Physics‐Constrained Machine Learning of Evapotranspiration. Issue 24 (23rd December 2019)
- Record Type:
- Journal Article
- Title:
- Physics‐Constrained Machine Learning of Evapotranspiration. Issue 24 (23rd December 2019)
- Main Title:
- Physics‐Constrained Machine Learning of Evapotranspiration
- Authors:
- Zhao, Wen Li
Gentine, Pierre
Reichstein, Markus
Zhang, Yao
Zhou, Sha
Wen, Yeqiang
Lin, Changjie
Li, Xi
Qiu, Guo Yu - Abstract:
- Abstract: 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 . Plain Language Summary: A physics constrained machine learning model is developed using the FLUXNET2015 Tier 1 data set. This new approach is able to effectively retrieve latent heat flux while constraining energy conservation in the surface energy budget. This hybrid model has better performance in extrapolation than a pure machine learning model. Key Points: A physics‐constrained machine learning model of evapotranspiration (hybrid model) is developed and trained using the FLUXNET 2015 data set TheAbstract: 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 . Plain Language Summary: A physics constrained machine learning model is developed using the FLUXNET2015 Tier 1 data set. This new approach is able to effectively retrieve latent heat flux while constraining energy conservation in the surface energy budget. This hybrid model has better performance in extrapolation than a pure machine learning model. Key Points: A physics‐constrained machine learning model of evapotranspiration (hybrid model) is developed and trained using the FLUXNET 2015 data set The evapotranspiration retrieved by the hybrid model is as accurate as pure machine learning model and also conserves surface energy balance The hybrid model better reproduces extremes and thus better extrapolates compared to the pure machine learning approach … (more)
- Is Part Of:
- Geophysical research letters. Volume 46:Issue 24(2019)
- Journal:
- Geophysical research letters
- Issue:
- Volume 46:Issue 24(2019)
- Issue Display:
- Volume 46, Issue 24 (2019)
- Year:
- 2019
- Volume:
- 46
- Issue:
- 24
- Issue Sort Value:
- 2019-0046-0024-0000
- Page Start:
- 14496
- Page End:
- 14507
- Publication Date:
- 2019-12-23
- Subjects:
- machine learning -- physics constrained -- evapotranspiration -- FLUXNET -- energy conservation -- generalizations
Geophysics -- Periodicals
Planets -- Periodicals
Lunar geology -- Periodicals
550 - Journal URLs:
- http://www.agu.org/journals/gl/ ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1029/2019GL085291 ↗
- Languages:
- English
- ISSNs:
- 0094-8276
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4156.900000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17751.xml