Preprints
https://doi.org/10.5194/tc-2022-242
https://doi.org/10.5194/tc-2022-242
 
02 Jan 2023
02 Jan 2023
Status: this preprint is currently under review for the journal TC.

Measuring the spatiotemporal variability of snow depth in subarctic environments using unmanned aircraft systems (UAS) – Part 2: Snow processes and snow-canopy interactions

Leo-Juhani Meriö1, Anssi Rauhala2, Pertti Ala-aho1, Anton Kuzmin3, Pasi Korpelainen3, Timo Kumpula3, Bjørn Kløve1, and Hannu Marttila1 Leo-Juhani Meriö et al.
  • 1Water, Energy and Environmental Engineering, Faculty of Technology, University of Oulu, 90014, Finland
  • 2Civil Engineering, Faculty of Technology, University of Oulu, Finland
  • 3Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, Finland

Abstract. 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 (5–95 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 (2–20 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.

Leo-Juhani Meriö et al.

Status: open (until 27 Feb 2023)

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Leo-Juhani Meriö et al.

Data sets

Unmanned aircraft system (UAS) snow depth mapping at the Pallas Atmosphere-Ecosystem Supersite Rauhala, A., Meriö, L. J., Korpelainen, P. and Kuzmin, A. https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836

Leo-Juhani Meriö et al.

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Short summary
Information on seasonal snow cover is essential to the understanding of snow processes and operational forecasting. We study the spatiotemporal variability of snow depth and snow processes in subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the potential of the drones to be used for a detailed study of snow depth in multiple land cover types and snow-vegetation interactions.