Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China
详细信息 查看全文
- 作者：Yang Chen ; Jiangzhou Xia ; Shunlin Liang ; Jinming Feng ; Joshua B. Fisher ; Xin Li ; Xianglan Li ; Shuguang Liu ; Zhuguo Ma ; Akira Miyata ; Qiaozhen Mu ; Liang Sun ; Jianwei Tang ; Kaicun Wang ; Jun Wen ; Yueju Xue ; Guirui Yu ; Tonggang Zha ; Li Zhang ; Qiang Zhang ; et al.
- 关键词：Evapotranspiration ; Eddy covariance ; Priestley&ndash ; Taylor ; Penman&ndash ; Monteith
- 刊名：Remote Sensing of Environment
- 出版年：January, 2014
- 年：2014
- 卷：140
- 期：Complete
- 页码：279-293
- 全文大小：4225 K
文摘
Evapotranspiration (ET) is a key component of terrestrial ecosystems because it links the hydrological, energy, and carbon cycles. Several satellite-based ET models have been developed for extrapolating local observations to regional and global scales, but recent studies have shown large model uncertainties in ET simulations. In this study, we compared eight ET models, including five empirical and three process-based models, with the objective of providing a reference for choosing and improving methods. The results showed that the eight models explained between 61 and 80% of the variability in ET at 23 eddy covariance towers in China and adjacent regions. The mean annual ET for all of China varied from 535 to 852 mm yr鈭?#xA0;1 among the models. The interannual variability of yearly ET varied significantly between models during 1982-2009 because of different model structures and the dominant environmental factors employed. Our evaluation results showed that the parameters of the empirical methods may have different combination because the environmental factors of ET are not independent. Although the three process-based models showed high model performance across the validation sites, there were substantial differences among them in the temporal and spatial patterns of ET, the dominant environment factors and the energy partitioning schemes. The disagreement among current ET models highlights the need for further improvements and validation, which can be achieved by investigating model structures and examining the ET component estimates and the critical model parameters.