Influence of the Estimated Global Solar Radiation on the Reference Evapotranspiration Obtained Through the Penman-Monteith Fao 56 Method

Cunha, Angélica Carvalho; Filho, Luís Roberto Almeida Gabriel; Tanaka, Adriana Aki; Góes, Bruno César; Putti, Fernando Ferrari

doi:10.1016/j.agwat.2020.106491

Cited by 18 publications

(9 citation statements)

References 19 publications

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“…In general, Volterra-Kolmogorov-Gabor (VKG) polynomial can be used to model complex systems that include a set of several input variables and one target variable: 6 Complexity…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

“…Several numerical models such as Hargreaves-Samani, Blaney-Criddle, ornthwaite, and FAO-56 Penman-Monteith (FAO-56 PM) have been introduced for this purpose, among which the World Meteorological Organization (WMO) model FAO-56 PM can be recommended as the most suitable and a reliable alternative to lysimeter data [3]. Outputs of the FAO-56 PM method are now completely acceptable to be considered as observational ET0 rates in ET0 modeling and generally ET0 related studies [4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH

2022

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Evapotranspiration represents the water requirement of plants during their growing season, and its accurate measurement at the farm is essential for agricultural water planners and managers. Field measurements of evapotranspiration have always been associated with many difficulties that have led researchers to seek a way to remotely measure this component in horticultural and agricultural areas. This study aims to investigate an indirect approach for daily rice crop evapotranspiration (ETc) measurement by machine learning (ML) techniques and the least available climatic variables. For this purpose, daily meteorological variables were obtained from three ground meteorological stations in rice cultivation regions of northern Iran during 2003–2016. The ETc rates were calculated by seven meteorological variables, the FAO-56 Penman-Monteith equation, and the regional calibrated rice crop coefficient and considered as the reference data. The MLs, including Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), and Group Method of Data Handling (GMDH), were utilized for ETc modeling. Different input combinations were applied, based on the use of minimum effective variables as input. Results showed that the models showed the most accurate performances in the input combination of four climatic variables: sunshine duration, maximum temperature, relative humidity, and wind speed. Investigating the accuracy of models in rice growth phases showed that the least estimation error belonged to the initial growing stage, which increased during the mid-season and late-season growing stages. A comparison of the models showed that the GMDH model performed better against the other competitors. For this model, both the Nash-Sutcliffe (NS) coefficient and R2 were greater than 0.98, and the Root Mean Square Error (RMSE) ranged between 0.214 and 0.234 mm per day in all stations. The current approach showed promising results in rice evapotranspiration modeling by only four common meteorological variables and can be reliably applied for indirect measurement of this variable over the rice farms. The studied approach will have research value for rice and other crops in similar/different climatic conditions.

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“…In general, Volterra-Kolmogorov-Gabor (VKG) polynomial can be used to model complex systems that include a set of several input variables and one target variable: 6 Complexity…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH

2022

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“…This is the first research by optimization algorithms to calibrate the A–P model coefficients in Iran. Through these algorithms, the A–P model coefficients have calibrated faster and more accurately, and R s is a fundamental input for calculating ET 38 , have estimated more correctly. Accurate estimation of R s provides an accurate calculation of ET.…”

Section: Introductionmentioning

confidence: 99%

Calibration and validation of the Angstrom–Prescott model in solar radiation estimation using optimization algorithms

Dehkordi

Bakhtiari

Qaderi

et al. 2022

Sci Rep

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The Angstrom–Prescott (A–P) model is widely suggested for estimating solar radiation (Rs) in areas without measured or deficiency of data. The aim of this research was calibration and validation of the coefficients of the A–P model at six meteorological stations across arid and semi-arid regions of Iran. This model has improved by adding the air temperature and relative humidity terms. Besides, the coefficients of the A–P model and improved models have calibrated using some optimization algorithms including Harmony Search (HS) and Shuffled Complex Evolution (SCE). Performance indices, i.e., Root Mean Square Error (RMSE), Mean Bias Error, and coefficient of determination (R2) have used to analyze the models ability in estimating Rs. The results indicated that the performance of the A–P model had more precision and less error than improved models in all the stations. In addition, the best results have obtained for the A–P model with the SCE algorithm. The RMSE varies between 0.82 and 2.67 MJ m−2 day−1 for the A–P model with the SCE algorithm in the calibration phase. In the SCE algorithm, the values of RMSE had decreased about 4% and 7% for Mashhad and Kerman stations in the calibration phase compared to the HS algorithm, respectively.

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“…A estimativa de ETc utilizando equações empíricas, com ajustes para representar a demanda evapotranspirativa local, é uma alternativa para obtenção de informações confiáveis e pode oferecer uma opção viável para o uso de dados climáticos limitados (AWAL et al, 2020;CHEN et al, 2020). Devido às mudanças climáticas, ao aumento da demanda de água e ao desperdício de uso de recursos hídricos, a busca de novos modelos se faz necessária, principalmente em relação aos modelos de parametrização local, conferindo maior precisão (CUNHA et al, 2021).…”

Section: Introductionunclassified

Estimativa Da Evapotranspiração De Referência Para Botucatu-Sp Por Meio De Modelos De Regressão

Sarnighausen

Gomes

Pai

et al. 2021

RBClima

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A evapotranspiração (ET) é um parâmetro agrometeorológico importante para o manejo de irrigação, zoneamento agroclimático e gestão de recursos hídricos. O modelo padrão de estimativa, parametrizado pela Food and Agriculture Organization (FAO-56), é o de Penman-Monteith e utiliza o saldo de radiação como variável de entrada, cujas medidas nem sempre estão disponíveis nas estações meteorológicas do país. A busca por ajustes aos modelos e alternativas para a estimativa de ET é frequente na literatura, a fim de identificar modelos que utilizem dados de entrada de fácil aquisição, como temperaturas máximas e mínimas. Em vista da necessidade de simplificar a estimativa de evapotranspiração, este artigo tem por objetivo utilizar variáveis meteorológicas de fácil aquisição como temperatura, umidade relativa do ar, velocidade do vento e precipitação para estimar a evapotranspiração de referência diária de Botucatu-SP. Modelos de regressão lineares múltiplos, com interação e termos polinomiais, e dinâmicas, admitindo defasagem no tempo, foram utilizados para obter o modelo de predição, considerando um nível de significância de 5%. Por meio dos índices de desempenho estatísticos utilizados foi possível identificar um modelo cujo ajuste é considerado aceitável para épocas em que a umidade relativa é igual ou superior a 76%. Sendo assim, são necessários estudos para incorporar ao modelo o efeito de sazonalidade em relação à previsão de ETo, a fim de fazer uso de modelos cujas variáveis sejam disponíveis em estações meteorológicas.

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Influence of the Estimated Global Solar Radiation on the Reference Evapotranspiration Obtained Through the Penman-Monteith Fao 56 Method

Cited by 18 publications

References 19 publications

Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH

Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH

Calibration and validation of the Angstrom–Prescott model in solar radiation estimation using optimization algorithms

Estimativa Da Evapotranspiração De Referência Para Botucatu-Sp Por Meio De Modelos De Regressão

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