Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada

Abstract

Cold content (CC) is an internal energy state within a snowpack and is defined by the energy deficit required to attain isothermal snowmelt temperature (0g€¯g C). Cold content for a given snowpack thus plays a critical role because it affects both the timing and the rate of snowmelt. Measuring cold content is a labour-intensive task as it requires extracting in situ snow temperature and density. Hence, few studies have focused on characterizing this snowpack variable. This study describes the multilayer cold content of a snowpack and its variability across four sites with contrasting canopy structures within a coniferous boreal forest in southern Québec, Canada, throughout winter 2017-2018. The analysis was divided into two steps. In the first step, the observed CC data from weekly snowpits for 60g€¯% of the snow cover period were examined. During the second step, a reconstructed time series of modelled CC was produced and analyzed to highlight the high-resolution temporal variability of CC for the full snow cover period. To accomplish this, the Canadian Land Surface Scheme (CLASS; featuring a single-layer snow model) was first implemented to obtain simulations of the average snow density at each of the four sites. Next, an empirical procedure was used to produce realistic density profiles, which, when combined with in situ continuous snow temperature measurements from an automatic profiling station, provides a time series of CC estimates at half-hour intervals for the entire winter. At the four sites, snow persisted on the ground for 218g€¯d, with melt events occurring on 42 of those days. Based on snowpit observations, the largest mean CC (-2.62g€¯MJg€¯m-2) was observed at the site with the thickest snow cover. The maximum difference in mean CC between the four study sites was -0.47g€¯MJg€¯m-2, representing a site-to-site variability of 20g€¯%. Before analyzing the reconstructed CC time series, a comparison with snowpit data confirmed that CLASS yielded reasonable bulk estimates of snow water equivalent (SWE) (R2Combining double low line0.64 and percent bias (Pbias) Combining double low line-17.1g€¯%), snow density (R2Combining double low line0.71 and Pbias Combining double low line1.6g€¯%), and cold content (R2Combining double low line0.93 and Pbias Combining double low line-3.3g€¯%). A snow density profile derived by utilizing an empirical formulation also provided reasonable estimates of layered cold content (R2Combining double low line0.42 and Pbias Combining double low line5.17g€¯%). Thanks to these encouraging results, the reconstructed and continuous CC series could be analyzed at the four sites, revealing the impact of rain-on-snow and cold air pooling episodes on the variation of CC. The continuous multilayer cold content time series also provided us with information about the effect of stand structure, local topography, and meteorological conditions on cold content variability. Additionally, a weak relationship between canopy structure and CC was identified.