Objective air temperature mapping for the Iberian Peninsula using spatial interpolation and GIS

Abstract

Abstract:This study presents an objective mapping of monthly mean air temperature over the Iberian Peninsula using the spatial interpolation of climatological data. The research focuses on an interpolation method (multiple regression with residual correction) that combines statistical global analysis with a local interpolation (splines and inverse distance weighting). Geographical information (the independent variables) is used to predict air temperature (the dependent variable) through the regression relationship and to test several approaches. A comprehensive GIS implementation makes it possible to address many cartographic issues and to produce mean minimum, mean and mean maximum air temperature surfaces for the entire Iberian Peninsula. The spatial resolution of the maps is 200 m and their temporal resolution is monthly and annual. An associated error level obtained through validation tests with independent data is computed for each map. These validation tests show that the best results have an average R 2 (mean of all months) of 0.91 (mean temperature), 0.87 (mean maximum temperature) and 0.83 (mean minimum temperature). When the objective is to map a large area, best results are obtained when the model uses all stations of the Iberian Peninsula together (general peninsular model) and not when using different local subsets (hydrographical basins regional models). However, when the objective is mapping a regional area, basin models produce better outcomes than the general model being applied to these regional areas.

Comparison of regression techniques for mapping fog frequency: application to the Aragón region (northeast Spain)

“…To the best of our knowledge, this is the first approach in the scientific literature to objectively map this climatic variable. We demonstrated that the local and spatially complex nature of fogs makes them more difficult to map than other climatic variables such as precipitation and temperature for which it is common to obtain high R 2 values for regression models, usually higher than 0.7 (Ninyerola et al, 2007a(Ninyerola et al, , 2007b. R 2 values higher than 0.7 have been obtained previously for the Aragón region in monthly regression models of precipitation and temperature (Vicente-Serrano et al, 2003;Vicente-Serrano et al, 2007;López-Moreno et al, 2007).…”

“…Many previous studies have mapped climate parameters such as precipitation (Ninyerola et al, 2000(Ninyerola et al, , 2007aBrown and Comrie, 2002), temperature (Vicente-Serrano et al, 2003;Ninyerola et al, 2007b), evapotranspiration (Martínez-Cob, 1996;Vicente-Serrano et al, 2007), and snow depth (López- Moreno and Nogués-Bravo, 2005). The climate variables that have received the greatest attention are precipitation and temperature, as maps of these variables are of great interest for numerous applications.…”

Climate change impacts on body size and food web structure on mountain ecosystems

“…A climatic dataset was obtained by modelling climatic surfaces from discrete climatic data from the Spanish weather-monitoring system [31][32][33]. Climatic variables included temperature (8C), precipitation (mm) and the annual and summer precipitation to potential evapotranspiration (PPET) ratio [34].…”

Comparison of different procedures to map reference evapotranspiration using geographical information systems and regression‐based techniques

“…A number of studies have demonstrated the vast potential of these techniques in mapping different climate variables, as the method provides significant improvements over results from local and geostatistical methods (e.g. Agnew and Palutikof, 2000;Ninyerola et al, 2000Ninyerola et al, , 2006Daly et al, 2002Daly et al, , 2003Brown and Comrie, 2002;Hong et al, 2005;Ustrnul and Czekierda, 2005;Perry and Hollis, 2005;Beguería and VicenteSerrano, 2006;López-Moreno and Nogués-Bravo, 2005). However, no attempt has been made to map ET o using these techniques.…”