2016
DOI: 10.7127/rbai.v10n200407
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Métodos Empíricos Para Estimativa Da Evapotranspiração De Referência No Estado Do Rio De Janeiro

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Cited by 4 publications
(3 citation statements)
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“…The Priestley-Taylor method was the one that showed the worst performance, and the obtained values did not present a good fit concerning the class A pan method used in CENICAÑA The values were of r = 0.36, d = 0.35, and c = 0.13, indicating "terrible" performance. In the same way, Vallory et al (2016) concluded that the Camargo method for the estimation of ET 0 on a daily basis cannot be recommended in summer climatic conditions, which are the conditions usually found in the study area.…”
Section: Ementioning
confidence: 82%
“…The Priestley-Taylor method was the one that showed the worst performance, and the obtained values did not present a good fit concerning the class A pan method used in CENICAÑA The values were of r = 0.36, d = 0.35, and c = 0.13, indicating "terrible" performance. In the same way, Vallory et al (2016) concluded that the Camargo method for the estimation of ET 0 on a daily basis cannot be recommended in summer climatic conditions, which are the conditions usually found in the study area.…”
Section: Ementioning
confidence: 82%
“…On the other hand, Tagliaferre et al (2010) observed, for Eunápolia -BA, a behavior similar to that observed in this study, where the Hargreaves-Samani method showed a tendency of 23% overestimation of ETo data. Vallory et al (2016) also observed a tendency of overestimation of ETo data for the state of Rio de Janeiro, using the PriestleyTaylor and Hargreaves-Samani methods.…”
Section: Resultsmentioning
confidence: 99%
“…The GHCN product was also in uenced by latitude, in addition to altitude, and longitude. The differences between both products are probably due to the interpolation method and the number and time series size used in the interpolation.Given the importance of ET, di culty in obtaining PET (or ET o ) data, and reliable statistical results (with reasonable -good performance and low errors), PET (or ETo) estimations using GCD constitute a convenient and attractive alternative to solve these data gaps problem(Vallory et al 2016;Monteiro et al 2021). Furthermore, PET estimations using GCD allow to evaluate systematic and non-systematic errors, linking estimate performance with local characteristics.…”
mentioning
confidence: 99%