Snow accumulation in forests from ground and remote‐sensing data

Abstract

Abstract:Winter-forest processes affect global and local climates. The interception-sublimation fraction (F) of snowfall in forests is a substantial part of the winter water budget (up to 40%). Climate, weather-forecast and hydrological modellers incorporate increasingly realistic surface schemes into their models, and algorithms describing snow accumulation and snow-interception sublimation are now finding their way into these schemes. Spatially variable data for calibration and verification of wintertime dynamics therefore are needed for such modelling schemes. The value of F was determined from snow courses in open and forested areas in Hokkaido, Japan. The value of F was related to species and canopystructure measures such as closure, sky-view fraction (SVF ) and leaf-area index (LAI ). Forest structure was deduced from fish-eye photographs. The value of F showed a strong linear correlation to structure: F D 0Ð44 0Ð6 ð SVF for SVF < 0Ð72 and F D 0 for SVF > 0Ð72, and F D 0Ð11 LAI. These relationships seemed valid for evergreen conifers, larch trees, alder, birch and mixed deciduous stands. Forest snow accumulation S F could be estimated from snowfall in open fields S o and to LAI according to S F D S o (1 0Ð11 LAI ) as well as from SVF according to S F D S o (0Ð56 C 0Ð6 SVF) for SVF < 0Ð72. The value of S F was equal to S o for SVF values above 0Ð72. The value of sky-view fraction was correlated to the normalized difference snow index (NDSI ) using a Landsat-TM image for observation plots exceeding 1 ha. Variables F and S F were related to NDSI for these plots according to: F D 0Ð37NDSI C 0Ð29 and S F D S o 0Ð81 C 0Ð37NDSI . These relationships are somewhat hypothetical because plot-size limitation only allowed one sparse-forest observation of NDSI to be used. There is, therefore, a need to confirm these relationships with further studies.

Runoff Response to Climate Warming and Forest Disturbance in a Mid-Mountain Basin

“…The study area is covered by a coniferous forest, the canopy of which can efficiently intercept precipitation, especially in the form of snow. The intercepted rainwater or snow directly control water losses through sublimation governed by radiation fluxes [54,55]. Therefore, during a snow-melting period, forest decline or forest disturbance led to an increase in the frequency and magnitude of high flows [18,55].…”

Influence of canopy density on snow distribution in a temperate mountain range

“…Thus, SVF represents the weighted gap fraction from the forest floor (Hellstrom, 2000). Lundberg et al (2004) note that the use of SVF in analysing variability in snow distribution provides an accuracy comparable to other measurements of canopy density, such as leaf area index. A grey-scale threshold was used to discriminate between forest canopy and sky (Lundberg et al, 2004).…”

“…Others highlight the role of the forest canopy in controlling snow accumulation (through interception and subsequent evaporation/sublimation) and melting processes (by affecting various elements of the snow energy balance). Thus, spatial variations in the density of the forest canopy can lead to significant variations in the spatial distribution of snowpack, which in areas beneath a dense forest canopy can be as little as 40-50% of the snowpack thickness in open areas (Bernier and Swanson, 1992;Pomeroy and Gray, 1995;Pomeroy et al, 1998;Koivusalo and Kokkonen, 2002;Murray and Buttle, 2003;Lundberg et al, 2004). Recently, Talbot et al (2006) reported that snow dynamics are more highly dependent on stand density than on aspect.…”

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

“…Truthful identification of the precipitation phase (rain/snow) is of course crucial for the functioning of meteorological models that forecast the precipitation phase itself [2] but also for accurate correction of gauge measured winter precipitation [3] and for land surface models (LSM) predicting snow accumulation and melt [4], glacier and polar ice water balance models [5], models for lake and sea ice growth [6], and climate change models [7]. It is also important for models predicting avalanche hazards [8], sublimation of snow in forests [9], urban snowmelt quality [10], winter road safety [11], infiltration into frozen soils [12], survival of mammals and plants under snow cover [13], flooding from rain on snow events [14] etc. Precipitation phase determination is a modeling challenge for both hydrology and meteorology; therefore, a cross discipline approach combining methods and knowledge from both sciences could lead to new insight for both.…”