A hierarchical Bayesian approach for multi‐site optimization of a satellite‐based evapotranspiration model. Issue 26 (14th November 2018)

• Record Type: Journal Article
• Authors: Su, Yonghong; Feng, Qi; Zhu, Gaofeng; Gu, Chunjie; Wang, Yunquan; Shang, Shasha; Zhang, Kun; Han, Tuo; Chen, Huiling; Ma, Jinzhu
• Is Part Of: Hydrological processes. Volume 32:Issue 26(2018)
• Page Start: 3907
• Page End: 3923
• Publication Date: 2018-11-14
• Subjects: differential evolution Markov chain algorithm -- evaporation -- evapotranspiration -- hierarchical Bayesian method -- process‐based ET model -- transpiration; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.13298
• Languages: English
• ISSNs: 0885-6087

Abstract

Abstract: Modelling is an important tool in simulating and partitioning evapotranspiration (ET). To obtain realistic partitioning of ET, a hierarchical Bayesian (HB) method was used to fit the Priestly–Taylor Jet Propulsion Laboratory (PT‐JPL) model against the multi‐tower Flux Network (FLUXNET) datasets. Unique to the HB method is its ability to exchange of information between sites and simultaneously estimate the species‐and PFT‐level parameters. The results suggested that the sensitive parameters varied at the both species and PFT levels. The parameter β (water control of soil evaporation) exhibited relatively wide species‐and PFT‐level posterior distributions, indicating that the original parameterization of soil moisture constraint may be problematic. Generally, the model with parameters determined by the HB approach showed better performance in predicting and partitioning ET than the original model, especially in evergreen needleleaf forests, open shrublands, closed shrublands, and woody savannas. To overcome the problem of parameter uncertainty (equifinality), direct observations of different components of ET are urgently needed in future studies, and assessments the extent to which the parameter uncertainties are reduced by the use of additional data.