Snowmelt contributes a significant fraction of groundwater recharge in snow‐dominated regions, making its accurate quantification crucial for sustainable water resources management. While several components of the hydrological cycle can be measured directly, catchment‐scale recharge can only be quantified indirectly. Stable water isotopes are often used as tracers to estimate snowmelt recharge, even though estimates based on stable water isotopes are biased due to the large variations of δ²H and δ¹⁸O in snow and the difficulty to measure snowmelt directly. To overcome this gap, a new tracer method based on on‐site measurements of dissolved He, ⁴⁰Ar, ⁸⁴Kr, N₂, O₂, and CO₂ is presented. The new method was developed alongside classical tracer methods (stable water isotopes, ²²²Rn, ³H/³He) in a highly instrumented boreal catchment. By revealing (noble gas) recharge temperatures and excess air, dissolved gases allow (i) the contribution of snowmelt to recharge, (ii) the temporal recharge dynamics, and (iii) the primary recharge pathways to be identified. In contrast to stable water isotopes, which produced highly inconsistent snowmelt recharge estimates for the experimental catchment, dissolved gases produced consistent estimates even when the temperature of snowmelt during recharge was not precisely known. As dissolved gases are not controlled by the same processes as stable water isotopes, they are not prone to the same biases and represent a highly complementary tracer method for the quantification of snowmelt recharge dynamics in snow‐dominated regions. Furthermore, an observed systematic depletion of N₂ in groundwater provides new evidence for the pathways of biological N‐fixation in boreal forest soils.

The Canadian Land Surface Scheme (CLASS) has been applied over the years in coupled and uncoupled (offline) modes at local, regional, and global scales using various forcing datasets. In this study, CLASS is applied at a local scale in the offline configuration to evaluate its performance when driven by the ERA5 reanalysis. Simulated surface energy fluxes, as well as several other water balance components, are investigated at four sites across the Canadian boreal biome. The results from CLASS driven by ERA5 (CLASS-RNL) are compared with available in situ measurements, as well as with results from CLASS driven by observations (CLASS-CTL). Additional simulations are conducted to evaluate the effects of biases in the ERA5 precipitation, where CLASS is forced by ERA5 data, but with ERA5 precipitation being replaced by observed precipitation (CLASS-RNL-ObsP). The results show that simulated surface variables in CLASS-RNL are in good agreement with observations as well as with those simulated in CLASS-CTL. The CLASS-RNL captures well the observed annual cycles of the surface energy and water fluxes, as well as the year-to-year variation of snow depth, soil temperature, and soil moisture. A strong correlation is found between the observed and CLASS-RNL simulated snow depth and soil temperature. Biases in the ERA5 precipitation did not affect the simulation of soil state variables, whereas the simulated surface heat and water fluxes, as well as the snow depth, were significantly affected. For instance, the simulated runoff in CLASS-RNL is much higher than in CLASS-RNL-ObsP and CLASS-CTL at the most humid sites due to significant positive bias in ERA5 precipitation.

Cold content (CC) is an internal energy state within a snowpack and is defined by the energy deficit required to attain isothermal snowmelt temperature (0 ^∘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 60 % 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 218 d, with melt events occurring on 42 of those days. Based on snowpit observations, the largest mean CC (−2.62 MJ m⁻²) was observed at the site with the thickest snow cover. The maximum difference in mean CC between the four study sites was −0.47 MJ m⁻², representing a site-to-site variability of 20 %. Before analyzing the reconstructed CC time series, a comparison with snowpit data confirmed that CLASS yielded reasonable bulk estimates of snow water equivalent (SWE) (R²=0.64 and percent bias (Pbias) $=-\mathrm{17.1}$ %), snow density (R²=0.71 and Pbias =1.6 %), and cold content (R²=0.93 and Pbias $=-\mathrm{3.3}$ %). A snow density profile derived by utilizing an empirical formulation also provided reasonable estimates of layered cold content (R²=0.42 and Pbias =5.17 %). 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.

Temperature-index (TI) models are commonly used to simulate the volume and occurrence of meltwater in snow-fed catchments. TI models have varying levels of complexity but are all based on air temperature observations. The quality and availability of data that drive these models affect their predictive ability, particularly given that they are frequently applied in remote environments. This study investigates the performance of non-calibrated TI models in simulating the subcanopy snow water equivalent (SWE) of a small watershed located in Eastern Canada, for which some distinctive observations were collected. Among three relatively simple TI algorithms, the model that performed the best was selected based on the average percent bias (Pbias of 24%) and root mean square error (RMSE of 100 mm w.e.), and was designated as the base TI model. Then, a series of supplemental tests were conducted in order to quantify the performance gain that resulted from including the following inputs/processes to the base TI model: subcanopy incoming radiation, canopy interception, snow surface temperature, sublimation, and cold content. As a final test, all the above modifications were performed simultaneously. Our results reveal that, with the exception of snow sublimation (Pbias of 5.4%) and snow surface temperature, the variables mentioned above were unable to improve TI models within our sites. It is therefore worth exploring other feasible alternatives to existing TI models in complex forested environments.

Cold content (CC) is an internal energy state within a snowpack and is defined by the energy deficit required to attain isothermal snowmelt temperature (0 °C). For any snowpack, fulfilling the cold content deficit is a pre-requisite before the onset of the snowmelt. Cold content for a given snowpack thus plays a critical role because it affects both the timing and the rate of snowmelt. Estimating the 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 2017–18. The analysis was divided into two steps. In the first step, the observed CC data from weekly snowpits for 60 % of the snow cover period were examined. During the second step, a reconstructed time series of 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 218 days, with melt events occurring on 42 of those days. Based on snowpit observations, the largest mean CC (−2.62 MJ m⁻²) was observed at the site with the thickest snow cover. The maximum difference in mean CC between the four study sites was −0.47 MJ m⁻², representing a site-to-site variability of 20 %. Before analyzing the reconstructed CC time series, a comparison with snowpit data confirmed that CLASS yielded reasonable estimates of the snow water equivalent (SWE) (R² = 0.64 and percent bias (Pbias) = −17.1 %), bulk snow density (R² = 0.71 and Pbias = 1.6 %), and bulk cold content (R² = 0.90 and Pbias = −2.0 %). A snow density profile derived by utilizing an empirical formulation also provided reasonable estimates of cold content (R² = 0.42 and Pbias = 5.17 %). 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.