Investigating snowpack across scale in the northern Great Lakes–St. Lawrence forest region of Central Ontario, Canada

• Published in: Hydrological processes Vol. 33; no. 26; pp. 3310 - 3329
• Main Authors: Beaton, Andy D., Metcalfe, Robert A., Buttle, James M., Franklin, Steven E.
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 30.12.2019
• Subjects: Autocorrelation; Bias; Canadian Meteorological Centre (CMC); Coefficients; Daily precipitation; Data; Depth; Estimates; Forests; GlobSnow; Great Lakes–St. Lawrence forest; In situ measurement; Lakes; Land cover; Land use; Plant cover; Remote regions; scale; Scaling; Seasonal variations; Seasonality; Snow; Snow accumulation; Snow depth; snow processes; snow water equivalent (SWE); Snow-water equivalent; Snowpack; time‐lapse; Water depth; Canada
• ISSN: 0885-6087; 1099-1085
• DOI: 10.1002/hyp.13558

Scaling issues in snow hydrology persist due to limitations in instrumentation and inability to measure physical properties and processes at spatiotemporal scales required for analysis. Snow depth and water equivalent (SWE) across scale estimated using time‐lapse photos, transects, and model grids (Canadian Meteorological Centre depth, GlobSnow SWE) were found to represent different physical processes and have substantially different statistical moments. Findings have implications for understanding limitations of distributing snowpack measurements, data assimilation, and validation of remotely sensed estimates. This study investigates scaling issues by evaluating snow processes and quantifying bias in snowpack properties across scale in a northern Great Lakes–St. Lawrence forest. Snow depth and density were measured along transects stratified by land cover over the 2015/2016 and 2016/2017 winters. Daily snow depth was measured using a time‐lapse (TL) camera at each transect. Semivariogram analysis of the transect data was conducted, and no autocorrelation was found, indicating little spatial structure along the transects. Pairwise differences in snow depth and snow water equivalent (SWE) between land covers were calculated and compared across scales. Differences in snowpack between forested sites at the TL points corresponded to differences in canopy cover, but this relationship was not evident at the transect scale, indicating a difference in observed process across scale. TL and transect estimates had substantial bias, but consistency in error was observed, which indicates that scaling coefficients may be derived to improve point scale estimates. TL and transect measurements were upscaled to estimate grid scale means. Upscaled estimates were compared and found to be consistent, indicating that appropriately stratified point scale measurements can be used to approximate a grid scale mean when transect data are not available. These findings are important in remote regions such as the study area, where frequent transect data may be difficult to obtain. TL, transect, and upscaled means were compared with modelled depth and SWE. Model comparisons with TL and transect data indicated that bias was dependent on land cover, measurement scale, and seasonality. Modelled means compared well with upscaled estimates, but model SWE was underestimated during spring melt. These findings highlight the importance of understanding the spatial representativeness of in situ measurements and the processes those measurements represent when validating gridded snow products or assimilating data into models.