Snow accumulation in forests from ground and remote‐sensing data

Abstract

AbstractWinter‐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 canopy‐structure 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 = 0·44 − 0·6 × SVF for SVF < 0·72 and F = 0 for SVF > 0·72, and F = 0·11 LAI. These relationships seemed valid for evergreen conifers, larch trees, alder, birch and mixed deciduous stands. Forest snow accumulation (SF) could be estimated from snowfall in open fields (So) and to LAI according to SF = So (1 − 0·11 LAI) as well as from SVF according to SF = So (0·56 + 0·6 SVF) for SVF < 0·72. The value of SF was equal to So for SVF values above 0·72. 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 SF were related to NDSI for these plots according to: F = −0·37NDSI + 0·29 and SF = So (0·81 + 0·37NDSI). 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. Copyright © 2004 John Wiley & Sons, Ltd.