Snow interception modelling: Isolated observations have led to many land surface models lacking appropriate temperature sensitivities

When formulating a hydrologic model, scientists rely on parameterizations of multiple processes based on field data, but literature review suggests that more frequently people select parameterizations that were included in pre-existing models rather than re-evaluating the underlying field experiments. Problems arise when limited field data exist, when “trusted” approaches do not get reevaluated, and when sensitivities fundamentally change in different environments. The physics and dynamics of snow interception by conifers is just such a case, and it is critical to simulation of the water budget and surface albedo. The most commonly used interception parameterization is based on data from four trees from one site, but results from this field study are not directly transferable to locations with relatively warmer winters, where the dominant processes differ dramatically. Here, we combine a literature review with model experiments to demonstrate needed improvements. Our results show that the choice of model form and parameters can vary the fraction of snow lost through interception by as much as 30%. In most simulations, the warming of mean winter temperatures from ?7 to 0°C reduces the modelled fraction of snow under the canopy compared to the open, but the magnitude of simulated decrease varies from about 10% to 40%. The range of results is even larger when considering models that neglect the melting of in-canopy snow in higher-humidity environments where canopy sublimation plays less of a role. Thus, we recommend that all models represent canopy snowmelt and include representation of increased loading due to increased adhesion and cohesion when temperatures rise from ?3 to 0°C. In addition to model improvements, field experiments across climates and forest types are needed to investigate how to best model the combination of dynamically changing forest cover and snow cover to better understand and predict changes to albedo and water supplies.