Articles | Volume 18, issue 6
Hydrol. Earth Syst. Sci., 18, 2049–2064, 2014
https://doi.org/10.5194/hess-18-2049-2014
Hydrol. Earth Syst. Sci., 18, 2049–2064, 2014
https://doi.org/10.5194/hess-18-2049-2014

Research article 03 Jun 2014

Research article | 03 Jun 2014

Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions

F. M. Anayah1,* and J. J. Kaluarachchi2 F. M. Anayah and J. J. Kaluarachchi
  • 1Utah Water Research Laboratory, Utah State University, Logan, UT 84322-8200, USA
  • 2College of Engineering, Utah State University, Logan, UT 84322-4100, USA
  • *currently at: Palestine Technical University, Kadoorie, Tulkarm, Palestine

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 recent 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 toward predicting ET in large river basins with limited data and requiring no calibration.