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Open AccessArticle

Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates with Local Calibration

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Department of Agricultural Engineering, Federal University of Viçosa (UFV), Avenue Peter Henry Rolfs, Viçosa 36570-900, Minas Gerais, Brazil
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Biosystems Engineering Department, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Avenue Pádua Dias, n°11, Piracicaba-SP 13418-900, Brazil
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Author to whom correspondence should be addressed.
Water 2019, 11(11), 2272; https://doi.org/10.3390/w11112272
Received: 8 September 2019 / Revised: 10 October 2019 / Accepted: 26 October 2019 / Published: 30 October 2019
(This article belongs to the Section Water Use and Scarcity)
Improving irrigation water management is an important asset when facing increased water shortages. The Hargreaves–Samani (HS) method is a simple method that can be used as an alternative to the Penman–Monteith (PM) method, which requires only temperature measurements for estimating reference evapotranspiration (ETo). However, the applicability of this method relies on its calibration to local meteorological specificities. The objective of this study was to investigate the effects of local calibration on the performance of the HS equation. The study was carried out for the middle portion of the São Francisco River Basin (MSFB), Brazil, and considered four calibration approaches: A1—single calibration for the entire MSFB; A2—separate calibration by clusters of months; A3—by clusters of stations; and A4—for all contexts resulting by combining A2 and A3. Months from the wet season showed larger improvements by the calibration of the HS model, since mean air temperature and its daily range showed stronger correlations to ETo. On the other hand, the months from the dry season and stations from the eastern region of MSFB performed poorly regardless of the calibration approach adopted. This occurred because, in those cases, ETo presented larger correlation to variables that are missing in the HS equation, and the use of the full PM equation seems unavoidable. View Full-Text
Keywords: agrometeorology; biomes transition; data clustering; k-means; Penman–Monteith agrometeorology; biomes transition; data clustering; k-means; Penman–Monteith
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Althoff, D.; Santos, R.A.; Bazame, H.C.; Cunha, F.F.; Filgueiras, R. Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates with Local Calibration. Water 2019, 11, 2272.

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