High‐resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut

Meloche, Julien, Langlois, Alexandre, Rutter, Nick, McLennan, Donald, Royer, Alain, Billecocq, Paul and Ponomarenko, Serguei (2022) High‐resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut. Hydrological Processes, 36 (3). e14546. ISSN 0885-6087

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Official URL: https://doi.org/10.1002/hyp.14546

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 at resolutions (< 100 m) that influence ecological habitats and permafrost thaw. A machine learning method using topographic parameters with the Random Forest (RF) algorithm previously developed in alpine environments was applied 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. Addition of an ecotype parameter (proxy for vegetation height) showed minimal predictive improvement. Using RF, snow depth distributions were predicted from topographic parameters with a root mean square error = 8 cm (23%) (R^2 = 0.79) at 10 m resolution for an arctic watershed (1 500 km2) in western Nunavut, Canada.

Item Type: Article
Additional Information: Funding information: This research was made possible thanks to the financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC), Polar Knowledge Canada, the Canadian Foundation for Innovation (CFI), Environment and Climate Change Canada (ECCC), Fonds de recherche du Québec–Nature et technologies (FRQNT), Northern Scientific Training Program (NSTP) and research funding from Northumbria University, UK.
Uncontrolled Keywords: Arctic snow, Random Forest, Snow depth
Subjects: F800 Physical and Terrestrial Geographical and Environmental Sciences
Department: Faculties > Engineering and Environment > Geography and Environmental Sciences
Depositing User: John Coen
Date Deposited: 08 Apr 2022 09:36
Last Modified: 20 Apr 2022 11:49
URI: http://nrl.northumbria.ac.uk/id/eprint/48846

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