Resolving Small‐Scale Forest Snow Patterns Using an Energy Balance Snow Model With a One‐Layer Canopy

Abstract

Modeling spatiotemporal dynamics of snow in forests is challenging, as involved processes are strongly dependent on small-scale canopy properties. In this study, we explore how local canopy structure information can be integrated in a medium-complexity energy balance snow model to replicate observed snow patterns at very high spatial resolutions. Snow depth distributions simulated with the Flexible Snow Model (FSM2) were tested against extensive experimental data acquired in discontinuous subalpine forest stands in Eastern Switzerland over three winters. While the default canopy implementation in FSM2 fails to capture the observed snow depth variability, performance is considerably improved when local canopy cover fraction and hemispherical sky view fraction are additionally accounted for (30% reduction in root mean square error). However, realistic snow depth distribution patterns throughout the season are only achieved if effective temperatures of near and distant canopy elements are discerned and if a mechanism to mimic preferential deposition of snow in canopy gaps is included. We demonstrate that by diversifying the canopy structure input in order to reflect respective portions of the canopy relevant to different processes, even a simple model based on widely used process parameterizations and canopy metrics can be applied for high-resolution simulations of the sub-canopy snow cover with just a few modifications. The presented approaches could be implemented in commonly used land surface models, allowing upscaling experiments and development of sub-grid parameterizations without necessitating complex high-resolution models.

Process‐Level Evaluation of a Hyper‐Resolution Forest Snow Model Using Distributed Multisensor Observations

“…This study furthers the work of Mazzotti et al (2020) by addressing two major research needs identified in their conclusion. First, we attempt to overcome the limitations of existing model evaluation efforts by validating our hyper‐resolution FSM2 simulations of the forest snowpack evolution with novel data sets acquired with mobile multisensor platforms.…”

“…Model features specific to the enhanced FSM2 as introduced in this study are detailed below. Note that we only outline new options added to the original FSM2 version presented in Mazzotti et al (2020). For a comprehensive description of the original FSM2 canopy module, we refer to the Appendix in their publication.…”

“…The FSM2 (Mazzotti et al, 2020) used in this study is a stand‐alone, open‐source, physics‐based multimodel framework that adds a canopy implementation to the snow model FSM (Essery, 2015), allowing snowpack simulations within forests stands. The snowpack in FSM2 is represented with medium complexity, which is suitable for snow‐hydrological applications that do not require detailed simulation of snowpack stratigraphy (Magnusson et al, 2015).…”

“…Hyper-resolution models have so far mainly been assessed through comparison of snow depth distribution patterns, maintaining uncertainties in individual process representations. Broxton et al (2015) and Mazzotti et al (2020) demonstrated their models' ability to replicate small-scale snow depth patterns within discontinuous stands using spatial snow depth distribution data obtained from airborne lidar and extensive manual surveys. However, Mazzotti et al (2020) highlighted that model validation based exclusively on snow distribution data may mask error compensation mechanisms within the models.…”

“…Broxton et al (2015) and Mazzotti et al (2020) demonstrated their models' ability to replicate small-scale snow depth patterns within discontinuous stands using spatial snow depth distribution data obtained from airborne lidar and extensive manual surveys. However, Mazzotti et al (2020) highlighted that model validation based exclusively on snow distribution data may mask error compensation mechanisms within the models. They argued that further evaluation should occur at the level of individual fluxes, which, to date, has not been possible due to a general lack of suitable experimental data.…”