Depth-Resolved Profiles of Soil Thermal Diffusivity Estimated from Temperature Time Series

In this study, researchers develop a parameter estimation approach that combines thermal modeling, Bayesian inference, Markov chain Monte Carlo simulation, and sliding time windows 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.

Researchers first assessed under which environmental conditions, temperature sensor characteristics, and deployment geometries soil thermal diffusivity can be reliably inferred. 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%. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced with accuracy to a few millimeters and with a bias defined by a standard deviation ≤ 0.01°C. Researchers then applied the developed approach to field data acquired on the Seward Peninsula, Alaska. Results indicate significant 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 bulk density or water content. These findings represent a critical step in the development of cost-effective methodologies to estimate soil thermal and physical properties at numerous depths and locations.