Estimation of ground heat flux from soil temperature over a bare soil

Abstract

Ground soil heat flux, G 0 , is a difficult-to-measure but important component of the surface energy budget. Over the past years, many methods were proposed to estimate G 0 ; however, the application of these methods was seldom validated and assessed under different weather conditions. In this study, three popular models (force-restore, conduction-convection, and harmonic) and one widely used method (plate calorimetric), which had well performance in publications, were investigated using field data to estimate daily G 0 on clear, cloudy, and rainy days, while the gradient calorimetric method was regarded as the reference for assessing the accuracy. The results showed that harmonic model was well reproducing the G 0 curve for clear days, but it yielded large errors on cloudy and rainy days. The force-restore model worked well only under rainfall condition, but it was poor to estimate G 0 under rain-free conditions. On the contrary, the conduction-convection model was acceptable to determine G 0 under rain-free conditions, but it generated large errors on rainfall days. More importantly, the plate calorimetric method was the best to estimate G 0 under different weather conditions compared with the three models, but the performance of this method is affected by the placement depth of the heat flux plate. As a result, the heat flux plate was recommended to be buried as close as possible to the surface under clear condition. But under cloudy and rainy conditions, the plate placed at depth of around 0.075 m yielded G 0 well. Overall, the findings of this paper provide guidelines to acquire more accurate estimation of G 0 under different weather conditions, which could improve the surface energy balance in field.

A Numerical Model to Estimate the Soil Thermal Conductivity Using Field Experimental Data

“…To avoid possible divisions by zero using Equation 2, different works in the literature estimate the soil thermal conductivity using, for example, daily averages [4,18] or specific times [18]; some even eliminate data in which the temperatures are approaching zero [19]; and, others eliminate soil heat flux data near signal changes (before and after) because these values are relatively small and consequently increasing errors [19,30]. In contrast to these techniques, in this work, we propose the use of the Linear Least Squares Method (LLSM) to obtain the coefficient of soil thermal conductivity and the additional term (ε) presented in Equation (3).…”

“…The complexity of soil makes its thermal conductivity dependent on physical or chemical properties, such as soil water content, porosity, density, texture, and mineral composition [1]. Different empirical models have been suggested for the estimation of soil thermal conductivity, taking into account these properties [2][3][4][5][6][7][8]. However, these models are mainly obtained using data from controlled experiments in the laboratory by measurement techniques including steady-state methods such as the guarded hot plate method [9], transient methods including the single line heat source probe [10,11] and the dual-probe heat-pulse method [12][13][14].…”

“…However, because the data are discrete (measured in time intervals), the soil temperature gradient, ∂T ∂z , can be obtained by the variation rate between the soil temperature and the depth, ∆T ∆z = T 2 −T 1 z 2 −z 1 . This methodology is called the Gradient Method [4,5,[16][17][18][19] Therefore, if we know the measurement of the soil heat flux at a given depth z (G z ) and the soil temperature at depths relatively close to G z , Equation (1) can be written as:…”

Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data

“…The estimation of G at local scales is generally performed with the Harmonic method, based on the Fourier series [11][12][13][14][15][16]. However, the Harmonic method cannot estimate the G for large areas, since it requires soil properties data (i.e., soil conductivity and temperature, at different soil depths), which are only measured at specific sites; usually with other SEB fluxes, in Eddy-Covariance (EC) towers.…”