Snow-vegetation relations in a High Arctic ecosystem: Inter-annual variability inferred from new monitoring and modeling concepts

• DOI: 10.1016/j.rse.2006.06.016
• Publication Date: 2006-09-13T11:16:35Z
• Authors: Jørgen Buus-Hinkler; Birger U. Hansen; Mikkel P. Tamstorf; Steen B. Pedersen
• Keywords: Snowmelt

Abstract For High Arctic ecosystems, snow is one of the most important climatic factors—affecting both length of the growing season, and primary plant production. To perform a retrospective analysis of inter-annual variability in snow distribution/amount and vegetative activity in a High Arctic ecosystem, these factors were investigated for the Zackenberg valley at 74.5°N, 21°.0W in Northeast Greenland. The analysis was based on recently introduced techniques that utilize Conventional- and Multispectral Digital Camera Images combined with Landsat TM/ETM+ and SPOT HRV satellite data. Since 1998 (and since 2002 multispectral) digital cameras have been installed on the Zackenberg mountain 500 m above the valley floor taking daily images of the valley automatically. The images taken by the cameras were transformed into digital orthophotos, which were then used to analyze snow cover and Normalized Difference Vegetation Index (NDVI). The snow cover and NDVI data were used to develop models to calculate end-of-winter snow accumulation, snow-cover-depletion, and net vegetative activity in 16 different melting (growing) seasons (1988–2004). At Zackenberg the end-of-winter snow accumulation shows significant inter-annual variability, whereas the end-of-winter snow cover distribution remains similar from year to year. A comparison between snow cover and NDVI distribution reveals that vegetative vigor in the Zackenberg area primarily is linked to the initiation time of the snow-free period rather than temperature. This indicates that in some Arctic regions increases in winter (snow)-precipitation might be as or even more crucial for the ecosystem, than the increased temperatures projected by the majority of General Circulation Models (GCMs).