Snow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post‐storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611–1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785–7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time‐lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule‐based direct insertion method. Interception determined using density estimates from the Hedstrom‐Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data. Assimilation of tree‐measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time‐lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data.

Snow water equivalent (SWE) is one of the most hydrologically important physical properties of a snowpack. The U.S. National Weather Service's Snow Data Assimilation System (SNODAS) provides snow products at high spatial (~1 km2) and temporal (daily) resolution for the contiguous United States and southern Canada. This study evaluated the SNODAS SWE product in the boreal forest, prairie, and Canadian Rockies of western Canada against extensive snow survey measurements. SNODAS was found to work well in sheltered environments, to overestimate SWE under needle‐leaf forests, and to be unable to capture the spatial variation of SWE in windswept prairie and alpine environments. Results indicate that SNODAS SWE accuracy is strongly influenced by the missing blowing snow redistribution and canopy energetics and snow interception and sublimation processes in the mass balance calculations of the SNODAS model and by erroneous precipitation data forcing the model. To demonstrate how errors caused by missing processes can be corrected in areas with low assimilation frequency, SNODAS data were assimilated into a physically based hydrological model created using the modular Cold Region Hydrological Modelling (CRHM) platform that includes blowing and intercepted snow redistribution and subcanopy melt energetic processes. This approach decreased the overestimation of SWE compared to SNODAS from 135 to 79% in the study area and suggests that snow assimilation modeled SWE quality can be improved if snow redistribution, sublimation, and subcanopy melt processes are incorporated.

Abstract Snow interception in cold regions needleleaf forest canopies is a crucial process that controls local snow accumulation and redistribution over >20% of the Earth's land surface. Various ground-based methods exist to measure intercepted snow load, however all are based on single-tree measurements and are difficult to implement. No research has focussed on detecting large areal intercepted snow loads and no studies have assessed the use of satellite observations. In this study, four remote sensing indices (NDSI, NDVI, albedo, and land surface temperature (LST)) were retrieved from Landsat images to study their sensitivity to canopy intercepted snow and the possibility of using them to detect the presence of intercepted snow. The results indicate that presence of intercepted snow on canopy increased NDSI and albedo, but decreased NDVI. Intercepted snow presence also decreased the areal variability of NDSI and NDVI while increasing that of albedo. For these three indices, the differences between snow-free and snowcovered canopies were correlated to topography and forest canopy cover. Of these indices, NDSI changed the greatest. Intercepted snow noticeably decreased the LST difference between forest and open areas in springtime while the influence in wintertime was relatively smaller. An intercepted snow detection approach that uses both NDSI and NDVI to classify pixels into either snowcovered canopy or other (snow-free canopy and non-forest areas) is proposed here. A case study applying this approach compared remote sensing detection to simulations by the snow interception and sublimation model implemented in the Cold Regions Hydrological Modelling platform (CRHM). This used local meteorological observations from the pine, spruce and fir forest covered Marmot Creek Research Basin in the Canadian Rockies. The remote sensing detection of intercepted snow agreed well with CRHM simulations for continuous forests (83%) and less well for sparse forests (72%) and clearings with small trees (70%). Therefore, the approach is suitable for intercepted snow detection over continuous evergreen canopies. This technique provides a new capability for large-scale snow interception model validation and data assimilation to cold regions hydrological forecasting models.