Espacialização da temperatura do ar diária considerando a altitude

Abstract

Climate change has become a global concern. Each decade that passes is hotter than the previous one and the expectation is that this trend will continue due to the large amount of greenhouse gases released into the atmosphere. The study of air temperature is relevant and essential for climate studies and its application in environmental, forestry, agricultural sciences, among others. For studies of air temperature, hydroclimatic data are needed, which are obtained through data collected at meteorological stations. However, the number of stations is limited, in addition to their irregular distribution and scarce data. To obtain spatialized data, the interpolation of point data is generally used, however, as the weather stations are sparse, the distance between them may exceed the limit of the spatial dependence of the variables, generating inadequate results, especially when the topography effect is not considered. There is a difficulty in accurately interpolating air temperature data in mountainous terrains, due to the effect that atmospheric pressure has on temperature. Thus, including topographic information in the air temperature spatialization can improve the efficiency of the results. The objective of this work was to spatialize daily air temperature and evapotranspiration data, constituting a database corrected by the altitude effect. This work was carried out in the state of Espírito Santo, which has an altimetric amplitude of 2892 meters from the coastal regions to the highest point located in Serra do Caparaó. The meteorological data used in this study come from twenty-two meteorological stations belonging to INMET and a digital elevation model (MDE), obtained from the SRTM (Shuttle Radar Topography Mission). To achieve the proposed objectives, the Poisson equation and the adiabatic lapse rate were used to estimate the air temperature considering the topography effect. These methods were compared to the IDW interpolator to assess the gain obtained from including topographic information in the temperature estimate. The performance of the methods was evaluated by cross-validation, using the coefficient of determination (R²), the bias and the mean absolute error (MAE). The method that showed the best performance was used to spatialize the maximum, average and minimum air temperature data. These data were applied to the Hargreaves and Samani equation to obtain daily spatial data of reference evapotranspiration (ET0). The method that obtained the best performance in the spatialization of air temperature data was to adjust the measured data for the altitude conditions of the estimated point using the environmental lapse rate (-6.5 °C km-1), and then apply the IDW interpolator. The method provided a gain of 51% in the reduction of the MAE values, when compared with the IDW interpolator, without considering the topography effect. This method was used to spatialize the daily data of maximum, average and minimum air temperature for the entire territory of Espírito Santo with a spatial resolution of 500 meters. Thus, it was possible to generate a database without failures of maximum, average and minimum air temperature and ET0 corrected by the topography effect for the period from January 1, 2007 to February 28, 2020 (13.17 years, 4808 days) for the entire territory of Espírito Santo. These data will be made available for free download on the Internet.