scholarly journals A 1-D analytical method for estimating surface water-groundwater interactions and effective thermal diffusivity using temperature time series

2012 ◽  
Vol 48 (11) ◽  
Author(s):  
A. M. McCallum ◽  
M. S. Andersen ◽  
G. C. Rau ◽  
R. I. Acworth
Keyword(s):  
Time Series ◽  
Thermal Diffusivity ◽  
Surface Water ◽  
Analytical Method ◽  
Temperature Time Series ◽  
Effective Thermal Diffusivity ◽  
Temperature Time

scholarly journals The effect of streambed heterogeneity on groundwater-surface water exchange fluxes inferred from temperature time series

2015 ◽  
Vol 51 (1) ◽  
pp. 198-212 ◽  
Author(s):  
Dylan J. Irvine ◽  
Roger H. Cranswick ◽  
Craig T. Simmons ◽  
Margaret A. Shanafield ◽  
Laura K. Lautz
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Water Exchange ◽  
Temperature Time Series ◽  
Temperature Time ◽  
Streambed Heterogeneity

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

2021 ◽  
Author(s):  
Carlotta Brunetti ◽  
John Lamb ◽  
Stijn Wielandt ◽  
Sebastian Uhlemann ◽  
Ian Shirley ◽  
...  
Keyword(s):  
Time Series ◽  
Thermal Diffusivity ◽  
Physical Properties ◽  
Environmental Conditions ◽  
Temperature Sensor ◽  
Time Window ◽  
Temperature Time Series ◽  
Soil Thermal Diffusivity ◽  
Temperature Time ◽  
Over Time

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.

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