Preprints
https://doi.org/10.5194/egusphere-2023-968
https://doi.org/10.5194/egusphere-2023-968
22 May 2023
 | 22 May 2023

Disentangling the effect of geomorphological features and tall shrubs on snow depth variation in a sub-Arctic watershed using UAV derived products

Ian Shirley, Sebastian Uhlemann, John Peterson, Katrina Bennett, Susan S. Hubbard, and Baptiste Dafflon

Abstract. Spatial variation in snow depth is a main driver of heterogeneity in discontinuous permafrost landscapes, exerting a strong control on thermal and hydrological processes, vegetation dynamics, and carbon cycling. Topography and vegetation are understood to play an important role in driving variation in snow depth, but complex morphology often impedes efforts to disentangle these drivers. Maps of ground, vegetation and snow surface elevation were collected using an Unmanned Aerial Vehicle (UAV) over multiple years across a watershed on the Seward Peninsula in Alaska. Here, we quantify drivers of snow depth variation using the inferred maps of snow depth during peak snow accumulation in 2019 and 2022 and collocated ground surface elevation and vegetation height. A novel approach to extract microtopographic information from complex landscape morphologies is used to classify different features (e.g. drainage paths, risers and terraces, thermokarst patterned ground) and characterize their relationships with snow depth variation. A simple model developed using topographic information alone is shown to correlate strongly with local snow depth variation where vegetation height is low. We build a machine learning model to quantify snow trapping by shrub canopies in the watershed and show that snow trapping can be characterized by an exponential function of canopy height above snow (RMSE = 0.12 m, R2 = 0.5). Finally, we demonstrate that relationships between microtopography, vegetation height, and snow depth hold in years of deep and shallow snowpack. These results can be applied to improve representation of heterogeneity and vegetation-snow feedbacks in Earth System Models and to increase the spatial resolution of pan-arctic estimates of snow depth.

Ian Shirley, Sebastian Uhlemann, John Peterson, Katrina Bennett, Susan S. Hubbard, and Baptiste Dafflon

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-968', Anonymous Referee #1, 29 Jul 2023
    • AC1: 'Reply on RC1', Ian Shirley, 30 Sep 2023
  • CC1: 'Comment on egusphere-2023-968', Florent Dominé, 31 Jul 2023
    • AC3: 'Reply on CC1', Ian Shirley, 30 Sep 2023
  • RC2: 'Comment on egusphere-2023-968', Anonymous Referee #2, 01 Aug 2023
    • AC2: 'Reply on RC2', Ian Shirley, 30 Sep 2023
Ian Shirley, Sebastian Uhlemann, John Peterson, Katrina Bennett, Susan S. Hubbard, and Baptiste Dafflon
Ian Shirley, Sebastian Uhlemann, John Peterson, Katrina Bennett, Susan S. Hubbard, and Baptiste Dafflon

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Short summary
Snow depth has a strong impact on soil temperatures and carbon cycling in the arctic. Because of this, we want to understand why snow is deeper in some places than others. Using cameras mounted on a drone, we mapped snow depth, vegetation height, and elevation across a watershed in Alaska. In this paper, we develop novel techniques using image processing and machine learning to characterize the influence of topography and shrubs on snow depth in the watershed.