&lt;b&gt;Reference evapotranspiration models using different time scales in the Jaboticabal region of São Paulo, Brazil

Caporusso, Natália Buzinaro; Rolim, Glauco de Souza

doi:10.4025/actasciagron.v37i1.18277

Cited by 24 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similar results were found by Bragança et al (2010), Moura et al (2013) and Caporusso & Rolim (2015). The HS method presented poor fit, regardless of the time scale and season (Table 2), which was already verified at the daily scale ( Figure 1).…”

Section: Resultssupporting

confidence: 87%

Penman-Monteith with missing data and Hargreaves-Samani for ETo estimation in Espírito Santo state, Brazil

Venâncio

Cunha

Mantovani

et al. 2019

Rev. bras. eng. agríc. ambient.

View full text Add to dashboard Cite

The Penman-Monteith method (PM-FAO) is recommended as standard for calculation of reference evapotranspiration (ETo). However, its use requires a series of meteorological variables that is not normally available, restricting its application in many locations. A solution to the problem of unavailability of meteorological data was presented in FAO Bulletin 56, which contains methodologies for estimating wind speed, solar radiation and relative humidity. The objective of the present study was to evaluate the performance of the PM-FAO methodologies for missing data and Hargreaves-Samani as alternatives to the PM-FAO standard method at different time scales and seasons for the municipalities of Linhares and São Mateus, located in the northern region of the state of Espírito Santo. The comparison was performed using linear regression parameters (β0 and β1), coefficient of determination, standard error of estimation (SEE) and coefficient of performance. The best alternative to the standard PM-FAO standard method for estimating ETo in the studied area was the Penman-Monteith method with missing wind speed data, since the R2 for this method always remained above 0.94 and the confidence coefficient was classified as great, for all seasons and scales. The Hargreaves-Samani method did not present satisfactory performance, with R2 below 0.7, regardless of the time scale and time of the year, and it yielded the greatest SEE (1.0 mm d-1) at spring on a two-day scale. Thus, its use in the northern region of the Espírito Santo state is not recommended.

show abstract

Section: Resultssupporting

confidence: 87%

Penman-Monteith with missing data and Hargreaves-Samani for ETo estimation in Espírito Santo state, Brazil

Venâncio

Cunha

Mantovani

et al. 2019

Rev. bras. eng. agríc. ambient.

View full text Add to dashboard Cite

show abstract

“…Among the numerous empirical methods to estimate ETo, nine models were selected that had the best performance under different climates on the basis of previous investigations [1,[46][47][48][49][50], and the results were compared with the FPM. ETo is the reference crop evapotranspiration (mm/day), R n is the net radiation (MJ/m 2 /day), G is the soil heat flux (MJ/m 2 /day), γ is the psychrometric constant (kPa/ • C), e s is the saturation vapor pressure (kPa), e a is the actual vapor pressure (kPa), ∆ is the slope of the saturation vapor pressure-temperature curve (kPa/ • C), T is the average daily air temperature ( • C), u is the mean daily wind speed at 2 m (m/s), H is the elevation (m), ϕ is the latitude (rad), T min is the minimum air temperature ( • C), T max is the maximum air temperature ( • C), RH is the average relative humidity (%), n is the actual duration of sunshine (hr), R s is the solar radiation (MJ/m 2 /day), R a is the extraterrestrial radiation (MJ/m 2 /day), λ is the latent heat of vaporization (MJ/kg), and C T , I, K T , a, b, P, a t , and T x are empirical coefficients Genetic algorithm (GA), genetic programming (GP), and gene expression programming (GEP) are different kinds of data-driven machine learning techniques to find an optimum solution for complex problems by artificial intelligence (AI).…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

et al. 2019

View full text Add to dashboard Cite

In the current research, gene expression programming (GEP) was applied to model reference evapotranspiration (ETo) in 18 regions of Iran with limited meteorological data. Initially, a genetic algorithm (GA) was employed to detect the most important variables for estimating ETo among mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), sunshine (n), and wind speed (WS). The results indicated that a coupled model containing the Tmean and WS can predict ETo accurately (RMSE = 0.3263 mm day −1 ) for arid, semiarid, and Mediterranean climates. Therefore, this model was adjusted using the GEP for all 18 synoptic stations. Under very humid climates, it is recommended to use a temperature-based GEP model versus wind speed-based GEP model. The optimal and lowest performance of the GEP belonged to Shahrekord (SK), RMSE = 0.0650 mm day −1 , and Kerman (KE), RMSE = 0.4177 mm day −1 , respectively. This research shows that the GEP is a robust tool to model ETo in semiarid and Mediterranean climates (R 2 > 0.80). However, GEP is recommended to be used cautiously under very humid climates and some of arid regions (R 2 < 0.50) due to its poor performance under such extreme conditions. Atmosphere 2019, 10, 311 2 of 15 sediment [9], predicting velocity in compound channels [10], characterizing risks in water supply systems [11], and modeling evaporation [12].Although the GEP has been developed in water resources studies, the application of this technique for ETo modeling is limited. Some of the successful applications of the GEP to estimate ETo can be listed as follows.Parasuraman et al. [3] modeled ETo using only ground temperature and net radiation. Irmak and Kamble [13] investigated evapotranspiration data assimilation with the GA and soil, water, atmosphere, and plant (SWAP) model for on-demand irrigation. The data assimilation methodology obtained from the present research can be considered a practical tool at the field scale for scheduling the irrigation estimated by remote sensing-based evapotranspiration. The results showed that the GA was effectively able to determine the terms included in the fitness function, and parameters were predicted reasonably, especially if only four variables were included. Traore and Guven [14] developed regional-specific numerical models of ETo using the GEP in Sahel. Statistically, the GEP was an effectual modeling tool for the successful computation of ETo under the study area. The results indicated that, using the GEP model, it would be possible to formulate an accurate and applicable numerical equation for each region by irrigation systems for the manual computation of ETo under the study area, where sufficient meteorological variable is often missing. Shiri [15] claimed that GEP outperforms empirical model to calculate ETo in hyper arid regions over Iran. Traore et al. [16] found that GEP is a robust tool to model ETo in in Jiangsu province, China. Mattar [17] demonstrated that GEP is a more accurate method than empirical equations to ...

show abstract

“…A evapotranspiração é uma variável fundamental para obter a necessidade hídrica das culturas, e também fornece subsídio para o dimensionamento de sistemas de bombeamento, adução e distribuição de água na irrigação (ABABAEI, 2014;FERRAZ et al, 2014, MANCOSU et al, 2014ROLIM, 2015). Os métodos empregados na quantificação da ETo podem ser diretos ou indiretos.…”

Section: Introductionunclassified

Desempenho De Equações Agrometeorológicas Para as Estações Seca E Chuvosa Para a Cidade De Sorriso/Mt

Santana¹,

Lima²,

Damascena³

et al. 2019

Enci Bio

View full text Add to dashboard Cite

RESUMO A ETo originalmente introduzida sob o termo evapotranspiração potencial (ETp), é um importante parâmetro agrometeorológico, principalmente para planejamento e manejo de irrigação. Existem diversas equações para calcular a ETo, desde aqueles mais simples, com base puramente empírica, até aquelas que apresentam embasamento físico consistente sobre o fenômeno, contudo, há sempre algum empirismo presente. Neste sentido, o presente trabalho tem por objetivo avaliar o desempenho das equações Jensen-Haise, Hargreaves-Samani, Turc, Priestley-Taylor, Makkink, Radiação Global, Benevides-Lopez, Camargo, Budiko e Ivanov, de ETo, para o período seco e chuvoso, para Sorriso/MT, comparativamente a equação padrão da FAO. Foram feitas as avaliações por análise de regressão e do indicador de desempenho "c". Os resultados mostraram que, para a escala diária, todas as equações avaliadas apresentaram ajustes insatisfatórios, quando comparados à equação padrão da FAO. Logo, seu uso não é recomendado sem antes receber a calibração dos coeficientes para a cidade em estudo. PALAVRAS-CHAVE: evapotranspiração, irrigação, planejamento

show abstract

<b>Reference evapotranspiration models using different time scales in the Jaboticabal region of São Paulo, Brazil

Cited by 24 publications

References 21 publications

Penman-Monteith with missing data and Hargreaves-Samani for ETo estimation in Espírito Santo state, Brazil

Penman-Monteith with missing data and Hargreaves-Samani for ETo estimation in Espírito Santo state, Brazil

A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

Desempenho De Equações Agrometeorológicas Para as Estações Seca E Chuvosa Para a Cidade De Sorriso/Mt

Contact Info

Product

Resources

About