18 Nov 2021
18 Nov 2021
Status: a revised version of this preprint is currently under review for the journal ESurf.

Estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series and uncertainty quantification

Carlotta Brunetti, John Lamb, Stijn Wielandt, Sebastian Uhlemann, Ian Shirley, Patrick McClure, and Baptiste Dafflon Carlotta Brunetti et al.
  • Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach needs to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time-windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise) and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 °C at 5 cm below the ground surface, temperature gradients > 2 °C m−1, and a sliding time-window of at least 4 days, the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors spaced precisely (with few-millimeter accuracy), with a level of noise ≤ 0.02 °C, and with a bias defined by a standard deviation ≤ 0.01 °C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time-window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in carbon or water content.

Carlotta Brunetti et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esurf-2021-68', Elchin Jafarov, 20 Jan 2022
    • AC1: 'Reply on RC1', Carlotta Brunetti, 17 Mar 2022
  • RC2: 'Comment on esurf-2021-68', Anonymous Referee #2, 25 Jan 2022
    • AC2: 'Reply on RC2', Carlotta Brunetti, 17 Mar 2022

Carlotta Brunetti et al.

Data sets

Synthetic soil temperature time-series Carlotta Brunetti

Carlotta Brunetti et al.


Total article views: 623 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
507 98 18 623 9 7
  • HTML: 507
  • PDF: 98
  • XML: 18
  • Total: 623
  • BibTeX: 9
  • EndNote: 7
Views and downloads (calculated since 18 Nov 2021)
Cumulative views and downloads (calculated since 18 Nov 2021)

Viewed (geographical distribution)

Total article views: 594 (including HTML, PDF, and XML) Thereof 594 with geography defined and 0 with unknown origin.
Country # Views %
  • 1


Latest update: 27 May 2022
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 fraction of soil components needed to predict the subsurface storage and fluxes of water, carbon, and nutrients.