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 R² (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. Copyright © 2007 Royal Meteorological Society

This study proposes a methodology to compute solar radiation values on the Earth surface on a monthly and annual basis and taking into account the relief, which is not an usual approach although it has enormous importance for environmental studies. It consists of two steps: a computational, physically‐based model (potential solar radiation) and a refining through meteorological data (interpolated solar radiation). The potential solar radiation model allows computing the incident energy assuming uniform atmospheric standard optical density. The only information required is a Digital Elevation Model (DEM, 180 m resolution in our case) and a number of meteorological stations providing global solar radiation data (74 in our case). This model, implemented into the MiraMon Geographic Information System (GIS), takes into account astronomical, atmospheric and geographical factors. Atmospheric effects are considered through correctors obtained by comparing the potential values with the observed at the meteorological stations. The resulting monthly correctors have been spatially interpolated to build up anomaly maps, which allow to obtain a refined solar radiation cartography. The potential model has proven to be very precise as it gives results close to the interpolated radiation values, allowing a conversion to interpolated solar radiation maps with errors of 7.3% (March), 6.1% (June), 6.4% (September) and 13.1% (December) computed from a leave‐one‐out cross‐validation. These results are always better than those obtained by classical interpolators, and still better if the process is run on the rugged terrain areas. A sensitivity analysis estimates the influence of the DEM quality on the model, concluding that an RMS error of 8 m in the DEM heights represents an RMS error of 133 kJ·m⁻²·day⁻¹(mean monthly) on the results. Finally, we have applied the model to obtain the 12‐monthly (based on daily average) global solar radiation maps of Catalonia (NE of Iberian Peninsula) as well as the annual one. Copyright © 2008 Royal Meteorological Society

Month-to-month air temperature (T_air) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month T_air mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical T_air modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum T_air, but also for maximum T_air. The improvement achieved by the inclusion of remote sensing LST products was higher for minimum T_air (up to 0.35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data.

Abies alba Mill. (European silver fir) and Fagus sylvatica L. (beech) are Eurosiberian species dispersed over the Iberian Peninsula. Climate change predictions indicate a rise in temperature and a decrease in precipitation in this region, threatening the future existence of these species. In the present study we analyzed the future topo-climatic suitability of Abies alba and Fagus sylvatica and the mixed forests of these two species, using the General Linear Models technique and data from the third National Forest Inventory (Ministerio de Agricultura PyA, 2007). We considered two modeling approaches based on niche theory: modeling community (Abieti-Fagetum) and overlapping individual species models. General trends showed an overall decrease in both species’ topo-climatic suitability and indicated that the Pyrenees will play a crucial role as a climatic refuge. The modeling approaches markedly differed, however, in their current and future spatial agreement. Despite good accuracy results, community modeling through co-occurrence does not encompass the environmental space of individual species prejudicing future assessments in new environmental situations, suggesting a need for future studies in community modeling.