Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-6041-2021
© Author(s) 2021. 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-25-6041-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Nov 2021
Research article |
| 25 Nov 2021
| 25 Nov 2021
A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration
Jiancong Chen
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of
California, Berkeley, CA, USA,
Baptiste Dafflon
Earth and Environmental Sciences Area,
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Anh Phuong Tran
Earth and Environmental Sciences Area,
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of
Water Resources Engineering and Technology, Water Resources Institute, 8,
Phao Dai Lang, Dong Da, Hanoi, Vietnam
Nicola Falco
Earth and Environmental Sciences Area,
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Susan S. Hubbard
Earth and Environmental Sciences Area,
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Short summary
The novel hybrid predictive modeling (HPM) approach uses a long short-term memory recurrent neural network to estimate evapotranspiration (ET) and ecosystem respiration (Reco) with only meteorological and remote-sensing inputs. We developed four use cases to demonstrate the applicability of HPM. The results indicate HPM is capable of providing ET and Reco estimations in challenging mountainous systems and enhances our understanding of watershed dynamics at sparsely monitored watersheds.
The novel hybrid predictive modeling (HPM) approach uses a long short-term memory recurrent...
