Articles | Volume 23, issue 7
https://doi.org/10.5194/hess-23-2877-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-2877-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jul 2019
Research article |
| 10 Jul 2019
| 10 Jul 2019
Bayesian performance evaluation of evapotranspiration models based on eddy covariance systems in an arid region
Guoxiao Wei
Key Laboratory of Western China's Environmental System (Ministry of
Education), Lanzhou University, Lanzhou, 730000, China
School of Earth and Environmental Sciences, Lanzhou University,
Lanzhou, 730000, China
Xiaoying Zhang
CORRESPONDING AUTHOR
College of Construction Engineering, Jilin University, Changchun, 130400, China
Ming Ye
Department of Earth, Ocean, and Atmospheric Science, Florida State
University, Tallahassee, FL 32306, USA
Ning Yue
Key Laboratory of Western China's Environmental System (Ministry of
Education), Lanzhou University, Lanzhou, 730000, China
School of Earth and Environmental Sciences, Lanzhou University,
Lanzhou, 730000, China
Fei Kan
Key Laboratory of Western China's Environmental System (Ministry of
Education), Lanzhou University, Lanzhou, 730000, China
School of Earth and Environmental Sciences, Lanzhou University,
Lanzhou, 730000, China
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Cited
18 citations as recorded by crossref.
- Disentangling model complexity in green roof hydrological analysis: A Bayesian perspective G. Brunetti et al. 10.1016/j.watres.2020.115973
- An enhanced shuttleworth-wallace model for simulation of evapotranspiration and its components H. Chen et al. 10.1016/j.agrformet.2021.108769
- Modeling the dynamics of evapotranspiration of wolfberry (Lycium barbarum L.) under different cultivation methods on the Tibetan Plateau J. Wang et al. 10.1016/j.jhydrol.2024.131537
- Handling model complexity with parsimony: Numerical analysis of the nitrogen turnover in a controlled aquifer model setup G. Brunetti et al. 10.1016/j.jhydrol.2020.124681
- Improving Evapotranspiration Model Performance by Treating Energy Imbalance and Interaction G. Wei et al. 10.1029/2020WR027367
- Construction of an evapotranspiration model and analysis of spatiotemporal variation in Xilin River Basin, China H. Yu et al. 10.1371/journal.pone.0256981
- Precipitation and evapotranspiration partitioning on the Three-River Source Region: A comparison between water balance and energy balance models L. Zhang et al. 10.1016/j.ejrh.2021.100936
- Seawater intrusion alters nitrogen cycling patterns through hydrodynamic behavior and biochemical reactions: Based on Bayesian isotope mixing model and microbial functional network G. Xiong et al. 10.1016/j.scitotenv.2022.161368
- Two Alternatives to the Two‐Source Energy Balance Evapotranspiration Model G. Wei et al. 10.1029/2022WR032958
- Evapotranspiration partitioning by integrating eddy covariance, micro-lysimeter and unmanned aerial vehicle observations: A case study in the North China Plain J. Bian et al. 10.1016/j.agwat.2024.108735
- The effect of pixel heterogeneity on surface heat and water vapor flux estimated by the remote sensing-based model coupled with deep learning J. Feng et al. 10.1016/j.jhydrol.2023.130036
- Application and Uncertainty Analysis of Data-Driven and Process-Based Evapotranspiration Models Across Various Ecosystems Q. Wang et al. 10.1007/s11269-024-03772-5
- Evapotranspiration and Its Partitioning in Alpine Meadow of Three-River Source Region on the Qinghai-Tibetan Plateau L. Zhang et al. 10.3390/w13152061
- Simple and Two-Level Hierarchical Bayesian Approaches for Parameter Estimation with One- and Two-Layer Evapotranspiration Models of Crop Fields S. Shiraki et al. 10.3390/w13243607
- Ranking of Empirical Evapotranspiration Models in Different Climate Zones of Pakistan M. Hamed et al. 10.3390/land11122168
- Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions T. Du et al. 10.3390/rs11232856
- Hybrid deep learning method for a week-ahead evapotranspiration forecasting A. Ahmed et al. 10.1007/s00477-021-02078-x
- Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia M. Muhammad et al. 10.3390/w14182858
18 citations as recorded by crossref.
- Disentangling model complexity in green roof hydrological analysis: A Bayesian perspective G. Brunetti et al. 10.1016/j.watres.2020.115973
- An enhanced shuttleworth-wallace model for simulation of evapotranspiration and its components H. Chen et al. 10.1016/j.agrformet.2021.108769
- Modeling the dynamics of evapotranspiration of wolfberry (Lycium barbarum L.) under different cultivation methods on the Tibetan Plateau J. Wang et al. 10.1016/j.jhydrol.2024.131537
- Handling model complexity with parsimony: Numerical analysis of the nitrogen turnover in a controlled aquifer model setup G. Brunetti et al. 10.1016/j.jhydrol.2020.124681
- Improving Evapotranspiration Model Performance by Treating Energy Imbalance and Interaction G. Wei et al. 10.1029/2020WR027367
- Construction of an evapotranspiration model and analysis of spatiotemporal variation in Xilin River Basin, China H. Yu et al. 10.1371/journal.pone.0256981
- Precipitation and evapotranspiration partitioning on the Three-River Source Region: A comparison between water balance and energy balance models L. Zhang et al. 10.1016/j.ejrh.2021.100936
- Seawater intrusion alters nitrogen cycling patterns through hydrodynamic behavior and biochemical reactions: Based on Bayesian isotope mixing model and microbial functional network G. Xiong et al. 10.1016/j.scitotenv.2022.161368
- Two Alternatives to the Two‐Source Energy Balance Evapotranspiration Model G. Wei et al. 10.1029/2022WR032958
- Evapotranspiration partitioning by integrating eddy covariance, micro-lysimeter and unmanned aerial vehicle observations: A case study in the North China Plain J. Bian et al. 10.1016/j.agwat.2024.108735
- The effect of pixel heterogeneity on surface heat and water vapor flux estimated by the remote sensing-based model coupled with deep learning J. Feng et al. 10.1016/j.jhydrol.2023.130036
- Application and Uncertainty Analysis of Data-Driven and Process-Based Evapotranspiration Models Across Various Ecosystems Q. Wang et al. 10.1007/s11269-024-03772-5
- Evapotranspiration and Its Partitioning in Alpine Meadow of Three-River Source Region on the Qinghai-Tibetan Plateau L. Zhang et al. 10.3390/w13152061
- Simple and Two-Level Hierarchical Bayesian Approaches for Parameter Estimation with One- and Two-Layer Evapotranspiration Models of Crop Fields S. Shiraki et al. 10.3390/w13243607
- Ranking of Empirical Evapotranspiration Models in Different Climate Zones of Pakistan M. Hamed et al. 10.3390/land11122168
- Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions T. Du et al. 10.3390/rs11232856
- Hybrid deep learning method for a week-ahead evapotranspiration forecasting A. Ahmed et al. 10.1007/s00477-021-02078-x
- Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia M. Muhammad et al. 10.3390/w14182858
Latest update: 23 Nov 2024
Short summary
Accurately evaluating evapotranspiration (ET) is a critical challenge in improving hydrological process modeling. Here we evaluated four ET models (PM, SW, PT–FC, and AA) under the Bayesian framework. Our results reveal that the SW model has the best performance. This is in part because the SW model captures the main physical mechanism in ET; the other part is that the key parameters, such as the extinction factor, could be well constrained with observation data.
Accurately evaluating evapotranspiration (ET) is a critical challenge in improving hydrological...
