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Monte Carlo Simulation of Snow Depth in a Forest
Author(s) -
Woo MingKo,
Steer Peter
Publication year - 1986
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr022i006p00864
Subject(s) - snow , monte carlo method , subarctic climate , environmental science , sampling (signal processing) , tree (set theory) , physical geography , hydrology (agriculture) , geology , statistics , mathematics , geography , geomorphology , computer science , geotechnical engineering , mathematical analysis , oceanography , filter (signal processing) , computer vision
Snow depth in a forest is highly variable and to reduce the cost of extensive field sampling for obtaining mean depths, a simulation model was developed. First, the location of individual trees in a representative portion of the forest is either surveyed in the field or simulated based on the statistical characteristics pertaining to the distribution of trees. In this forest, a large number of randomly located sample points is generated by Monte Carlo technique. The azimuth and distance from each point to the nearest tree is determined, and a snow depth simulated based on the observed snow depth distribution around individual trees. The model was applied successfully to a northern spruce forest in subarctic Ontario, showing that this simulation provides a useful approach to determine mean snow depth.