Snow processes in mountain forests: interception modeling for coarse-scale applications

Nora Helbig,Jean-Matthieu Monnet,Michaela Teich,Laure Vincent,Yves Lejeune,Jean-Emmanuel Sicart,David Moeser

doi:10.5194/hess-24-2545-2020

Abstract

Abstract. Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.

Highlights

Snow interception is the amount of snow captured in a forest canopy
Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale digital surface model (DSM) is available to derive subgrid forest parameters
While it is clear that accumulated snowfall is the key parameter for modeling snow interception by forest canopy and as such regulates its magnitude, the shape of the interception curve is predominantly controlled by forest canopy parameters and the interception model form itself

Summary

Introduction

Snow interception is the amount of snow captured in a forest canopy. As much as 60 % of the cumulative snowfall may be retained in evergreen coniferous forests (Pomeroy and Schmidt, 1993; Pomeroy et al, 1998; Storck and Lettenmaier, 2002). Modeling the spatial distribution of snow in forested regions is necessary for climate and water resource modeling over a variety of scales (see Essery et al, 2009; Rutter et al, 2009). In mountainous areas where forested and alpine regions coexist, accurate estimates of forest albedo play a key role in correctly modeling the surface energy balance. Due to the connectivity between interception and albedo, formulations of surface albedo over forested areas necessitate estimates of intercepted snow (e.g., Roesch et al, 2001; Roesch and Roeckner, 2006; Essery, 2013; Bartlett and Verseghy, 2015)

Methods

Results

Discussion

Conclusion