Representativeness of Local Snow Data
for Large-Scale Hydrologic Investigations
Daqing Yang1 and Ming-ko Woo2
Arctic snow cover usually attains maximum values at the end of winter and such information is
important for hydrological investigations because most floods are associated with spring snowmelt.
Snow data obtained from weather stations or collected at some local sites are often extrapolated to
large areas, but without verifying that the upscaling procedure yields correct results. This study
compares maximum snow cover data gathered over two large target areas (170 to 300 km2) with
weather station snow course measurements to determine the representativeness of local-scale data
for areas typically occupied by large grid cells of macro-hydrological models. The field snow survey
results confirmed the controlling role of terrain on snow distribution in the High Arctic. The variabil-
ity of area mean snow water equivalence for a grid cell (with dimensions of 1 1 km2 to 13 13 km2)
increases with terrain complexity but decreases with grid size. Although point data do not represent
the snow cover over an area, an attempt was made to upscale the weather station data to the target
areas using an index method. Test results show that this index approach works well in the area with
a shallow snow cover, but the error increases for an area with relatively deep snow. More effort is
need to refine this method, perhaps in conjunction with remote sensing, so that point data can be
upscaled to yield snow information suitable for large-scale hydrological models or land surface
schemes.
1
Frontier Research Program for Global Change, Frontier Research Promotion Office, Seavans Building, 7th
Floor, 1-2-1 Shibaura, Minato-ku, Tokyo 105, Japan
2 School of Geography and Geology, McMaster University, Hamilton, Ontario L8S 4K1, Canada
43
Previous Page
