In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

Abstract. From 19 to 22 June 2013, intense rainfall and concurrent snowmelt led to devastating floods in the Canadian Rockies, foothills and downstream areas of southern Alberta and southeastern British Columbia, Canada. Such an event is typical of late-spring floods in cold-region mountain headwater, combining intense precipitation with rapid melting of late-lying snowpack, and represents a challenge for hydrological forecasting systems. This study investigated the factors governing the ability to predict such an event. Three sources of uncertainty, other than the hydrological model processes and parameters, were considered: (i) the resolution of the atmospheric forcings, (ii) the snow and soil moisture initial conditions (ICs) and (iii) the representation of the soil texture. The Global Environmental Multiscale hydrological modeling platform (GEM-Hydro), running at a 1 km grid spacing, was used to simulate hydrometeorological conditions in the main headwater basins of southern Alberta during this event. The GEM atmospheric model and the Canadian Precipitation Analysis (CaPA) system were combined to generate atmospheric forcing at 10, 2.5 and 1 km over southern Alberta. Gridded estimates of snow water equivalent (SWE) from the Snow Data Assimilation System (SNODAS) were used to replace the model SWE at peak snow accumulation and generate alternative snow and soil moisture ICs before the event. Two global soil texture datasets were also used. Overall 12 simulations of the flooding event were carried out. Results show that the resolution of the atmospheric forcing affected primarily the flood volume and peak flow in all river basins due to a more accurate estimation of intensity and total amount of precipitation during the flooding event provided by CaPA analysis at convection-permitting scales (2.5 and 1 km). Basin-averaged snowmelt also changed with the resolution due to changes in near-surface wind and resulting turbulent fluxes contributing to snowmelt. Snow ICs were the main sources of uncertainty for half of the headwater basins. Finally, the soil texture had less impact and only affected peak flow magnitude and timing for some stations. These results highlight the need to combine atmospheric forcing at convection-permitting scales with high-quality snow ICs to provide accurate streamflow predictions during late-spring floods in cold-region mountain river basins. The predictive improvement by inclusion of high-elevation weather stations in the precipitation analysis and the need for accurate mountain snow information suggest the necessity of integrated observation and prediction systems for forecasting extreme events in mountain river basins.