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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-430
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-430
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 22 Oct 2018

Research article | 22 Oct 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Bayesian performance evaluation of evapotranspiration models for an arid region in northwestern China

Guoxiao Wei1,2, Xiaoying Zhang3,4, Ming Ye5, Ning Yue1,2, and Fei Kan1,2 Guoxiao Wei et al.
  • 1Key Laboratory of Western China's Environmental System (Ministry of Education), Lanzhou University, 730000, China
  • 2School of Earth and Environmental Sciences, Lanzhou University, 730000, China
  • 3Institute of Groundwater and Earth Sciences, Jinan University, 510632, China
  • 4Construct Engineering College, Jilin University, 130400, China
  • 5Department of Earth, Ocean, and Atmospheric Science, Florida State University, 32306, USA

Abstract. Evapotranspiration (ET) is a major component of the land surface process involved in energy fluxes and balance, especially in the hydrological cycle of agricultural ecosystems. While many models have been developed to estimate ET, there has been no agreement on which model has the best performance. In this study, we evaluate four widely used ET models (i.e., the Shuttleworth Wallace (SW) model, Penman-Monteith (PM) model, Priestley-Taylor and Flint-Childs (PT-FC) model, and Advection-Aridity (AA) model) by using half-hourly ET observations obtained at a spring maize field in an arid region. The model evaluation is based on Bayesian model comparison and ranking using the Bayesian model evidence (BME), which balances between goodness-of-fit to data and model complexity. The BME-based model ranking (from the best to the worst) is SW, PM, PT-FC, and AA. The residuals between observations and corresponding model simulations are also analyzed, and the same model ranking is also obstained by using residual-based statistics, i.e., the coefficient of determination (R2), index of agreement (IA), root mean square error (RMSE) and model efficiency (EF). The PM and SW models overestimate ET, whereas the PT-FC and AA models underestimate ET in the study period. The four models also underestimate ET during the periods of partial crop cover. Especially during the late maturity stage, the PT-FC and AA models consistently produce an underestimation, and provide the worst simulated ET. As a result, at the half-hourly time scale, the SW model is the best model and recommend as the first choice for evaluating ET of spring maize in arid desert oasis areas.

Guoxiao Wei et al.
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