Rank-Ordering of Topographic Variables Correlated with Temperature

Affiliation(s)

Université de Franche-Comté, Besan?on, France.

Université de Bourgogne, Dijon, France.

University of Hamburg, Institute of Geophysics, Hamburg, Germany.

Université de Franche-Comté, Besan?on, France.

Université de Bourgogne, Dijon, France.

University of Hamburg, Institute of Geophysics, Hamburg, Germany.

ABSTRACT

Spatial variations in temperature may be ascribed to many variables. Among these, variables pertaining to topography are prominent. Thus various topographic variables were calculated from 50 m-resolution digital terrain models (DTMs) for three study areas in France and for Slovenia. The “classic” geomatic variables (altitude, aspect, gradient, etc.) are supplemented by the description of landforms (amplitude of humps and hollows). Special care is taken in managing collinearity among variables and building windows with different dimensions. Statistical processing involves linear regressions of daily temperatures taken as the response variables and six topographic variables (explanatory variables). Altitude accounts significantly for the spatial variation in temperatures in 90% of cases, except in the Gironde, a lowlying area (50%). The scale of landforms also appears to be highly correlated to the measured temperature. Variations in the frequency with which topographic descriptors account for temperatures are examined from several standpoints. Altitude is less frequently taken as an explanatory variable for spatial variation of temperatures in winter (75%) than in spring (80%) and late summer (85%). Minimum temperatures are influenced on average much more by the amplitude of humps and hollows (56%) than maximum temperatures (38%) are. The frequency with which these two landforms account for the spatial variation of temperature is reversed between the minima and maxima.

Spatial variations in temperature may be ascribed to many variables. Among these, variables pertaining to topography are prominent. Thus various topographic variables were calculated from 50 m-resolution digital terrain models (DTMs) for three study areas in France and for Slovenia. The “classic” geomatic variables (altitude, aspect, gradient, etc.) are supplemented by the description of landforms (amplitude of humps and hollows). Special care is taken in managing collinearity among variables and building windows with different dimensions. Statistical processing involves linear regressions of daily temperatures taken as the response variables and six topographic variables (explanatory variables). Altitude accounts significantly for the spatial variation in temperatures in 90% of cases, except in the Gironde, a lowlying area (50%). The scale of landforms also appears to be highly correlated to the measured temperature. Variations in the frequency with which topographic descriptors account for temperatures are examined from several standpoints. Altitude is less frequently taken as an explanatory variable for spatial variation of temperatures in winter (75%) than in spring (80%) and late summer (85%). Minimum temperatures are influenced on average much more by the amplitude of humps and hollows (56%) than maximum temperatures (38%) are. The frequency with which these two landforms account for the spatial variation of temperature is reversed between the minima and maxima.

Cite this paper

D. Joly, B. Bois and K. Zaksek, "Rank-Ordering of Topographic Variables Correlated with Temperature,"*Atmospheric and Climate Sciences*, Vol. 2 No. 2, 2012, pp. 139-147. doi: 10.4236/acs.2012.22015.

D. Joly, B. Bois and K. Zaksek, "Rank-Ordering of Topographic Variables Correlated with Temperature,"

References

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[15] T. Peuker and D. H. Douglas, “Detection of Surface-Specific Points by Local Parallel Processing of Discrete Terrain- Elevation Data,” Computer Graphics and Image Process- ing, Vol. 4, No. 4, 1975, pp. 375-387. doi:10.1016/0146-664X(75)90005-2

[16] T. Foucart, “Colinearity and Linear Regressions,” “Colinéarité et Régressions Linéaire, ” Mathématiques et Sciences Humaines (Mathematics and Social Sciences), Vol. 173, No. 1, 2006, pp. 5-25.

[17] D. Belsey, E. Kuhne and R. Welsch, “Regression Diagnostics: Identifying Influential Data and Sources of Collinearity,” Wiley, New York, 1980. doi:10.1002/0471725153

[18] L. D. Schroeder, D. L. Sjoquist and P.E. Stephan, “Understanding Regression Analysis,” Sage Publications, Beverly Hills, 1986.

[19] P. Bressoux, “Modélisation Statistique Appliquée aux Sciences Sociales,” In: Méthodes en Sciences Humaines, de Boeck Université, Bruxelles, 2008.

[20] A. Hufty, “Introduction à la Climatologie,” de Boeck Université, Bruxelles, 2001, p. 544.

[1] L. Prihodko and S. N. Goward, “Estimation of Air Temperature from Remotely Sensed Surface Observations,” Remote Sensing of Environment, Vol. 60, No. 3, 1997, pp. 335-346. doi:10.1016/S0034-4257(96)00216-7

[2] K. Czajkowski, S. N. Goward, S. Stadler and A. Walz, “Thermal Remote Sensing of Near Surface Environmental Variables: Application over the Oklahoma Mesonet,” The Professional Geographer, Vol. 52, No. 2, 2000, pp. 345- 357. doi:10.1111/0033-0124.00230

[3] J. D. Jang, A.A. Viau and F. Anctil, “Neural Network Estimation of Air Temperatures from AVHRR Data,” International Journal of Remote Sensing, Vol. 25, No. 21, 2004, pp. 4541-4554. doi:10.1080/01431160310001657533

[4] S. Stisen, I. Sandholt, A. N?rgaard, R. Fensholt and L. Eklundh, “Estimation of Diurnal Air Temperature Using MSG SEVIRI Data in West Africa,” Remote Sensing of Environment, Vol. 110, No. 2, 2007, pp. 262-274. doi:10.1016/j.rse.2007.02.025

[5] K. Zak?ek and M. Schroedter-Homscheidt, “Parameterization of Air Temperature in High Temporal and Spatial Resolution from a Combination of the SEVIRI and MODIS Instruments,” ISPRS Journal of Photogrammetry and Re- mote Sensing, Vol. 64, No. 4, 2009, pp. 414-421. doi:10.1016/j.isprsjprs.2009.02.006

[6] J.A. Hevesi, J.D. Istok and A.L. Flint, “Precipitation Estimation in Mountainous Terrain Using Multivariate Geosatistics. Part I: Structural Analysis,” Journal of Applied Meteorology, Vol. 31, No. 7, 1992, pp. 661-676.

[7] C. H. Jarvis and N. Stuart, “A Comparison among Strategies for Interpolating Maximum and Minimum Daily Air Temperatures. Part I: The Selection of “Guiding” Topo- graphic and Land Cover Variables,” Journal of Applied Meteorology, Vol. 40, 2001, pp. 1060-1074.

[8] D. Joly, L. Nilsen, R. Fury, A. Elvebakk and T. Brossard, “Temperature Interpolation at a Large Scale: Test on a Small Area in Svalbard,” International Journal of Climatology, Vol. 23, No.13, 2003, pp. 1637-1654. doi:10.1002/joc.949

[9] D. Joly, “Spatial Analysis, Cartography, and Climate. Geographical Information and Climatology,” In: P. Carrega, Ed., Geographical Information and Climatology, ISTE-Wiley, London, 2010, pp. 29-71.

[10] D. Joly, T. Brossard, H. Cardot, J. Cavailhès, M. Hilal and P. Wavresky, “Types of Climates on Continental France, a Spatial Construction,” Cybergeo European Journal of Geography, Paper 501, 2010. doi:10.4000/cybergeo.23155 http//cybergeo.revues.org/index23155.html

[11] D. Joly, T. Brossard, H. Cardot, J. Cavailhès, M. Hilal and P. Wavresky, “Temperature Interpolation by Local Information; The Example of France,” International Journal of Climatology, Vol. 31, No. 14, 2011, pp. 2141-2153. doi:10.1002/joc.2220

[12] D. Joly and T. Brossard, “Contribution of Environment Variables to the Temperature Distribution at Different Resolution Levels on the Forefield of the Loven glaciers, Svalbard,” Polar Record, Vol. 43, No. 227, 2007, pp. 353-359.

[13] R. Geiger, H. Aron and P. Todhunter, “The Climate Near the Ground,” 6th Edition, Rowman & Littlefield Publishers Inc., Lanham, 2003.

[14] C. D. Whiteman, T. Haiden, B. Pospichal, S. Eisenbach and R. Steinacker, “Minimum Temperatures, Diurnal Temperature Ranges, and Temperature Inversions in Limestone Sinkholes of Different Sizes and Shapes,” Journal of App- lied Meteorology, Vol. 43, No. 8, 2004, pp. 1224-1236. doi:10.1175/1520-0450(2004)043<1224:MTDTRA>2.0.CO;2

[15] T. Peuker and D. H. Douglas, “Detection of Surface-Specific Points by Local Parallel Processing of Discrete Terrain- Elevation Data,” Computer Graphics and Image Process- ing, Vol. 4, No. 4, 1975, pp. 375-387. doi:10.1016/0146-664X(75)90005-2

[16] T. Foucart, “Colinearity and Linear Regressions,” “Colinéarité et Régressions Linéaire, ” Mathématiques et Sciences Humaines (Mathematics and Social Sciences), Vol. 173, No. 1, 2006, pp. 5-25.

[17] D. Belsey, E. Kuhne and R. Welsch, “Regression Diagnostics: Identifying Influential Data and Sources of Collinearity,” Wiley, New York, 1980. doi:10.1002/0471725153

[18] L. D. Schroeder, D. L. Sjoquist and P.E. Stephan, “Understanding Regression Analysis,” Sage Publications, Beverly Hills, 1986.

[19] P. Bressoux, “Modélisation Statistique Appliquée aux Sciences Sociales,” In: Méthodes en Sciences Humaines, de Boeck Université, Bruxelles, 2008.

[20] A. Hufty, “Introduction à la Climatologie,” de Boeck Université, Bruxelles, 2001, p. 544.