High‐resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut-Reference-Cited by-同舟云学术

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

Published:2022-03 Issue:3 Volume:36 Page:
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrological Processes

Author:

Meloche Julien¹²^ORCID,Langlois Alexandre¹²,Rutter Nick³,McLennan Donald⁴,Royer Alain¹²,Billecocq Paul¹²,Ponomarenko Serguei⁵

Affiliation:

1. Département de Géomatique Appliquée Université de Sherbrooke Sherbrooke Canada

2. Centre D'études Nordiques Quebec Canada

3. Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne UK

4. Arctic Research Foundation Ontario Canada

5. Polar Knowledge Canada Canadian High Arctic Research Station Cambridge Bay Canada

Funder

Polar Knowledge Canada

Natural Sciences and Engineering Research Council of Canada

Environment and Climate Change Canada

Publisher

Wiley

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14546

Reference56 articles.

1. Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

2. A physical SNOWPACK model for the Swiss avalanche warning

3. Effect of snow cover on the vulnerability of lemmings to mammalian predators in the Canadian Arctic

4. Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts

5. Analysis of snow-vegetation interactions in the low Arctic-Subarctic transition zone (northeastern Canada)

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4. Snow mechanical property variability at the slope scale – implication for snow mechanical modelling;The Cryosphere;2024-03-26

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