Calibration of Hargreaves–Samani and Priestley–Taylor equations in estimating reference evapotranspiration in the Northwest of Iran

Mehdizadeh, Saeid; Saadatnejadgharahassanlou, Hamid; Behmanesh, Javad

doi:10.1080/03650340.2016.1249474

Cited by 18 publications

(7 citation statements)

References 17 publications

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“…Evapotranspiration (ET) is an important component of the hydrologic cycle. An accurate estimation of ET is required for many applications, such as optimal water resources management, irrigation planning, determination of irrigation intervals, design of irrigation systems, agricultural water management, and studies related to water balance at each area [1][2][3][4][5][6]. Lysimeters are commonly applied to directly measure the ET; however, measuring it with this method is costly and requires considerable time, making it difficult to use in many areas.…”

Section: Introductionmentioning

confidence: 99%

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Mehdizadeh

Mohammadi

Pham

et al. 2021

Water

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Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were developed for estimating daily ETo. Two optimization algorithms, the shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO), were coupled on an adaptive neuro-fuzzy inference system (ANFIS) to develop and implement the two novel hybrid models (ANFIS-SFLA and ANFIS-IWO). Additionally, four empirical models with varying complexities, including Hargreaves–Samani, Romanenko, Priestley–Taylor, and Valiantzas, were used and compared with the developed hybrid models. The performance of all investigated models was evaluated using the ETo estimates with the FAO-56 recommended method as a benchmark, as well as multiple statistical indicators including root-mean-square error (RMSE), relative RMSE (RRMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). All models were tested in Tabriz and Shiraz, Iran as the two studied sites. Evaluation results showed that the developed coupled models yielded better results than the classic ANFIS, with the ANFIS-SFLA outperforming the ANFIS-IWO. Among empirical models, generally the Valiantzas model in its original and calibrated versions presented the best performance. In terms of model complexity (the number of predictors), the model performance was obviously enhanced by an increasing number of predictors. The most accurate estimates of the daily ETo for the study sites were achieved via the hybrid ANFIS-SFLA models using full predictors, with RMSE within 0.15 mm day−1, RRMSE within 4%, MAE within 0.11 mm day−1, and both a high R2 and NSE of 0.99 in the test phase at the two studied sites.

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Section: Introductionmentioning

confidence: 99%

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Mehdizadeh

Mohammadi

Pham

et al. 2021

Water

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“…Due to lack of access to Rs and VPD, the FAO method was used (Gholipoor 2008). Daily values of Rs were obtained from Hargreaves and Samani's equation (Mehdizadeh et al 2017) and the modified Allen et al (2006) equation. Solar radiation reaching the land surface (Rn, MJ m -2 d -1 ) was first measured above the Earth's atmosphere for each day of the year based on latitude and longitude and the solar constant (Allen et al 2006).…”

Section: Empirical Etref Equationsmentioning

confidence: 99%

Calibration of empirical equations for estimating reference evapotranspiration in different climates of Iran

Sharafi

Ghaleni

2021

Theor Appl Climatol

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The accurate estimation of reference evapotranspiration (ETref) is a crucial component for modeling hydrological and ecological cycles. The goal of this study was the calibration of 32 empirical equations used to determine ETref in the three classes of temperature-based, solar radiation-based and mass transfer-based evapotranspiration. The calibration was based on measurements taken between the years 1990 and 2019 at 41 synoptic stations located in very dry, dry, semidry and humid climates of Iran. The performance of the original and calibrated empirical equations compared to the PM-FAO56 equation was evaluated based on model evaluation techniques including: the coefficient of determination (R 2 ), the root mean square error (RMSE), the average percentage error (APE), the mean bias error (MBE), the index of agreement (D) and the scatter index (SI). The results show that the calibrated Baier and Robertson equation for temperature-based models, the Makkink equation for solar radiation-based models and the Penman equation for mass transfer-based models performed better than the original empirical equations. The calibrated

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“…Therefore, it appears reasonably to substitute it by other ETo methods which require small number of weather parameters [13]. The accuracy of a particular ETo method depends greatly on the climatic situations of the research area [14]. For humid subtropical weather climatic conditions PM ETo method is commonly suggested [15,16].…”

Section: Introductionmentioning

confidence: 99%

Penman and Thornwait Equations for Estimating Reference Evapotranspiration Under Semi-Arid Environment

Hafeez

Bakhsh

Basit

et al. 2020

Current Research in Agricultural Sciences

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The estimation of reference evapotranspiration (ETo) is required for effective development and management of agriculture water systems. In order to define the most accurate method to estimate ETo in semi-arid climatic environment of Faisalabad, Lahore and Peshawar. Penman and Thornwait ETo methods are compared with standard Penman-Monteith (PM) ETo method. The statistical results show that Penman ETo method overestimates PM ETo method in all the semi-arid climatic regions of Faisalabad, Lahore and Peshawar by 34.91%, 39.51% and 30.75%, respectively. The R2 were 0.98, 0.98 and 0.99 at Faisalabad, Lahore and Peshawar weather stations, respectively. The RMSE were 2.47 mm/day, 2.64 mm/day and 2.19 mm/day at Faisalabad, Lahore and Peshawar weather station, respectively. The MBE of -2.41 mm/day, -2.58 mm/day and -2.13 mm/day were noted at Faisalabad, Lahore and Peshawar weather stations, respectively. The statistical result of Thornwait (Th) ETo method as compared with PM ETo method indicates underestimation of ETo in winter season and overestimation of ETo in summer season by 13.81%, 22.43% and 14.54% at Faisalabad, Lahore and Peshawar stations, respectively. The R2 of 0.92, 0.89 and 0.95 were noted at Faisalabad, Lahore and Peshawar weather stations, respectively. The RMSE were 2.14 mm/day, 2.36 mm/day and 1.16 mm/day at Faisalabad, Lahore and Peshawar weather stations, respectively. The MBE were -0.68 mm/day, -1.12 mm/day and 0.61 mm/day at Faisalabad, Lahore and Peshawar weather stations, respectively. Overall, Thornwait method gave better estimation of ETo than Penman ETo method at all the weather stations. Contribution/Originality:The main objective of this research is to compare the performance of Penman and Thornwait ETo methods against standard PM ETo method under semi-arid climatic conditions of Lahore, Faisalabad and Peshawar, Pakistan.

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Calibration of Hargreaves–Samani and Priestley–Taylor equations in estimating reference evapotranspiration in the Northwest of Iran

Cited by 18 publications

References 17 publications

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Calibration of empirical equations for estimating reference evapotranspiration in different climates of Iran

Penman and Thornwait Equations for Estimating Reference Evapotranspiration Under Semi-Arid Environment

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