow depth near the canopy with increasing distance (from 1 m to 2.5 m) of the peak value through the snow season. This study highlights the potential of the UAS-SfM in high-resolution monitoring of snow depth in multiple land cover types and snow-vegetation interactions in subarctic and remote areas where field data is not available.