Comparative evaluation of 24 reference evapotranspiration equations applied on an evergreen-broadleaved forest
Autor: | Nikolaos Proutsos, I. Argyrokastritis, Athanassios Bourletsikas |
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Rok vydání: | 2017 |
Předmět: |
Hydrology
010504 meteorology & atmospheric sciences 0208 environmental biotechnology 02 engineering and technology Evergreen 01 natural sciences 020801 environmental engineering Comparative evaluation Goodness of fit Evapotranspiration Statistics Forest ecology Water cycle 0105 earth and related environmental sciences Water Science and Technology Mathematics |
Zdroj: | Hydrology Research. 49:1028-1041 |
ISSN: | 2224-7955 0029-1277 |
DOI: | 10.2166/nh.2017.232 |
Popis: | Reference evapotranspiration (ET0) is a major component of the hydrological cycle. Its use is essential both for the hydrological rainfall–runoff assessment models and determination of water requirements in agricultural and forest ecosystems. This study investigates the performance of 24 different methods, which produce ET0 or potential evapotranspiration estimates above a grass-covered ground in a Mediterranean forest environment in Greece and compares the derived results with those of the presumed most accurate and scientifically acceptable Penman–Monteith method (ETP-M). Their performance was evaluated on a daily basis for a period of 17 years, using 17 different statistical parameters of goodness of fit. The results showed that some empirical methods could serve as suitable alternatives. More specifically, Copais (ETCOP), Hargreaves original (ETHAR), and Valiantzas2 (ETVA2) methods, exhibited very good values of the model efficiency index, EF (0.934, 0.932, and 0.917, respectively) and the index of agreement, d (0.984, 0.982, and 0.977, respectively). Additionally, the differences of the estimated mean daily value against the respective ETP-M value (rt index) for all methods had a range of −27.8% (Penman – ETPEN) to +59.5% (Romanenko – ETROM), while Copais (ETCOP), Hargreaves–Samani modified1 (ETHS1), and STU (ETSTU) yielded the best values (−0.06%, +0.06%, and 0.22%, respectively). |
Databáze: | OpenAIRE |
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