Please use this identifier to cite or link to this item: https://scholar.ptuk.edu.ps/handle/123456789/532
Title: Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions
Authors: Anayah, Fathi
Kaluarachchi, Jagath
Keywords: Evapotranspiration
Complementary methods
Eddy covariance
Hydrologic modeling
Issue Date: 3-Jun-2014
Publisher: Hydrol. Earth Syst. Sci., 18
Citation: Anayah, F.M., Kaluarachchi, J.J., 2014. Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions. Hydrology and Earth System Sciences 18, 2049-2064. DOI: 10.5194/hess-18-2049-2014
Series/Report no.: Hydrol. Earth Syst. Sci., 18;
Abstract: Reliable estimation of evapotranspiration (ET) is important for the purpose of water resources planning and management. Complementary methods, including complementary relationship areal evapotranspiration (CRAE), advection aridity (AA) and Granger and Gray (GG), have been used to estimate ET because these methods are simple and practical in estimating regional ET using meteorological data only. However, prior studies have found limitations in these methods especially in contrasting climates. This study aims to develop a calibration-free universal method using the complementary relationships to compute regional ET in contrasting climatic and physical conditions with meteorological data only. The proposed methodology consists of a systematic sensitivity analysis using the existing complementary methods. This work used 34 global FLUXNET sites where eddy covariance (EC) fluxes of ET are available for validation. A total of 33 alternative model variations from the original complementary methods were proposed. Further analysis using statistical methods and simplified climatic class definitions produced one distinctly improved GG-model- based alternative. The proposed model produced a single- step ET formulation with results equal to or better than the re- cent studies using data-intensive, classical methods. Average root mean square error (RMSE), mean absolute bias (BIAS) and R2 (coefficient of determination) across 34 global sites were 20.57 mm month−1 , 10.55 mm month−1 and 0.64, respectively. The proposed model showed a step forward to- ward predicting ET in large river basins with limited data and requiring no calibration.
URI: https://scholar.ptuk.edu.ps/handle/123456789/532
Appears in Collections:Engineering and Technology Faculty

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