Equação De Hargreaves-Samani Calibrada Em Diferentes Bases Temporais Para Sete Lagoas, Mg

Júnior, João C. F. Borges; Oliveira, Aline Lílian Marques; Andrade, C. de L. T. de; Pinheiro, Marcus André Braido

doi:10.13083/reveng.v25i1.742

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The obtaining of improvements after the calibration of empirical methods was also reported by several authors, such as Patel et al (2015), Djaman et al (2016) andBorges Júnior et al (2017). Thus, calibration is an important tool to improve the performance of ET o estimation models, adapting them to local climatic conditions.…”

Section: Resultsmentioning

confidence: 59%

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Ferreira¹,

Duarte²,

Araújo³

et al. 2018

Biosci. J.

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ABSTRACT:The reference evapotranspiration (ET o ) is an important component for determining the water requirements of the crops. In order to estimate this variable accurately, the Food and Agriculture Organization (FAO) proposed the Penman-Monteith equation, however, this demands a large number of meteorological data, which restricts its use. In this context, this study compares the performance of the Penman-Monteith equation using only measured air temperature (PMT) and the Hargreaves-Samani (HS) equation with the performance of the multivariate adaptive regression splines (MARS) technique for the daily ET o estimation with only air temperature data. For the study, daily meteorological data from 2002 to 2016 were used. The data were collected from weather stations located in Florianópolis-SC, Manaus-AM and Petrolina-PE, being these selected in order to capture different climatic conditions. MARS models were developed for each weather station and the PMT e HS equations were locally calibrated. The performances of the original and calibrated equations and MARS models were evaluated based on the statistical indices root mean square error, mean absolute error, mean bias error and coefficient of determination. The ET o estimated by the Penman-Monteith method with full data was used as reference for the development of the MARS models, calibration of the equations and for the performance evaluation of the models under study. The calibration of the HS and PMT equations promoted better performances in relation to the original equations, improving the methods accuracy. The MARS technique presented good performance, outperforming the original and calibrated PMT and HS equations, with lower error values and higher coefficient of determination, and can be considered as an alternative to empirical methods.

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Section: Resultsmentioning

confidence: 59%

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Ferreira¹,

Duarte²,

Araújo³

et al. 2018

Biosci. J.

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show abstract

“…From the evaluation of the performance index (PI) and Willmott's index of agreement (d) it was observed that all calibrations under study promoted an increasing of these indexes, with exception only for Paracatu with calibration A, where respectively values equal and less than those found for the not calibrated equation were obtained. It should be noted that, in most literature records, local calibration, performed in several ways, promotes better performances (Cobaner, 2011;Arraes et al, 2016;Borges Júnior et al, 2017 Table 5: Statistical indexes and classification for the Hargreaves-Samani equation not calibrated (HS) and calibrated by linear regression (Lin), adjustment of parameter "A" (A), adjustment of parameters "A" and "C" (AC), adjustment of parameters "A", "B" and "C" (ABC) and adjustment of parameters "A", "B" and "C" followed by calibration by linear regression (ABC+Lin).…”

Section: Resultsmentioning

confidence: 99%

“…Thus, to better estimate ET o , the local calibration is recommended (Borges Júnior et al, 2012;Feng et al, 2017). In Brazil, several authors calibrated HS equation for different locations (Arraes et al, 2016;Lima Junior et al, 2016;Borges Júnior et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Calibration methods for the Hargreaves-Samani equation

et al. 2018

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The estimation of the reference evapotranspiration is an important factor for hydrological studies, design and management of irrigation systems, among others. The Penman Monteith equation presents high precision and accuracy in the estimation of this variable. However, its use becomes limited due to the large number of required meteorological data. In this context, the Hargreaves-Samani equation could be used as alternative, although, for a better performance a local calibration is required. Thus, the aim was to compare the calibration process of the Hargreaves-Samani equation by linear regression, by adjustment of the coefficients (A and B) and exponent (C) of the equation and by combinations of the two previous alternatives. Daily data from 6 weather stations, located in the state of Minas Gerais, from the period 1997 to 2016 were used. The calibration of the Hargreaves-Samani equation was performed in five ways: calibration by linear regression, adjustment of parameter “A”, adjustment of parameters “A” and “C”, adjustment of parameters “A”, “B” and “C” and adjustment of parameters “A”, “B” and “C” followed by calibration by linear regression. The performances of the models were evaluated based on the statistical indicators mean absolute error, mean bias error, Willmott’s index of agreement, correlation coefficient and performance index. All the studied methodologies promoted better estimations of reference evapotranspiration. The simultaneous adjustment of the empirical parameters “A”, “B” and “C” was the best alternative for calibration of the Hargreaves-Samani equation.

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“…This empirical method is notable for its wide use and is suggested by Allen et al (1998) and Pereira et al (2015) as an alternative to FAO-PM when only air temperature data are available. Due to the empirical nature of this method, there is a need for local calibration against the standard method with a view to gaining accuracy, even using machine learning techniques (Borges Júnior et al, 2017, Shiri et al, 2014, Shiri et al, 2015.…”

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confidence: 99%

Reference evapotranspiration based on temperature in Minas Gerais state, Brazil

et al. 2019

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In the last decades, the areas of irrigated crops in the state of Minas Gerais have grown considerably, contributing to increase agricultural production in the country. Among the techniques of irrigation management, the determination of the water consumption by the plants is based on the reference evapotranspiration (ET0), the objective of this work was to evaluate and calibrate the Hargreaves-Samani (HS) method and a Proposed Method (MP) against the standard Penman-Monteith-FAO method (PM-FAO) for the state of Minas Gerais. For that, data from 52 conventional meteorological stations of the National Institute of Meteorology (INMET) were used. ET0 was estimated by the methods cited including the Hargreaves-Samani calibrated method (HScal) based on the mean absolute error (MAE). The analysis of the methods was carried out using the following statistical indicators: root mean square error (RMSE), systematic root mean square error (RMSEs), unsystematic root mean square error (RMSEun), the proportions of systematic and unsystematic mean square error, Willmott’s concordance index (d), confidence index (C) and Pearson correlation coefficient (r). It was verified that after the calibration, the HScal method obtained a superior performance to the original HS method in all the studied stations. It was also observed a superior MP performance with respect to the HScal method, in all stations studied.

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Equação De Hargreaves-Samani Calibrada Em Diferentes Bases Temporais Para Sete Lagoas, Mg

Cited by 8 publications

References 8 publications

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Calibration methods for the Hargreaves-Samani equation

Reference evapotranspiration based on temperature in Minas Gerais state, Brazil

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