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Contribution of environmental factors to temperature distribution at different resolution levels on the forefield of the Loven Glaciers, Svalbard

Published online by Cambridge University Press:  01 October 2007

Daniel Joly  and
Thierry Brossard
Daniel Joly
Affiliation:
Laboratoire THéMA, CNRS, Université de Franche-Comté, 30, rue Mégevand, F25030 Besançon, France
Thierry Brossard
Affiliation:
Laboratoire THéMA, CNRS, Université de Franche-Comté, 30, rue Mégevand, F25030 Besançon, France
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Abstract

The climate and its components (temperature and precipitation) are organised according to different spatial scales that are structured hierarchically. The aim of this paper is to explore the dependence between temperature and deterministic factors at different scales on a 10 km2 study area on the northwestern coast of Svalbard. A GIS was developed which contained three sources of information: temperature, remotely sensed imagery and digital elevation models (DEM), and derived raster data layers. The first layer, temperatures, was acquired at regularly observed temporal intervals from 53 stations. The second layer comprised remotely sensed images (aerial photography and SPOT imagery) and DEM data at 2 m and 20 m resolution, respectively. From these, a windowing procedure was applied to derive several spatial subsets of different spatial resolutions (6, 14, 30, 60, 140, and 300 m). The third layer comprised slope, aspect, and a theoretical solar radiation value derived from the DEM, and a vegetation index derived from the remotely sensed imagery. Linear regressions were then systematically conducted on the datasets, with temperature as the dependent variable, and each of the other data layers as the independent variables. By using graphical analysis, we link the correlation coefficients obtained for each factor, from the smallest spatial resolution (6 m) to the largest resolution (300 m). The results indicated that each explanatory variable and scale brings a specific contribution to changes in temperature. For example, the effect of elevation remains constant for all spatial resolutions, reflecting a quasi ‘non-scalar’ pattern of this variable. For other variables however, the effect of spatial scale can have a strong effect. In the case of solar radiation, a maximum of explanation was obtained for spatial resolutions of 14 m and 60 m; for vegetation index the optimum contribution was related to the 300 m resolution. Thus, different environment characteristics may have significant effects on changes in temperature when differences in spatial scale are taken into account.

Type
Articles
Information
Polar Record , Volume 43 , Issue 4 , October 2007 , pp. 353 - 359
Copyright
Copyright © Cambridge University Press 2007

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References

Barry, R.G. 1992. Mountain weather and climate. London: Routledge.Google Scholar
Bolstad, P.V., Swift, L., Collins, F., and Regniere, J.. 1998. Measured and predicted air temperatures at basin to regional scales in the southern Appalachian mountains. Agricultural and Forest Meteorology 91: 161–176.Google Scholar
Brossard, T., Elvebakk, A., Joly, D., and Nilsen, L.. 2002. Modelling Index of thermophily by means of a multi-sources data base on Brøgger Peninsula (Svalbard). International Journal of Remote Sensing 23: 4683–4698.CrossRefGoogle Scholar
Chapin, F.S., McFadden, J.P., and Hobbie, S.E.. 1997. The role of arctic vegetation in ecosystem and global processes. In: Woodin, S.J., and Marquiss, M. (editors). Ecology of arctic environments. Oxford: Blackwell Science Ltd: 97–112.Google Scholar
Chen, J., Saounders, S.C., Crow, T.R., Naimab, R.J., Brosofske, K.D., Mroz, G.D., Brookshire, B.L., and Franklin, J.F.. 1999. Microclimate in forest ecosystem and landscape ecology. Bioscience 49: 288–297.Google Scholar
Gardner, T.W., Sasowski, K.C., and Day, R.L.. 1990. Automated extraction of geomorphometric properties from digital elevation data. Zeitschrift für Geomorphologie 80: 57–68.Google Scholar
Hollister, R.D., Weber, P.J., and Bay, C.. 2004. Plant response to temperature in Northern Alaska: implications for predicting vegetation change. Ecology 86: 1562–1570.Google Scholar
Joly, D., Brossard, T., Dupont, G., Elvebakk, A., Fury, R., and Nilsen, L.. 1999. Localisation optimale de capteurs en vue de la modélisation des températures sur le piémont de deux glaciers au Spitsberg. Publication de l'Association Internationale de Climatologie 12: 460–467.Google Scholar
Joly, D., Nilsen, L., Fury, R., Elvebakk, A., and Brossard, T.. 2003. Temperature interpolation at a large scale; test on a small area in Svalbard. International Journal of Climatology 23: 1637–1654.Google Scholar
Lookingbill, T., and Urban, D.L.. 2003. Spatial estimation of air temperature differences for landscape-scale studies in mountain environments. Agricultural and Forest Meteorology 114: 141–151.Google Scholar
McGraw, J.B., and Fetcher, N.. 1992. Response of tundra plant populations to climatic change. In: Chapin, F. S., Jefferies, R., Reynolds, J., Shaver, G., Svoboda, J., and Chu, E. W. (editors). Arctic ecosystems in a changing climate: an ecophysiological perspective. New York: Academic Press Inc: 359–376.Google Scholar
Nilsen, L., Brossard, T., and Joly, D.. 1999. Mapping plant communities in a local arctic landscape applying a scanned infrared aerial photograph in a geographical information system. International Journal of Remote Sensing 20 (2): 463–480.CrossRefGoogle Scholar
Perrin de Brichambaut, C. 1978. Estimation de l'énergie solaire disponible au sol. La Météorologie 6 (15): 5–45.Google Scholar
Reynolds, J.F., and Leadley, P.W., 1992. Modelling the response of arctic plants to changing climate. In: Chapin, F. S., Jefferies, R., Reynolds, J., Shaver, G., Svoboda, J., and Chu, E. W. (editors). Arctic ecosystems in a changing climate: an ecophysiological perspective. New York: Academic Press Inc: 413–440.Google Scholar
Stephenson, N.L. 1990. Climatic controls on vegetation distribution: the role of the water balance. American Naturalist 135: 649–670.Google Scholar
Weider, L.J., and Hobaek, A.. 2000. Phylogeography and arctic biodiversity: a review. Annales Zoologici Fennici 37: 217–231.Google Scholar
Wilmott, C.J., and Robeson, S.M.. 1995. Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology 15: 221–229.CrossRefGoogle Scholar
Yeakley, J.A., Swank, W.T., Swift, L.W., Hornberger, G.M., and Shugart, H.H.. 1998, Soil moisture gradients and controls on a Appalachian hillslope from drought through recharge. Hydrology and Earth System Science 2: 41–49.Google Scholar