Critical Analysis of the Snow Survey Network According to the Spatial Variability of Snow Water Equivalent (SWE) on Eastern Mainland Canada

Sena, Yawu Noumonvi; Chokmani, Karem; Gloaguen, Erwan; Bernier, Monique

doi:10.3390/hydrology6020055

Cited by 1 publication

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“…For example, in quantitative analysis spatial variability and the validation of spatial estimations made using remote sensing algorithms, major limit or obstacle is the uneven spatial distribution of the snow survey stations. Under this condition, the snow survey network's ability to reproduce the spatial variability of SWE is reduced (Sena et al, 2019). Because the number of snow sites is inadequate, some studies integrate data from other structures in the estimation process of their watershed study, regardless of the limitations and scale.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale spatialization of snow water equivalent (SWE) according to their spatial structures in eastern Canada

Sena

Chokmani

Gloaguen

et al. 2020

Preprint

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Abstract. The spatial variability of snow plays a key role in snow water storage, spring runoff and hydraulic dam management. The snow survey network unequally distributed ability, to monitoring the spatial variability of the snow cover is limited. The spatial variability of the snow cover is explained by physiographic factors, which generate spatial structures at different scales. The variability of the snow cover is explained by physiographic factors, which generate structures at different scales. These structures of spatial variability of the snow cover were delimited by a functional approach at the local (300 × 300 m) and regional (10 × 10 km) scales on eastern Canada. The territory was segmented into regions, (called spatial structures,) with homogeneous average maximum annual snow water equivalent (SWE). The aim of this paper is to spatialize the average maximum annual snow water equivalent (SWE) according to spatial variability structures at both scales. Initially, at the regional scale, the average maximum annual SWE is estimated using the stepwise regression approach. Secondly, the SWE residuals are estimated using a regression approach on local physiographic meta-variables. The estimated SWE allows quantifying the spatial variability of the average maximum annual SWE for regional and local physiographic factors. Indeed, at the regional scale, the physiographic regional factors explain 68 % of the variance of the spatial variability of the average maximum annual SWE. At the local scale, physiographic factors improve the estimate of the average annual maximum SWE by 21 % (R = 89 %) for an unexplained share of 10 % of the variance. Local physiographic factors reorganize the regional residuals of average maximum annual SWE and contribute to the local variability. This study shows the role of altitude in snow accumulation at the regional scale, where the presence of high mountains increases the amount of rainfall from wet winds. In each geographical area, the highest values of the SWE are related to high mountain peaks. The impact is confirmed at the foothills of the Canadian Shield mountains. At the local scale, the regional residual value was reorganized based on local physiographic factors (slope, forms of catchment, distance to rivers, etc.); this adjustment led to high SWE values in the concave landscape and the ubacs away from sunlight. The SWE accumulation area corresponds to the depressions and concave sections at foothills.

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Section: Introductionmentioning

confidence: 99%