Evapotranspiration is an important parameter for many projects related to climate characterization, hydrological modeling and water resources. This work was established as the first study in Rio Branco, eastern Acre, in order to derive empirical relations to estimate the reference evapotranspiration in the annual range from meteorological data readily available using the multiple linear regression analysis. Meteorological data of mean temperature (maximum and minimum), wind speed and insolation were obtainded from the National Institute of Meteorology for the period 1980-2014, which can be considered representative of the local climate. To estimate the reference evapotranspiration was used the Penman-Monteith-FAO, and multiple regression analysis was used as a selection process of significant variables for the model fit. Generated values by the proposed evapotranspiration models were compared to observed values for validation. Results indicated that the model with three variables (mean temperature, wind speed and insolation) satisfactorily estimated reference evapotranspiration for Rio Branco, AC, with great performance for annual data. Models with one variable (insolation) and two variables (mean temperature and insolation) showed less accuracy. However, they have advantage due to simplicity, since they can estimate the reference evapotranspiration from a few climatic parameters. From a practical point of view, these models can be regarded as a method to estimate the reference evapotranspiration when the input weather variables are insufficient to other methods.

The changes in climatic conditions can affect demand of water in regions because the evapotranspiration is affected by changes weather elements. The goal of this study was to identify possible trends in the reference evapotranspiration (ETo) to Acre State, with study units to Rio Branco locations (Acre’s capital), Tarauacá and Cruzeiro do Sul considering a period of 30 years, using monthly meteorological stations data. The methodology was adopted to achieve meteorological consistency data, in this way was made the gap filling in the time series by means of multivariate techniques. The trend analysis was performed using the Mann-Kendall nonparametric and your magnitude through the Theil-Sen test. The indirect method of Penmann-Montheith was used to calculate the ETo. It was found that there is upward trend in ETo at the autumn, winter and spring seasons and that became a significant through the years 1990, 1996 and 2001 to cities of Cruzeiro do Sul Tarauacá and Rio Branco, respectively. The average temperature datasets, minimum temperature, solar radiation and global solar radiation showed upward trend at 5% significance for all that three locations. These being the main responsible for the increase in ETo in these locations.

Sensitivity Analysis (SA) is important to understand the relative importance of climate variables in the reference evapotranspiration (ETo) computation. In this study, a sensitivity coefficient was used to predict ETo responses to disturbances of five climatic variables in the Amazonian Hydrographic Region - AHR (Brazilian Amazon). The ETo was estimated using the standardized equation of Penman-Monteith-FAO (PM-FAO). A 15-year meteorological data set of 38 surface meteorological stations were used in the study. An additional analysis was also presented to determine homogeneous regions of ETo by means of Cluster Analysis. The results showed that seven homogeneous sub-regions are sufficient to divide the AHR into different ETo patterns which were separated considering the intensity and the seasonal pattern of ETo. By the SA, the variables that contribute most to the computation of ETo using the PM-FAO method were the balance of radiation (Rn) and wind speed (u2). These results demonstrate that, in general, it should be emphasized to precise measures of insolation, since the precise estimation of Rn is directly associated with the measurement of this variable as well as of u2, which proved to be the second most influential variable in the ETo computation.