Statistical Modeling of Monthly Snow Depth Loss in Southern Canada
Publication: Journal of Hydrologic Engineering
Volume 24, Issue 3
Abstract
Quantifying the dynamics of snow depth is essential for understanding freshwater availability, mitigating flood and drought hazards, and monitoring the effects of climate change in cold regions. Here, a statistical approach for describing the dynamics of monthly snow depth loss (SDL) is developed and tested in 67 climate stations throughout southern Canada. The framework fuses an input selection scheme with multiple linear regression to approximate the SDL using a set of climate proxies, either explicitly or implicitly through modeling snow depth. Our findings suggest that statistical models—if properly developed and used—have the potential to form effective tools for describing the dynamics of SDL. In particular, the implicit statistical model, in which climate proxies are selected globally among all stations, provides an accurate model (expected ), which can outperform a frequently-used temperature-index model in a majority of stations. In addition, parameters of the statistical model can be regionalized efficiently (expected for the generalized model) using latitude, longitude, and altitude. This ability can provide a basis to extend the model application into ungauged sites.
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Acknowledgments
Financial support for this study is provided by Concordia University through Faculty of Engineering and Computer Science’s Student Research Support and startup fund, as well as the university’s strategic hire and Excellence Entrance Awards. Quebec’s Fond du Recherche Nature et Technologies (FRQNT) is also acknowledged for partial funding of the first author through the New Researcher Award, obtained by the corresponding author. We would like to thank the editorial board, the associate editor, and three anonymous reviewers for extremely constructive comments provided on the earlier version of this manuscript, which enormously enhanced the quality of this contribution as a whole.
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Journal of Hydrologic Engineering
Volume 24 • Issue 3 • March 2019
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©2018 American Society of Civil Engineers.
History
Received: Jan 9, 2018
Accepted: Oct 5, 2018
Published online: Dec 28, 2018
Published in print: Mar 1, 2019
Discussion open until: May 28, 2019
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