Bayesian performance evaluation of evapotranspiration models based on eddy covariance systems in an arid region

Wei, Guoxiao; Zhang, Xiaoying; Ye, Ming; Yue, Ning; Kan, Fei

doi:10.5194/hess-23-2877-2019

Title: Bayesian performance evaluation of evapotranspiration models based on eddy covariance systems in an arid region

Journal Article · 10 July 2019 · Hydrology and Earth System Sciences (Online)

DOI: https://doi.org/10.5194/hess-23-2877-2019 · OSTI ID:1613230

Wei, Guoxiao ^[1]; Zhang, Xiaoying ^[2]; Ye, Ming ^[3]; Yue, Ning ^[1]; Kan, Fei ^[1]

Lanzhou Univ. (China)
Jilin Univ., Changchun (China)
Florida State Univ., Tallahassee, FL (United States)

Abstract. Evapotranspiration (ET) is a major component of the land surface processinvolved in energy fluxes and energy balance, especially in the hydrologicalcycle of agricultural ecosystems. While many models have been developed aspowerful tools to simulate ET, there is no agreement on which model bestdescribes the loss of water to the atmosphere. This study focuses on twoaspects, evaluating the performance of four widely used ET models andidentifying parameters, and the physical mechanisms that havesignificant impacts on the model performance. The four tested models arethe Shuttleworth–Wallace (SW) model, Penman–Monteith (PM) model,Priestley–Taylor and Flint–Childs (PT–FC) model, and advection–aridity (AA)model. By incorporating the mathematically rigorous thermodynamicintegration algorithm, the Bayesian model evidence (BME) approach is adoptedto select the optimal model with half-hourly ET observations obtained at aspring maize field in an arid region. Our results reveal that the SW model has thebest performance, and the extinction coefficient is not merely partitioningthe total available energy into the canopy and surface but also includingthe energy imbalance correction. The extinction coefficient is wellconstrained in the SW model and poorly constrained in the PM model but notconsidered in PT–FC and AA models. This is one of the main reasons that theSW model outperforms the other models. Meanwhile, the good fitting of SWmodel to observations can counterbalance its higher complexity. In addition,the detailed analysis of the discrepancies between observations and modelsimulations during the crop growth season indicate that explicit treatmentof energy imbalance and energy interaction will be the primary way offurther improving ET model performance.

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Research Organization:: Florida State Univ., Tallahassee, FL (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0019438

OSTI ID:: 1613230

Journal Information:: Hydrology and Earth System Sciences (Online), Vol. 23, Issue 7; ISSN 1607-7938

Publisher:: European Geosciences Union (EGU)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 15 works

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