Abstract Accurate estimations of the precipitation phase at the surface are critical for hydrological and snowpack modelling in cold regions. Precipitation phase partitioning methods (PPMs) vary in their ability to estimate the precipitation phase at around 0°C and can significantly impact simulations of snowpack accumulation and melt. The goal of this study is to evaluate PPMs of varying complexity using high‐quality observations of precipitation phase and to assess the impact on snowpack simulations. We used meteorological data collected in Edmundston, New Brunswick, Canada, during the 2021 Saint John River Experiment on Cold Season Storms (SAJESS). These data were combined with manual observations of snow depth. Five PPMs commonly used in hydrological models were tested against observations from a laser‐optical disdrometer and a Micro Rain Radar. Most PPMs produced similar accuracy in estimating only rainfall and snowfall. Mixed precipitation was the most difficult phase to predict. The multi‐physics model Crocus was then used to simulate snowpack evolution and to diagnose model sensitivity to snowpack accumulation processes (PPM, snowfall density, and snowpack compaction). Sixteen snowpack accumulation periods, including nine warm accumulation events (average temperatures above −2°C) were observed during the study period. When considering all accumulation events, simulated changes in snow water equivalent ( SWE ) were more sensitive to the type of PPM used, whereas simulated changes in snow depth were more sensitive to uncertainties in snowfall density. Choice of PPM was the main source of model sensitivity for changes in SWE and snow depth when only considering warm events. Overall, this study highlights the impact of precipitation phase estimations on snowpack accumulation at the surface during near‐0°C conditions.

Abstract Bare soil evaporation has been studied extensively, but less is certain regarding how site‐specific features, especially the overstory tree canopy and ground covers, mediate evaporation processes. Inspired by recent advances on modelling bare soil evaporative efficiency (SEE), this study explored SEE over a range of soil substrates and ground cover types, with and without the presence of an overstory canopy in three mesic ecosystems in Canadian Rocky Mountains. A significant relationship was found between the critical soil water content and ground cover mass fractions across various ground cover types, both with and without the presence of an overstory canopy. This relationship is expected to be prevalent across various ecosystems. Moreover, a simple approach for modelling SEE of vegetated surfaces and a correction method to account for below‐canopy SEE is also proposed. The model yields satisfactory simulations, and the approach is expected to be widely applicable, given the strength that its parameters are easily acquired, and its formulations are simple and straightforward. While the model may be particularly suited to mesic ecosystems, the underlying mechanism of SEE suggests that this model can also be applied in dryer conditions. This approach will greatly improve ET parameterization in land‐surface models (LSMs) and increase our knowledge of the global water cycle and ecosystem responses under climate change impacts.