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Modeling monthly mean air temperature for Brazil |
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| Authors: | Clayton Alcarde Alvares José Luiz Stape Paulo Cesar Sentelhas José Leonardo de Moraes Gonçalves |
| Institution: | 1. Forestry Science and Research Institute (IPEF) and Forest Productivity Cooperative (FPC), Comendador Pedro Morgante Avenue, 3500, 13415-000, Piracicaba, S?o Paulo, Brazil 2. Department of Forestry and Environmental Resources, North Carolina State University, 3108 Jordan Hall, Raleigh, NC, 27695-8008, USA 5. Forest Productivity Cooperative (FPC), 3108 Jordan Hall, Raleigh, NC, 27695-8008, USA 3. College of Agriculture “Luiz de Queiroz”, Department of Biosystems Engineering, University of Sao Paulo, Pádua Dias Avenue, 11. 09, 13418-900, Piracicaba, S?o Paulo, Brazil 4. College of Agriculture “Luiz de Queiroz”, Department of Forestry Sciences, University of Sao Paulo, Pádua Dias Avenue, 11. 09, 13418-900, Piracicaba, S?o Paulo, Brazil |
| Abstract: | Air temperature is one of the main weather variables influencing agriculture around the world. Its availability, however, is a concern, mainly in Brazil where the weather stations are more concentrated on the coastal regions of the country. Therefore, the present study had as an objective to develop models for estimating monthly and annual mean air temperature for the Brazilian territory using multiple regression and geographic information system techniques. Temperature data from 2,400 stations distributed across the Brazilian territory were used, 1,800 to develop the equations and 600 for validating them, as well as their geographical coordinates and altitude as independent variables for the models. A total of 39 models were developed, relating the dependent variables maximum, mean, and minimum air temperatures (monthly and annual) to the independent variables latitude, longitude, altitude, and their combinations. All regression models were statistically significant (α?≤?0.01). The monthly and annual temperature models presented determination coefficients between 0.54 and 0.96. We obtained an overall spatial correlation higher than 0.9 between the models proposed and the 16 major models already published for some Brazilian regions, considering a total of 3.67?×?108?pixels evaluated. Our national temperature models are recommended to predict air temperature in all Brazilian territories. |
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