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High-resolution snow depth prediction using Random Forest algorithm with topographic parameters and an ecosystem map: a case study in the Greiner Watershed, Nunavut
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  • Julien Meloche,
  • Alexandre Langlois,
  • Nick Rutter,
  • Don McLennan,
  • Alain Royer,
  • Paul Billecocq,
  • Serguei Ponomarenko
Julien Meloche
Université de Sherbrooke
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Alexandre Langlois
Université de Sherbrooke
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Nick Rutter
Northumbria University
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Don McLennan
Arctic Research Fondation
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Alain Royer
Universite de Sherbrooke
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Paul Billecocq
Université de Sherbrooke
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Serguei Ponomarenko
Polar Knowledge Canada
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Abstract

Increased surface temperatures (0.7℃ per decade) in the Arctic affects polar ecosystems by reducing the extent and duration of annual snow cover. Monitoring of these important ecosystems needs detailed information on snow cover properties (depth and density) at resolutions (< 100 m) that influence ecological habitats and permafrost thaw. As arctic snow is strongly influenced by vegetation, an ecotype map at 10 m resolution was added to a method with the Random Forest (RF) algorithm previously developed for alpine environments and applied here over an arctic landscape for the first time. The topographic parameters used in the RF algorithm were Topographic Position Index (TPI) and up-wind slope index (Sx), which were estimated from the freely available Arctic DEM at 2 m resolution. Ecotypes with taller vegetation with moister soils were found to have deeper snow because of the trapping effect. Using feature importance with RF, snow depth distributions were predicted from topographic and ecosystem parameters with a root mean square error = 8 cm (23%) (R² = 0.79) at 10 m resolution for an arctic watershed (1 500 km²) in western Nunavut, Canada.

Peer review status:UNDER REVIEW

24 Nov 2021Submitted to Hydrological Processes
24 Nov 2021Assigned to Editor
24 Nov 2021Submission Checks Completed
24 Nov 2021Reviewer(s) Assigned