Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling

Pan, Shufen; Pan, Naiqing; Tian, Hanqin; Friedlingstein, Pierre; Sitch, Stephen; Shi, Hao; Arora, Vivek K.; Haverd, Vanessa; Jain, Atul K.; Kato, Etsushi; Lienert, Sebastian; Lombardozzi, Danica; Nabel, Julia E. M. S.; Ottlé, Catherine; Poulter, Benjamin; Zaehle, Sönke; Running, Steven W.

doi:https://doi.org/10.5194/hess-24-1485-2020

Articles | Volume 24, issue 3

https://doi.org/10.5194/hess-24-1485-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-24-1485-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 3

Research article

|

31 Mar 2020

Research article |

| 31 Mar 2020

Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling

Shufen Pan, Naiqing Pan, Hanqin Tian, Pierre Friedlingstein, Stephen Sitch, Hao Shi, Vivek K. Arora, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Julia E. M. S. Nabel, Catherine Ottlé, Benjamin Poulter, Sönke Zaehle, and Steven W. Running

Supplement

https://doi.org/10.5194/hess-24-1485-2020-supplement

Data sets

MOD16A_MONTHLY.MERRA_GMAO_1kmALB S. W. Running http://files.ntsg.umt.edu/data/NTSG_Products/MOD16/

Latent heat flux on land M. Jung https://www.bgc-jena.mpg.de/geodb/projects/FileDetails.php

P-LSH ET K. Zhang http://files.ntsg.umt.edu/data/ET_global_monthly/Global_8kmResolution/

Monthly global observation-driven Penman-Monteith-Leuning (PML) evapotranspiration and components Y. Zhang https://data.csiro.au/dap/landingpage?pid=csiro:17375

GIMMS NDVI3gV1 J. E. Pinzon and C. J. Tucker https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/

Short summary

Evapotranspiration (ET) links global water, carbon and energy cycles. We used 4 remote sensing models, 2 machine-learning algorithms and 14 land surface models to analyze the changes in global terrestrial ET. These three categories of approaches agreed well in terms of ET intensity. For 1982–2011, all models showed that Earth greening enhanced terrestrial ET. The small interannual variability of global terrestrial ET suggests it has a potential planetary boundary of around 600 mm yr^-1.