EGU22-7749
https://doi.org/10.5194/egusphere-egu22-7749
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using cosmic-ray neutron sensors with machine learning to improve predictions of evapotranspiration and carbon fluxes

Daniel Power1, Miguel Angel Rico-Ramirez1, Pierre Gentine2,3, David McJannet4, Humberto Ribeiro da Rocha5, and Rafael Rosolem1,6
Daniel Power et al.
  • 1University of Bristol, United Kingdom of Great Britain – England, Scotland, Wales (daniel.power@bristol.ac.uk)
  • 2Earth and Environmental Engineering Department, Columbia University, New York, NY10027, USA
  • 3Earth Institute, Columbia University, New York, NY 10025, USA
  • 4CSIRO, EcoSciences Precinct, GPO Box 2583, Dutton Park, QLD, 4001, Australia
  • 5Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, Brazil
  • 6Cabot Institute for the Environment, University of Bristol, Bristol, UK

How to cite: Power, D., Rico-Ramirez, M. A., Gentine, P., McJannet, D., da Rocha, H. R., and Rosolem, R.: Using cosmic-ray neutron sensors with machine learning to improve predictions of evapotranspiration and carbon fluxes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7749, https://doi.org/10.5194/egusphere-egu22-7749, 2022.

This abstract has been withdrawn on 04 May 2022.