Abstract:Precipitation phase is fundamental to a catchment's hydrological response to precipitation events. Phase is particularly variable over time and space in the Canadian Rockies where snowfall or rainfall can occur any month of the year. Phase is controlled by the microphysics of the falling hydrometeor, but microphysical calculations require detailed atmospheric information that is often lacking for hydrological analyses. In hydrology, there have been many methods developed to estimate phase, but most are regionally calibrated, and many depend on air temperature (T a ) and use daily time steps. Phase is not only related to T a , but to other meteorological variables, and precipitation events are temporally dynamic, adding uncertainty to the use of daily indices to estimate phase. To better predict precipitation phase, the psychrometric energy balance of a falling hydrometeor was calculated and used to develop a method to estimate precipitation phase. High quality precipitation phase and meteorological data were observed at multiple elevations in a small Canadian Rockies catchment, Marmot Creek Research Basin, at 15-min intervals over several years to develop and test the method. The results of the psychrometric energy balance method were compared to phase observations, to other methods over varying time scales and seasons and at varying elevations and topographic exposures. The results indicate that the psychrometric energy balance method performs much better than T a index methods and that this improvement, and the accuracy of the psychrometric energy balance method, increases as the time step of calculation decreases.

Abstract. Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land-atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s −1 , clear skies, high sun angles and surfaces with negligible vegetation cover.

Abstract. Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.

Recent findings that 2-anilo-5-[(4-methylpentan-2yl)amino]cyclohexa-2,5-diene-1,4-dione (6PPD-quinone), the transformation product of a common tire rubber antioxidant, is acutely toxic in stormwater-impacted streams has highlighted the need for a better understanding of contaminants in urban runoff. This study represents one of the first reports of 6PPD-quinone and other tire rubber-derived compounds in stormwater and snowmelt of a coldclimate Canadian city (Saskatoon, 2019(Saskatoon, −2020. Semiquantification of the five target compounds, N,N′-diphenylguanidine (DPG), N,Ndicyclohexylmethylamine (DCA), N,N′-dicyclohexylurea (DCU), 1cyclohexyl-3-phenylurea (CPU), and 6PPD-quinone, revealed DPG was most abundant, with average concentrations of 60 μg L −1 in stormwater and 1 μg L −1 in snowmelt. Maximum observed concentrations of DPG were greater than 300 μg L −1 , equivalent to loadings of 15 kg from a single rain event. These concentrations of DPG represent some of the highest reported in urban runoff globally. 6PPD-Quinone was detected in 57% (12/ 21) of stormwater samples with a mean concentration of approximately 600 ng L −1 (2019) and greater than 80% (28/31) of snowmelt samples with mean concentrations of 80−370 ng L −1 (2019 and 2020). Concentrations of 6PPD-quinone exceeded the acute LC 50 for coho salmon (0.8−1.2 μg L −1 ) in greater than 20% of stormwater samples. Mass loadings of all target chemicals correlated well with roads and residential land-use area.