Abstract The Penman-Monteith equation is recommended for the estimation of reference evapotranspiration (ETo). However, it requires meteorological data that are commonly unavailable. Thus, this study evaluates artificial neural network (ANN), multivariate adaptive regression splines (MARS), and the original and calibrated Hargreaves-Samani (HS) and Penman-Monteith temperature (PMT) equations for the estimation of daily ETo using temperature. Two scenarios were considered: (i) local, models were calibrated/developed and evaluated using data from individual weather stations; (ii) regional, models were calibrated/developed using pooled data from several stations and evaluated independently in each one. Local models were also evaluated outside the calibration/training station. Data from 9 stations were used. The original PMT outperformed the original HS, but after local or regional calibrations, they performed similarly. The locally calibrated equations and the local machine learning models exhibited higher performances than their regional versions. However, the regional models had higher generalization capacity, with a more stable performance between stations. The machine learning models performed better than the equations evaluated. When comparing the ANN models with the HS equation, mean RMSE reduced from 0.96 to 0.87 and from 0.84 to 0.73, in regional and local scenarios, respectively. ANN and MARS performed similarly, with a slight advantage for ANN.

ABSTRACT Correct determinations of distribution (Ed) and application (EA) efficiencies allow adequate estimations of the gross irrigation depth. This study aimed: i) to determine the distribution efficiency using the Christiansen uniformity coefficient (CUC) in a sprinkler irrigation system under different weather conditions and working pressures; ii) to compare the mean, median, and cumulative CUC values; and iii) to evaluate the predictive capacity of four EA estimation models. CUC values were determined from 80 assessments, as well as the mean, median, and cumulative. The precipitated water depth accumulated in each collector was considered for the accumulated CUC. More uniform evaluations were used for EA (working pressure of 196 kPa), resulting in 20 samples. Besides being measured, EA was estimated by Keller & Bliesner, Playán, Tarjuelo, and Beskow methods. Statistical indicators were the root mean square error, mean bias error, Willmott agreement index, mean absolute error, and Pearson correlation coefficient. CUC values ranged from 66.51 to 92.04%, and the accumulated CUC provided an improvement over the isolated evaluations. The Beskow model had the best EA estimations in conventional spraying.

RESUMO O método de Penman-Monteith (PM-FAO) é considerado padrão para estimativa da evapotranspiração de referência (ET0), mas exige vários dados meteorológicos, que muitas vezes não estão disponíveis. O objetivo deste trabalho foi avaliar o desempenho do método de PM-FAO com dados meteorológicos limitados e de outros métodos como alternativas para estimativa da evapotranspiração de referência no município de Jaíba, Brasil. Foram utilizados dados meteorológicos diários de 2007 a 2016 da estação do Instituto Nacional de Meteorologia. Os valores de evapotranspiração de referência diários foram aleatorizados, sendo que, 70% desses foram utilizados para determinar os parâmetros de calibração da evapotranspiração de referência para as equações de cada método sob estudo. Os dados restantes foram utilizados para testar a calibração em relação ao método padrão. Na avaliação de desempenho foram utilizados os coeficientes de Willmott, de confiança e a raiz do erro quadrático médio. Quando houve ausência de um dado meteorológico, seja radiação solar, umidade relativa do ar ou velocidade do vento, ou na ausência simultânea de velocidade do vento e umidade relativa do ar o método de PM-FAO apresentou os melhores desempenhos, sendo assim recomendado para Jaíba. O método de PM-FAO com dois dados faltantes, sendo um deles a radiação solar, apresentou desempenho intermediário. Os métodos que utilizaram apenas dados de temperatura do ar não são recomendados para a região.

ABSTRACT FAO Penman-Monteith (FO-PM) is considered the standard method for the estimation of reference evapotranspiration (ET0) but requires various meteorological data, which are often not available. The objective of this work was to evaluate the performance of the FAO-PM method with limited meteorological data and other methods as alternatives to estimate ET0 in Jaíba-MG. The study used daily meteorological data from 2007 to 2016 of the National Institute of Meteorology’s station. Daily ET0 values were randomized, and 70% of these were used to determine the calibration parameters of the ET0 for the equations of each method under study. The remaining data were used to test the calibration against the standard method. Performance evaluation was based on Willmott’s index of agreement, confidence coefficient and root-mean-square error. When one meteorological variable was missing, either solar radiation, relative air humidity or wind speed, or in the simultaneous absence of wind speed and relative air humidity, the FAO-PM method showed the best performances and, therefore, was recommended for Jaíba. The FAO-PM method with two missing variables, one of them being solar radiation, showed intermediate performance. Methods that used only air temperature data are not recommended for the region.