Abstract:RESUMO-O modelo de Hargreaves e Samani é utilizado na estimativa da evapotranspiração de referência, sendo muito útil para o manejo da irrigação, sendo o mesmo considerado de uso prático, pois utiliza elementos meteorológicos de fácil obtenção como a temperatura. Entretanto, o mesmo necessita de calibração regional para estimar adequadamente a evapotranspiração. Objetivou-se calibrar o modelo de Hargreaves e Samani para o estado do Ceará, utilizando dados meteorológicos de doze cidades. Os parâmetros empíricos… Show more
“…If this calibration improves performance, an efficient and easy-to-use method is available. This calibration has been done by several researchers in many regions of Brazil: Goiás (Fernandes et al, 2012), Ceará (Lima Júnior et al, 2016), Pernambuco (Arraes et al, 2016) and Sete Lagoas -MG (Borges Júnior et al, 2017) and, of the world: Spain (Gavilán et al, 2006), Iran (Tabari & Zalaee, 2011), Bosnia and Herzegovina (Čadro et al, 2017) and Sichuan-China (Feng et al, 2017).…”
The Penman-Monteith method (PM-FAO) is recommended as standard for calculation of reference evapotranspiration (ETo). However, its use requires a series of meteorological variables that is not normally available, restricting its application in many locations. A solution to the problem of unavailability of meteorological data was presented in FAO Bulletin 56, which contains methodologies for estimating wind speed, solar radiation and relative humidity. The objective of the present study was to evaluate the performance of the PM-FAO methodologies for missing data and Hargreaves-Samani as alternatives to the PM-FAO standard method at different time scales and seasons for the municipalities of Linhares and São Mateus, located in the northern region of the state of Espírito Santo. The comparison was performed using linear regression parameters (β0 and β1), coefficient of determination, standard error of estimation (SEE) and coefficient of performance. The best alternative to the standard PM-FAO standard method for estimating ETo in the studied area was the Penman-Monteith method with missing wind speed data, since the R2 for this method always remained above 0.94 and the confidence coefficient was classified as great, for all seasons and scales. The Hargreaves-Samani method did not present satisfactory performance, with R2 below 0.7, regardless of the time scale and time of the year, and it yielded the greatest SEE (1.0 mm d-1) at spring on a two-day scale. Thus, its use in the northern region of the Espírito Santo state is not recommended.
“…If this calibration improves performance, an efficient and easy-to-use method is available. This calibration has been done by several researchers in many regions of Brazil: Goiás (Fernandes et al, 2012), Ceará (Lima Júnior et al, 2016), Pernambuco (Arraes et al, 2016) and Sete Lagoas -MG (Borges Júnior et al, 2017) and, of the world: Spain (Gavilán et al, 2006), Iran (Tabari & Zalaee, 2011), Bosnia and Herzegovina (Čadro et al, 2017) and Sichuan-China (Feng et al, 2017).…”
The Penman-Monteith method (PM-FAO) is recommended as standard for calculation of reference evapotranspiration (ETo). However, its use requires a series of meteorological variables that is not normally available, restricting its application in many locations. A solution to the problem of unavailability of meteorological data was presented in FAO Bulletin 56, which contains methodologies for estimating wind speed, solar radiation and relative humidity. The objective of the present study was to evaluate the performance of the PM-FAO methodologies for missing data and Hargreaves-Samani as alternatives to the PM-FAO standard method at different time scales and seasons for the municipalities of Linhares and São Mateus, located in the northern region of the state of Espírito Santo. The comparison was performed using linear regression parameters (β0 and β1), coefficient of determination, standard error of estimation (SEE) and coefficient of performance. The best alternative to the standard PM-FAO standard method for estimating ETo in the studied area was the Penman-Monteith method with missing wind speed data, since the R2 for this method always remained above 0.94 and the confidence coefficient was classified as great, for all seasons and scales. The Hargreaves-Samani method did not present satisfactory performance, with R2 below 0.7, regardless of the time scale and time of the year, and it yielded the greatest SEE (1.0 mm d-1) at spring on a two-day scale. Thus, its use in the northern region of the Espírito Santo state is not recommended.
“…After verification of the data homogeneity, the potential evapotranspiration (ETo) (mm d -1 ) for the 15 INMET stations was calculated using the equation proposed by [14], which considers the following variables: radiation at the top of the atmosphere, maximum, average and minimum daily temperature. This equation is an alternative for estimating potential evapotranspiration in sites with limited data availability, according to [15], and is expressed by the following equation: As the number of stations with precipitation data only (169) was much higher than the stations with data for the calculation of the potential evapotranspiration (15), the ANA rainfall series were grouped by INMET meteorological station. For this grouping, the Thiessen polygon method was used in order to obtain estimates of actual and maximum evapotranspiration for the 184 stations.…”
Cotton agroclimatic zoning is an essential tool to establish the most favorable periods for its cultivation, when the environmental conditions are more propitious, in order to reduce risks in agricultural activity. The objective of this work was to develop the zoning of the risk estimation of cotton yield reduction in the state of Mato Grosso, using the FAO method. Cultivars of early, medium and late cycles were considered, with four sowing dates (12/11, 12/21, 1/01 and 1/11) and three available water capacities (60, 140 and 200 mm). Results were specialized by ordinary kriging. The southernmost regions of the state presented the highest reduction risks, due to the lower precipitation in these areas. Sowing period 1 presented the lowest yield reduction risk, and the late-cycle cultivar in season 4 was the one that presented the highest reduction risk. Trough the validation of the obtained results, it can be considered that the methodology adopted in this work to verify the risk of yield decrease proved to be efficient.
“…O modelo de Hargreaves e Samani (HS) apresentou bons resultados em vários locais em que foi utilizado, principalmente quando efetuado ajuste dos seus parâmetros (BORGES; MENDIONDO, 2007;TALAEE, 2011;JÚNIOR et al, 2012;RAZIEI;PEREIRA, 2013;TABARI et al, 2013;LIMA JUNIOR et al, 2016).…”
RESUMOO objetivo deste trabalho foi ajustar o modelo de Hargreaves e Samani de estimativa da evapotranspiração de referência para as condições da cidade de Maceió, Alagoas. Foram utilizados dados meteorológicos de temperatura do ar, velocidade do vento, umidade relativa do ar e radiação solar global. Os ajustes realizados foram comparados com o modelo padrão Penman Monteith, utilizando os índices estatísticos erro padrão de estimativa, índice de Willmont e coeficiente de correlação (r), e o desempenho foi avaliado utilizando o índice "c". De acordo com os índices estatísticos o ajuste que obteve a melhor estimativa foi o com três parâmetros calibrados. Os ajustes de um e dois parâmetros apresentaram desempenho semelhante.Palavras-chave: temperatura do ar, Penman Monteith, radiação solar.
ADJUSTMENT OF PARAMETERS OF THE HARGREAVES AND SAMANI EQUATION FOR ESTIMATE OF THE REFERENCE EVAPOTRANSPIRATION AT DIARY SCALE FOR MACEIÓ-AL ABSTRACTThe objective of this work was to adjust the model of Hargreaves and Samani estimation of reference evapotranspiration for the conditions of the city of Maceio-AL. Were used meteorological data of air temperature, wind speed, relative humidity and solar radiation. The adjustments made were compared with the standard model Penman Monteith FAO 56, using statistical indices estimate of standard error (EPE), Willmont index, correlation coefficient (r), and the performance was evaluated using the "c" index. According to the statistical indices adjustment that got the best estimate was three calibrated parameters. The settings of one and two parameters showed similar performance.
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