Articles | Volume 10, issue 4
https://doi.org/10.5194/esurf-10-687-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-10-687-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jul 2022
Research article |
| 04 Jul 2022
| 04 Jul 2022
Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series
Carlotta Brunetti
CORRESPONDING AUTHOR
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
John Lamb
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Stijn Wielandt
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Sebastian Uhlemann
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Ian Shirley
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Patrick McClure
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Baptiste Dafflon
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Short summary
This paper proposes a method to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution from soil temperature time series. We validate and apply this method to synthetic and field case studies. The improved quantification of soil thermal properties is a cornerstone for advancing the indirect estimation of the fraction of soil components needed to predict subsurface storage and fluxes of water, carbon, and nutrients.
This paper proposes a method to estimate thermal diffusivity and its uncertainty over time, at...
