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Temperature interpolation at a large scale: test on a small area in Svalbard
Author(s) -
Joly D.,
Nilsen L.,
Fury R.,
Elvebakk A.,
Brossard T.
Publication year - 2003
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.949
Subject(s) - environmental science , latitude , digital elevation model , scale (ratio) , glacier , geographic coordinate system , climatology , arctic , vegetation (pathology) , elevation (ballistics) , spatial distribution , multivariate interpolation , field (mathematics) , interpolation (computer graphics) , climate change , remote sensing , physical geography , geography , geology , computer science , geodesy , mathematics , statistics , cartography , oceanography , medicine , animation , geometry , pathology , computer graphics (images) , pure mathematics , bilinear interpolation
A major environmental concern regarding the Arctic is how global change effects can influence vegetation and ecosystems. The amount of summer warmth is the single most important variable for biological processes in the Arctic, and the one that is most likely to be affected by climate change. A major task is to establish how temperature conditions are modified at a very high spatial resolution. In order to build up scenarios that are as relevant as possible concerning vegetation development, the key point is to know how the spatial distribution of temperature varies and can be related to different environment factors at different spatial scales. A method is suggested for modelling the temperature distribution at a high resolution (2 × 2 m 2 ) and with high accuracy. In step one, a polynomial equation is established for modelling the distribution of temperature values measured in the field at low spatial resolution (1 × 1 km 2 ), with latitude and longitude defining the general trend surface of the temperature distribution. In step two, residue values from the previous step are explained by environment factors stored as numerical information layers in a geographical information system. These environmental data are derived from a high‐resolution digital elevation model and from a digitized infrared aerial photograph. Finally, the combination of these kilometric and micro‐scale models make it possible to restore the thermic field of the study area and to represent it through different maps at a very fine‐grained resolution. The method is tested on two glacier forefields in Svalbard, covering an area of 8 km 2 . Copyright © 2003 Royal Meteorological Society