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Snow depth distribution in canopy gaps over heterogeneous forest structure in the Central Pyrenees
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  • F. Rojas-Heredia,
  • Jesus Revuelto,
  • Deschamps-Berger C.,
  • Esteban Alonso-González,
  • P. Domínguez-Aguilar,
  • J. García,
  • F. Pérez-Cabello,
  • Ignacio Lopez-Moreno
F. Rojas-Heredia
Instituto Pirenaico de Ecologia

Corresponding Author:[email protected]

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Jesus Revuelto
Instituto Pirenaico de Ecologia
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Deschamps-Berger C.
Instituto Pirenaico de Ecologia
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Esteban Alonso-González
Instituto Pirenaico de Ecologia
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P. Domínguez-Aguilar
Instituto Pirenaico de Ecologia
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J. García
University of Valencia
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F. Pérez-Cabello
University of Zaragoza
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Ignacio Lopez-Moreno
Instituto Pirenaico de Ecologia
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Abstract

This research analyses the snow depth distribution in canopy gaps across two plots in Central Pyrenees, to improve understanding of snow–forest and topography interactions. Snow depth maps, forest structure–canopy gap (FSCG) characteristics and topographic variables were generated by applying Structure from Motion algorithms (SfM) to images acquired from Unmanned Aerial Vehicles (UAVs). Six flights were conducted under different snowpack conditions in 2021, 2022 and 2023. Firstly, the snow depth database was analyzed in terms of the ratio between the radius of the canopy gap and the maximum height of the surrounding trees ( r/ h), in order to classify the gaps as small-size, medium-size, large-size or open areas at both sites independently. Then the Kendall correlation coefficients between the snow depth, FSCG and topographic variables were computed, and a Random Forest (RF) model for each survey day was implemented, to determine the influence of these variables for explaining snow depth patterns. The results demonstrate the high reliability of the UAV SfM photogrammetry approach for measuring snowpack dynamics at fine scale in canopy gaps and open areas. At site 1, the larger the r/ h observed, the greater was the snow depth obtained. This pattern was not evident at site 2, which presented high variability related to the survey dates and categories, highlighting the relevance of topography for determining optimum snow accumulation in forested areas. Slope systematically exhibited a negative and significant correlation with snow depth, and was consistently the highest-ranked variable for explaining snow distribution at both sites according to the RF models. Distance to the Canopy Edge also presented high influence, especially at site 1. The findings suggest differences in the main drivers throughout each site and survey of the topographic and FSCG variables are needed to understand snow depth distribution over heterogeneous mountain forest domains.
04 Mar 2024Submitted to Hydrological Processes
04 Mar 2024Submission Checks Completed
04 Mar 2024Assigned to Editor
04 Mar 2024Reviewer(s) Assigned
25 Jun 2024Review(s) Completed, Editorial Evaluation Pending
06 Aug 20241st Revision Received
09 Aug 2024Submission Checks Completed
09 Aug 2024Assigned to Editor
09 Aug 2024Reviewer(s) Assigned
15 Aug 2024Reviewer(s) Assigned
15 Oct 2024Review(s) Completed, Editorial Evaluation Pending
18 Oct 2024Editorial Decision: Accept