Publicação
Modelling air temperature for the state of São Paulo, Brazil
| Resumo: | Spatial modelling of air temperature (maximum, mean and minimum) of the State of São Paulo (Brazil) was calculated by multiple regression analysis and ordinary kriging. Climatic data (mean values of five or more years) were obtained from 256 meteorological stations distributed uniformly over the State. The correlation between the climatic dependent variables, with latitude and altitude as independent variables was significant and could explain most of the spatial variability. The coefficients of determination (P < 0.05) varied in the range of 0.924 and 0.953, showing that multiple regression analysis is an accurate method for the modelling of air temperature for the State of São Paulo. Finally, these regression equations were used together with the kriged maps of the residual errors to build 15 digital maps of air temperature using a 0.5 km² Digital Elevation Model in a Geographic Information System. |
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| Autores principais: | Rodríguez-Lado,Luis |
| Outros Autores: | Sparovek,Gerd; Vidal-Torrado,Pablo; Dourado-Neto,Durval; Macías-Vázquez,Felipe |
| Assunto: | DEM GIS multiple regression analysis kriging climate modelling |
| Ano: | 2007 |
| País: | Brasil |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | IBICT |
| Idioma: | inglês |
| Origem: | Oasisbr |
| Resumo: | Spatial modelling of air temperature (maximum, mean and minimum) of the State of São Paulo (Brazil) was calculated by multiple regression analysis and ordinary kriging. Climatic data (mean values of five or more years) were obtained from 256 meteorological stations distributed uniformly over the State. The correlation between the climatic dependent variables, with latitude and altitude as independent variables was significant and could explain most of the spatial variability. The coefficients of determination (P < 0.05) varied in the range of 0.924 and 0.953, showing that multiple regression analysis is an accurate method for the modelling of air temperature for the State of São Paulo. Finally, these regression equations were used together with the kriged maps of the residual errors to build 15 digital maps of air temperature using a 0.5 km² Digital Elevation Model in a Geographic Information System. |
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