Evaluating three evapotranspiration estimates from model of different complexity over China using the ILAMB benchmarking system

• Published in: Journal of hydrology (Amsterdam) 2020-11, Vol.590, p.125553, Article 125553
• Main Authors: Wu, Genan, Cai, Xitian, Keenan, Trevor F., Li, Shenggong, Luo, Xiangzhong, Fisher, Joshua B., Cao, Ruochen, Li, Fa, Purdy, Adam J, Zhao, Wei, Sun, Xiaomin, Hu, Zhongmin
• Format: Article
• Language: English
• Subjects: Benchmarking; ENVIRONMENTAL SCIENCES; Evapotranspiration model; Model complexity
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2020.125553

•As evapotranspiration models become increasingly complex, there is a growing need for a comprehensive evaluation of the performance of models of different levels of complexity.•ILAMB system can assess the performance among three levels of complexity terrestrial evapotranspiration models at the regional scale comprehensively.•The more complex models outperformed the simplest model overall. Land surface models range in complexity of terrestrial evapotranspiration, yet it is unknown how model complexity translates to accuracy of modeled evapotranspiration estimates. Here, we use the International Land Model Benchmarking system to assess ET estimates from three models of varying complexity driven by the same forcing datasets: an earth system model, a terrestrial biosphere model, and a stand-alone ET model. The performance assessment includes both temporal and spatial evaluation, and different plant functional types across China. Our results indicate that the most complex model, an earth system model, performed best against the benchmarking datasets and metrics. Terrestrial biosphere model performed best in simulating inter-annual variability of ET, while earth system model performed best in simulating the seasonal cycle. The more complex models (earth system model and terrestrial biosphere model) perform better in forest, shrub and crop ecosystems, while the simpler model (stand-alone ET model) perform better in grass ecosystems. Our study demonstrates the impact of model complexity on ET estimates and highlights directions for future ET model improvements.