Hydroinformatics in evapotranspiration estimation

Naoum, Sherif; Tsanis, Ioannis K.

doi:10.1016/s1364-8152(02)00076-2

Cited by 56 publications

(33 citation statements)

References 5 publications

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“…The main concern in temperate regions is the shortage of water, required for various uses, during the dry seasons. Despite this significance, evapotranspiration is one of the less understood components of the hydrologic cycle (Brutsaert, 1982;Naoum & Tsanis, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…In general, the combination of energy balance/aerodynamic equations "provides the most accurate results as a result of their foundation in physics and basis on rational relationships" (Jensen et al, 1990). The Food and Agricultural Organization of the United Nations (FAO) assumed the ET definition from Smith et al (1997) and compiled the so-called FAO Penman-Monteith method as the standard for estimation of ET (Allen et al, 1998;Naoum & Tsanis, 2003); this is also known as the FAO-56 method (from the number of the related technical report).…”

Section: Introductionmentioning

confidence: 99%

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Generalized regression neural networks for evapotranspiration modelling

Kişi

2006

Hydrological Sciences Journal

164

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The potential is investigated of the generalized regression neural networks (GRNN) technique in modelling of reference evapotranspiration (ET 0 ) obtained using the FAO Penman-Monteith (PM) equation. Various combinations of daily climatic data, namely solar radiation, air temperature, relative humidity and wind speed, are used as inputs to the ANN so as to evaluate the degree of effect of each of these variables on ET 0 . In the first part of the study, a comparison is made between the estimates provided by the GRNN and those obtained by the Penman, Hargreaves and Ritchie methods as implemented by the California Irrigation Management System (CIMIS). The empirical models were calibrated using the standard FAO PM ET 0 values. The GRNN estimates are also compared with those of the calibrated models. Mean square error, mean absolute error and determination coefficient statistics are used as comparison criteria for the evaluation of the model performances. The GRNN technique (GRNN 1) whose inputs are solar radiation, air temperature, relative humidity and wind speed, gave mean square errors of 0.058 and 0.032 mm 2 day , and determination coefficients of 0.985 and 0.986 for the Pomona and Santa Monica stations (Los Angeles, USA), respectively. Based on the comparisons, it was found that the GRNN 1 model could be employed successfully in modelling the ET 0 process. The second part of the study investigates the potential of the GRNN and the empirical methods in ET 0 estimation using the nearby station data. Among the models, the calibrated Hargreaves was found to perform better than the others.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generalized regression neural networks for evapotranspiration modelling

Kişi

2006

Hydrological Sciences Journal

164

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“…It represents the first step in the estimation of crop water requirements (Allen et al, 1998;Naoum and Tsanis, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…In general, the combination of energy balance/aerodynamic equations 'provide the most accurate results as a result of their foundation in physics and basis on rational relationships' (Jensen et al, 1990). The Food and Agricultural Organization of the United Nations (FAO) assumed the ET definition from Smith et al (1997) and adopted the FAO Penman-Monteith as the standard equation for estimation of ET (Allen et al, 1998;Naoum and Tsanis, 2003). However, the evapotranspiration models, such as those based on the…”

Section: Introductionmentioning

confidence: 99%

Evapotranspiration modelling from climatic data using a neural computing technique

Kisi

2007

Hydrological Processes

148

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Abstract:Evapotranspiration is one of the basic components of the hydrologic cycle and essential for estimating irrigation water requirements. This paper investigates the modelling of evapotranspiration using the feed-forward artificial neural network (ANN) technique with the Levenberg-Marquardt (LM) training algorithm. The LM algorithm has never been used in evapotranspiration estimation before. The LM is used for the optimization of network weights, since this algorithm is more powerful and faster than the conventional gradient descent. Various combinations of daily climatic data, i.e. wind speed, air temperature, relative humidity and solar radiation, from three stations in Los Angeles, USA, are used as inputs to the ANN so as to evaluate the degree of effect of each of these variables on evapotranspiration. A comparison is made between the estimates provided by the ANN and those of the following empirical models: Penman, Hargreaves, Turc. Mean square error, mean absolute error and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. Based on the comparisons, it was found that the neural computing technique could be employed successfully in modelling evapotranspiration process from the available climatic data. The results also indicate that the Hargreaves method provides better performance than the Penman and Turc methods in estimation of the evapotranspiration. The accuracy of the ANN technique in evapotranspiration estimation using nearby station data was also investigated.

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“…A automação é uma ferramenta que contribui, sobremaneira, na tomada de decisão da programação das irrigações em condições de campo, em tempo quase que real estimulando, assim, o uso de microcomputadores e softwares no dimensionamento da reposição da água transferida pelas plantas no processo de evapotranspiração (Gomide, 1998). Por outro lado, vários autores têm comparado diferentes métodos de estimativa da evapotranspiração de referência, com o objetivo de encontrar aquele que melhor se ajuste a determinadas condições climáticas e disponibilidade de dados (Chiew et al, 1995;Michalopoulou & Papaioannou, 1991;Jensen et al, 1997;Sentelhas, 1998;Silva et al, 2001); já outros trabalhos são relacionados com a elaboração de softwares para a estimativa da evapotranspiração de referência (Silva et al, 2002;Naoum & Tsanis, 2003).…”

Section: Introductionunclassified

Desenvolvimento de um sistema de estimativa da evapotranspiração de referência

Silva¹,

Filho²,

Silva³

et al. 2005

Rev. bras. eng. agríc. ambient.

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Neste trabalho, apresenta-se um programa computacional denominado SEVAP (Sistema de Estimativa da Evapotranspiração) com o objetivo de se estimar a evapotranspiração através de oito métodos. Os métodos indicados no programa são: Penman-Monteith, Hargreaves, Jensen-Haise, Linacre, Makkink, Priestley & Taylor, Tanque Classe A e Thornthwaite. A eficiência do SEVAP foi avaliada com a aplicação dos dados da estação Meteorológica de Petrolina, PE, referentes ao ano 2002. Foram utilizados as médias mensais desse ano e os valores diários dos meses de março e agosto, do mesmo ano, das seguintes variáveis meteorológicas: temperaturas máxima e mínima do ar, umidade relativa do ar, insolação, velocidade do vento, evaporação do Tanque Classe A e radiação solar global. Os resultados evidenciam que o modelo SEVAP possibilita estimativas confiáveis e contínuas da evapotranspiração de referência; além disso, ele pode ser utilizado no monitoramento local e regional da evapotranspiração potencial, em escala diária ou mensal. Na ausência dos dados de insolação e de velocidade do vento, o método de Penman-Monteith pode ser substituído pelo método de Hargreaves.

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Hydroinformatics in evapotranspiration estimation

Cited by 56 publications

References 5 publications

Generalized regression neural networks for evapotranspiration modelling

Generalized regression neural networks for evapotranspiration modelling

Evapotranspiration modelling from climatic data using a neural computing technique

Desenvolvimento de um sistema de estimativa da evapotranspiração de referência

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