Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019GL085291 |
Physics-Constrained Machine Learning of Evapotranspiration | |
Zhao, Wen Li1,2; Gentine, Pierre2; Reichstein, Markus3,4; Zhang, Yao2; Zhou, Sha2; Wen, Yeqiang2,5; Lin, Changjie5; Li, Xi6; Qiu, Guo Yu1 | |
2019-12-23 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2019 |
文章类型 | Article;Early Access |
语种 | 英语 |
国家 | Peoples R China; USA; Germany |
英文摘要 | Estimating ecosystem evapotranspiration (ET) is important to understanding the global water cycle and to study land-atmosphere interactions. We developed a physics constrained machine learning (ML) model (hybrid model) to estimate latent heat flux (LE), which conserves the surface energy budget. By comparing model predictions with observations at 82 eddy covariance tower sites, our hybrid model shows similar performance to the pure ML model in terms of mean metrics (e.g., mean absolute percent errors) but, importantly, the hybrid model conserves the surface energy balance, while the pure ML model does not. A second key result is that the hybrid model extrapolates much better than the pure ML model, emphasizing the benefits of combining physics with ML for increased generalizations. The hybrid model allows inferring the structural dependence of ET and surface resistance (r(s)), and we find that vegetation height and soil moisture are the main regulators of ET and r(s). |
英文关键词 | machine learning physics constrained evapotranspiration FLUXNET energy conservation generalizations |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000503923400001 |
WOS关键词 | ENERGY-BALANCE CLOSURE ; SOIL-MOISTURE ; EDDY-COVARIANCE ; NEAR-SURFACE ; MODEL ; WATER ; ALGORITHM ; MODIS ; EVAPORATION ; CARBON |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/225235 |
专题 | 环境与发展全球科技态势 |
作者单位 | 1.Peking Univ, Sch Environm & Energy, Shenzhen Grad Sch, Shenzhen, Peoples R China; 2.Columbia Univ, Dept Earth & Environm Engn, New York, NY 10027 USA; 3.Max Planck Inst Biogeochem, Dept Biogeochem Integrat, Jena, Germany; 4.Michael Stifel Ctr Jena Data Driven & Simulat Sci, Jena, Germany; 5.Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing, Peoples R China; 6.Peking Univ, Inst Water Sci, Coll Engn, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Zhao, Wen Li,Gentine, Pierre,Reichstein, Markus,et al. Physics-Constrained Machine Learning of Evapotranspiration[J]. GEOPHYSICAL RESEARCH LETTERS,2019. |
APA | Zhao, Wen Li.,Gentine, Pierre.,Reichstein, Markus.,Zhang, Yao.,Zhou, Sha.,...&Qiu, Guo Yu.(2019).Physics-Constrained Machine Learning of Evapotranspiration.GEOPHYSICAL RESEARCH LETTERS. |
MLA | Zhao, Wen Li,et al."Physics-Constrained Machine Learning of Evapotranspiration".GEOPHYSICAL RESEARCH LETTERS (2019). |
条目包含的文件 | 条目无相关文件。 |
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