Targeting high robustness in snowpack modeling for Nordic hydrological applications in limited data conditions

Mas, Alexandre, Baraer, Michel, Arsenault, Richard, Poulin, Annie et Prefontaine, Jonathan. 2018. « Targeting high robustness in snowpack modeling for Nordic hydrological applications in limited data conditions ». Journal of Hydrology, vol. 564. pp. 1008-1021.
Compte des citations dans Scopus : 4.

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Abstract

Most hydrological models simulate snowmelt using a degree day or simplified energy balance method, which usually requires a calibration of snow-related parameters using discharge data. Despite its apparent efficiency, this method leads to empirical relations which are not proven to remain valid in a changing climate. The direct application of robust physically-based snow models in hydrological modeling is difficult due to the high number of not easily available input variables this model type requires. The objective of this study is to test the robustness of a physically-based snowpack model that requires only a limited number of common meteorological parameters. The MASiN model computes the energy and mass balance of multiple layers of the snowpack using hourly air temperature, relative humidity and wind speeds, as well as daily precipitations. MASiN was tested at 23 sites across Canada and Sweden, using a unique set of parameters fixed at a single site. At each site, the snow depth simulated by MASiN was compared against measurements. Robustness was challenged by comparing MASiN’s performance to that of three other models on three different criteria. MASiN showed the highest robustness among the tested models. With a unique set of parameters, it showed better results than the three reference models when used in similar conditions and matched their performances when reference models were calibrated at each site. The results prove non-data intensive physically based models to be promising tools for hydrological and other snow cover-related studies.

Item Type:	Peer reviewed article published in a journal
Professor:	Professor Baraër, Michel Arsenault, Richard Poulin, Annie
Affiliation:	Génie de la construction, Génie de la construction, Génie de la construction
Date Deposited:	21 Aug 2018 15:53
Last Modified:	03 Dec 2021 18:56
URI:	https://espace2.etsmtl.ca/id/eprint/17277

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