Abstract
In Eastern Canada, the snow survey network is highly optimized at the operational scale. However, it is commonly accepted that the network is limited when it comes to studying the spatial variability of the snow water equivalent (SWE), which forms different spatial structures that are active at multiple scales—from local to regional. The main objective of this study was to conduct a critical analysis of the existing snow survey network, based on the spatial variability of the existing SWE structures. To do so, we must (1) assess the snow survey network’s capacity to model spatial variability structures of SWE, and (2) study the spatial distribution based on the spatial variability structures of SWE. Initially, the snow survey network’s capacity to model the spatial variability structures of the SWE was evaluated by a variogram analysis. Second, the spatial distribution of the snow survey network’s data was analyzed through the Lorenz index curve and by measuring the spatial distribution using the Gini index. The results showed that, at a regional scale, the snow survey stations were evenly distributed within the spatial structures. However, at the local scale, the snow survey network was inadequate to model the spatial variability of SWE due to the reduced and uneven number of snow survey stations.
Highlights
The snow survey network provides in situ data of the snow cover’s physical parameters (density, depth, and snow water equivalent (SWE)). This snow survey network is optimized at the operational scale and provides real solutions to public safety issues At the economic level, it provides answers to practical problems related to hydroelectric reservoir management
To avoid this, opting for a particular approach, including the variogram analysis of each delimited structure, has the advantages of taking the spatial limits into account and reducing the modeling errors of the variability of SWE, allowing more effective modeling of the spatial variability of the SWE. This approach makes it possible to define a strategy for the maintenance of the most representative and informative distribution of SWE survey sites. Such a strategy can be defined by using the variogram analysis of the mean annual maximum of SWE, leading to the quantification of the spatial structure of regionalized variables [26,27]
A similar Gini coefficient value (0.27) was observed in unit 9, value (0.27) was observed in unit 9, where 35% of the stations provided less than 10% of the mean where 35% of the stations provided less than 10% of the mean annual maximum SWE data
Summary
The snow survey network provides in situ data of the snow cover’s physical parameters (density, depth, and snow water equivalent (SWE)). At the scale considered, the uneven spatial distribution of the snow survey stations is an obstacle to the quantitative analysis of spatial variability and the validation of spatial estimation of remote sensing algorithms. In such cases, the snow survey network’s ability to reproduce the spatial variability of SWE is reduced.
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