Estimating Reference Evapotranspiration by Hargreaves and Blaney-Criddle Methods in Humid Subtropical Conditions

Hafeez, Muhammad; Chatha, Zia Ahmad; Khan, Alamgir Akhtar; Bakhsh, Allah; Basit, Abdul; Tahira, Fatima

doi:10.18488/journal.68.2020.71.15.22

Cited by 5 publications

(3 citation statements)

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“…So, alternate ET0 methods like Hargreaves, Blaney-Criddle, FAO-56 reduced set, and Turc methods are used which required only a few meteorological parameters. It was found from previous studies that the Hargreaves method was performed well in predicting the ET0 as compared to other ET0 methods like BC, FA0-56 reduced method (Fisher and Pringle 2013;Hafeez et al 2020). That's why, Hargreaves method was used in this study due to its accuracy over other methods and non-availability of the required dataset, and non-reliability of downscaling and projecting the meteorological parameters required for the Penman-Monteith method.…”

Section: Hargreaves Methodsmentioning

confidence: 88%

Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios

Maqsood

Farooque

Abbas

et al. 2022

Water Resour Manage

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Evapotranspiration, one of the major elements of the water cycle, is sensitive to climate change. The main objective of this study was to examine the response of reference evapotranspiration (ET0) under various climate change scenarios using artificial neural networks and a general circulation model (GCM) -the Canadian Earth System Model Second Generation (CanESM2). The Hargreaves method was used to calculate ET0 for western, central, and eastern parts of Prince Edward Island. The two input parameters of the Hargreaves method; daily maximum temperature (Tmax), and daily minimum temperature (Tmin) were projected using CanESM2. The Tmax and Tmin were downscaled with the help of statistical downscaling and simulation model (SDSM) for three future periods 2020s (2011-2040), 2050s (2041-2070), and 2080s (2071-2100) under three representative concentration pathways (RCP's) including RCP 2.6, RCP P4.5, and RCP 8.5, and the. Temporally, there were major changes in Tmax, Tmin, and ET0 for the 2080s under RCP8.5. The temporal variations in ET0 for all RCPs matched the reports in the literature for other similar locations and for RCP8.5 it ranged from 1.63 (2020s) to 2.29 mm/day (2080s). As a next step, a one-dimensional convolutional neural network (1D-CNN), long-short term memory (LSTM), and multilayer perceptron (MLP) were used for estimating ET0 due to the non-linear behavior of ET0 and the limited meteorological input data. High coefficient of correlation (r > 0.95) values for both calibration and validation periods showed the potential of the artificial neural networks in ET0 estimation. The results of this study will help decision makers and water resource managers to quantify the availability of water in future for the island and to optimize the use of island water resources on a sustainable basis.

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Section: Hargreaves Methodsmentioning

confidence: 88%

Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios

Maqsood

Farooque

Abbas

et al. 2022

Water Resour Manage

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“…(3) Compared to the RBF network [25], SVR-rbf provided a bit lower performance but outperformed three previous regression equations. (4) The soft computing models outperformed the regression-based models in the b-factor estimation since they gave the lower values of MARE (%), MXARE (%), NE > 2%, and DEV (%) and the higher value of r 2 . (5) Models' Applicability for estimating monthly reference evapotranspiration (ETo) revealed that the soft computing models outperformed the regression-based models in ETo estimation owing to the lower percentage of yearly difference.…”

Section: Discussionmentioning

confidence: 94%

“…The Blaney-Criddle method, as proposed by the Food and Agricultural Organization (FAO) of the United Nations, is a well-known temperature-based reference crop evapotranspiration. This method gives more advantage when having limitations of the measured data than the Penman-Monteith method, which requires many meteorological data [2][3][4]. Many attempts [5][6][7][8][9] have been made to evaluate the efficiency of the FAO Blaney-Criddle method in estimating reference crop evapotranspiration for many regions.…”

Section: Introductionmentioning

confidence: 99%

Estimating FAO Blaney-Criddle b-Factor Using Soft Computing Models

et al. 2022

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FAO Blaney-Criddle has been generally an accepted method for estimating reference crop evapotranspiration. In this regard, it is inevitable to estimate the b-factor provided by the Food and Agriculture Organization (FAO) of the United Nations Irrigation and Drainage Paper number 24. In this study, five soft computing methods, namely random forest (RF), M5 model tree (M5), support vector regression with the polynomial function (SVR-poly), support vector regression with radial basis function kernel (SVR-rbf), and random tree (RT), were adapted to estimate the b-factor. And Their performances were also compared. The suitable hyper-parameters for each soft computing method were investigated. Five statistical indices were deployed to evaluate their performance, i.e., the coefficient of determination (r2), the mean absolute relative error (MARE), the maximum absolute relative error (MXARE), the standard deviation of the absolute relative error (DEV), and the number of samples with an error greater than 2% (NE > 2%). Findings reveal that SVR-rbf gave the highest performance among five soft computing models, followed by the M5, RF, SVR-poly, and RT. The M5 also derived a new explicit equation for b estimation. SVR-rbf provided a bit lower efficacy than the radial basis function network but outperformed the regression equations. Models’ Applicability for estimating monthly reference evapotranspiration (ETo) was demonstrated.

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Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change

Youssef,

Peters,

El-Shirbeny

et al. 2024

Cogent Food & Agriculture

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Estimating Reference Evapotranspiration by Hargreaves and Blaney-Criddle Methods in Humid Subtropical Conditions

Cited by 5 publications

References 0 publications

Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios

Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios

Estimating FAO Blaney-Criddle b-Factor Using Soft Computing Models

Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change

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