Snowfall interception in a deciduousNothofagusforest and implications for spatial snowpack distribution

Abstract

Native Nothofagus forests in the midlatitude region of the Andes Cordillera are notorious biodiversity hot spots, uniquely situated in the Southern Hemisphere such that they develop in snow‐dominated reaches of this mountain range. Spanning a smaller surface area than similar ecosystems, where forests and snow coexist in the Northern Hemisphere, the interaction between vegetation and snow processes in this ecotone has received lesser attention. We present the first systematic study of snow–vegetation interactions in the Nothofagus forests of the Southern Andes, focusing on how the interplay between interception and climate determines patterns of snow water equivalent (SWE) variability. The Valle Hermoso experimental catchment, located in the Nevados de Chillán vicinity, was fitted with eight snow depth sensors that provided continuous measurements at varying elevations, aspect, and forest cover. Also, manual measurements of snow properties were obtained during snow surveys conducted during end of winter and spring seasons for 3 years, between 2015 and 2017. Each year was characterized by distinct climatological conditions, with 2016 representing one of the driest winters on record in this region. Distance to canopy, leaf area index, and total gap area were measured at each observational site. A regression model was built on the basis of statistical analysis of local parameters to model snow interception in this kind of forest. We find that interception implied a 23.2% reduction in snow accumulation in forested sites compared with clearings. The interception in these deciduous trees represents, on average, 23.6% of total annual snowfall, reaching a maximum measured interception value of 13.8‐mm SWE for all snowfall events analysed in this research.

Variable effects of forest canopies on snow processes in a valley of the central Spanish Pyrenees

“…The variable climate of the mountainous headwaters induces high inter‐ and intra‐annual variability in snowpack characteristics (i.e., distribution, depth, density and snow–water equivalent [SWE]; Fayad et al, ). The characteristics of the snowpack also depend on interactions between topography and forest cover (Huerta, Molotch, & McPhee, ; Jenicek, Pevna, & Matejka, ; Jost, Weiler, Gluns, & Alila, ). Forest cover reduces snow accumulation on the ground because the tree canopies intercept some of the snowfall (Storck, Lettenmaier, & Bolton, ).…”

“…Previous studies have documented the different ways that snowpacks and forests interact at different altitudinal ranges in mid‐latitude mountain ranges (Huerta et al, ; Jenicek et al, ; Lundquist, Dickerson‐Lange, Lutz, & Cristea, ). However, there is uncertainty on the magnitude of these interactions among nearby areas and during different years, and this can affect the representativeness of a study site and study period that is used for research.…”

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

“…much as 24 % of total annual snowfall may be retained in deciduous forests in the Southern Andes (Huerta et al, 2019). Due to the sublimation of intercepted snow, a large portion of this snow never reaches the ground (Essery et al, 2003) and the interplay of interception and sublimation creates significant below forest heterogeneity in snow accumulation.…”

“…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.…”

“…This may explain why there is still no common ground with several snow-related variables in land surface models (Dirmeyer et al, 2006) which led to the current Earth System Model-Snow Model Intercomparison Project (ESM-SNOWMIP) showing overall larger errors in simulated snow depth on forest sites than on open sites (Krinner et al, 2018). Recently Huerta et al (2019) validated three previous snow interception models developed for coniferous forests with observed point snow interception values in a deciduous Nothofagus -forest of the Southern Andes. All three empirical models required recalibration, with the recalibrated Hedstrom and Pomeroy (1998) model showing the overall best performance.…”