Characterizing Maritime Snow Canopy Interception in Forested Mountains

Abstract

Air temperature (T air ) plays an important role in determining how a canopy intercepts snow and apart from event size is the single most important micrometeorological variable found to adequately influence interception rates and magnitude. We present results from a 6-year study on snow-forest interactions. This data set reveals the central role T air plays in how a forest intercepts snow and the need to effectively incorporate this within snow process models. Warm temperature events show a higher canopy interception efficiency (CIE) for all sites across the study period, while colder T air events demonstrated a lower corresponding CIE. Additionally, there is a structural component of the forest itself that plays a role in the ability of canopy to intercept snow. Recognizing the physical vertical structure of a forest as nontrivial, we present a simple canopy interception model that includes a novel three-dimensional forest metric that captures canopy complexity, G z , combined with event size and T air to adequately predict event-based interception. Low complexity forests decrease interception capacity from the outset, whereas a highly complex or diverse forest increases interception potential and leads to a nonlinear increase in temperature-based canopy interception due to more surface area able to intercept falling snow. Essentially, forest structure sets the boundary condition of the potential to intercept, while event size and T air determines the rate or amount of interception. We develop a simple canopy snow interception model from these data and compare modeled output against three commonly used interception models in both a maritime and continental snow climate. Partitioning the relative importance of snow-forest interactions on canopy interception will help provide the information to accurately model snow-forest interactions and also aid in our water resources predictive capabilities now and into the future. Special Section:Advances in remote sensing, measurement, and simulation of seasonal snow Key Points:• Snow interception in a maritime forest is driven by event size and air temperature within a boundary condition set by forest structure • We develop a simple canopy snow interception model from measured data across an elevation gradient and under various forest structure • Inclusion of air temperature in snow interception models is essential to adequately capture event-based canopy interception variability

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

“…However, forest snow dynamics are shaped by complex interacting processes that are controlled by the structure of the overhead canopy and thus display large spatial and temporal variation. Snow interception by the canopy Moeser, Stähli, et al, 2015;Roth & Nolin, 2019) and subsequent sublimation and unloading to the ground (MacKay & Pomeroy et al, 1998), shading of shortwave radiation (Hardy et al, 2004;Malle et al, 2019;Musselman, Molotch, Margulis, Kirchner, et al, 2012), and emission of longwave radiation by the vegetation (Essery, Pomeroy, et al, 2008;Pomeroy et al, 2009;Webster et al, 2016) all vary with canopy structure in specific ways and thus contribute to heterogeneous snow depth distribution patterns, which are difficult to replicate with models (Clark, Hendrikx, et al, 2011). The forest snow model intercomparison project SNOWMIP2 Rutter et al, 2009) evaluated 33 forest snow models differing in both process complexity and canopy implementation approaches.…”

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

“…They attribute this to errors in the processes linked to the snow interception model based on Hedstrom and Pomeroy (1998) due to an underestimation of the melt of intercepted snow. Previous snow interception models also failed to accurately model snow interception from a maritime climate (Roth and Nolin, 2019). While Roth and Nolin (2019) successfully modelled snow interception by including air temperature in a maritime climate, their model consistently underestimates snow interception in a continental climate forest.…”

“…Previous snow interception models also failed to accurately model snow interception from a maritime climate (Roth and Nolin, 2019). While Roth and Nolin (2019) successfully modelled snow interception by including air temperature in a maritime climate, their model consistently underestimates snow interception in a continental climate forest. Overall, this demonstrates the need for more robust parameterizations of the processes affecting snow under forest which is an important challenge for global snow modeling.…”

“…Several statistical parameterizations for forest interception snow depth (I HS ) and snow water equivalent (I SW E ) have been suggested using a variety of canopy metrics and functional dependencies for the rate and amount of storm snowfall (e.g. Satterlund and Haupt, 1967;Schmidt and Gluns, 1991;Hedstrom and Pomeroy, 1998;Hellström, 2000;Lundberg et al, 2004;Andreadis et al, 2009;Moeser et al, 2015b;Huerta et al, 2019;Roth and Nolin, 2019). Though these parameterizations have been demonstrated to perform well, they often rely on detailed forest canopy density and structure metrics which are either not readily available or cannot easily be upscaled, limiting functionality in models where the mean of model grid cells over several hundreds of meters to a few kilometers is required, i.e.…”

“…Total open area is defined as the total open area in the canopy around a point and mean distance to canopy defines how far away the edge of the canopy is from a point. Recently Roth and Nolin (2019) extended mean distance to canopy vertically, by deriving it for 1 m horizontal slices that were normalized with the corresponding elevation above the ground.…”