scholarly journals An Empirical Model for Estimating Soil Thermal Conductivity from Soil Water Content and Porosity

2016 ◽  
Vol 17 (2) ◽  
pp. 601-613 ◽  
Author(s):  
Bing Tong ◽  
Zhiqiu Gao ◽  
Robert Horton ◽  
Yubin Li ◽  
Linlin Wang

Abstract Soil thermal conductivity λ is a vital parameter for soil temperature and soil heat flux forecasting in hydrological models. In this study, an empirical model is developed to relate λ only to soil volumetric water content θ and soil porosity θs. Measured λ values for eight soils are used to establish the empirical model, and data from four other soils are used to evaluate the model. The new model is also evaluated by its performance in the Simple Biosphere Model 2 (SiB2). Results show that the root-mean-square errors (RMSEs; ranging from 0.097 to 0.266 W m−1 K−1) of the new model estimates of λ are lower than those (ranging from 0.416 to 1.006 W m−1 K−1) for an empirical model of similar complexity reported in the literature earlier. Further, with simple inputs and equations, the new model almost has the accuracy of other more complex models (RMSE of λ ranging from 0.040 to 0.354 W m−1 K−1) that require additional detailed soil information. The new model can be readily incorporated in large-scale models because of its simplicity as compared to the more complex models. The new model is tested for its effectiveness by incorporating it into SiB2. Compared to the original SiB2 λ model, the new λ model provides better estimates of surface effective radiative temperature and soil wetness. Owing to the newly presented empirical model’s requirement for simple, available inputs and its accuracy, its usage is recommended within large-scale models for applications where detailed information about soil composition is lacking.

2018 ◽  
Vol 19 (2) ◽  
pp. 445-457 ◽  
Author(s):  
Xiaoting Xie ◽  
Yili Lu ◽  
Tusheng Ren ◽  
Robert Horton

Abstract Soil thermal diffusivity κ is an essential parameter for studying surface and subsurface heat transfer and temperature changes. It is well understood that κ mainly varies with soil texture, water content θ, and bulk density ρb, but few models are available to accurately quantify the relationship. In this study, an empirical model is developed for estimating κ from soil particle size distribution, ρb, and degree of water saturation Sr. The model parameters are determined by fitting the proposed equations to heat-pulse κ data for eight soils covering wide ranges of texture, ρb, and Sr. Independent evaluations with published κ data show that the new model describes the κ(Sr) relationship accurately, with root-mean-square errors less than 0.75 × 10−7 m2 s−1. The proposed κ(Sr) model also describes the responses of κ to ρb changes accurately in both laboratory and field conditions. The new model is also used successfully for predicting near-surface soil temperature dynamics using the harmonic method. The results suggest that this model provides useful estimates of κ from Sr, ρb, and soil texture.


2020 ◽  
Author(s):  
Tangtang Zhang ◽  
Xin Ma

<p>Soil temperature, soil water content and soil thermal properties were measured in an artificial forestland and a natural regrowth grassland from November in 2017 to July in 2019. The results show that the effects of soil temperature and soil water content on thermal properties are different in different soil condition. Soil thermal conductivity (K) and soil volumetric heat capacity (C) increase with increasing temperature in unfrozen period, but soil diffusivity (D) has no significant dynamic cycle and it almost keeps a constant level in a certain time. Soil thermal conductivity (K) decreases with increasing temperature during soil frozen period. The C and K increase with increasing soil water content in unfrozen period, while the D decrease with increasing soil water content.</p>


2021 ◽  
Author(s):  
Behnam Jowkar-Baniani

Comprehensive set of thermal conductivity data for a loam soil was generated, for temperature variations from 5ºC to 92ºC and water content variations from dry to saturation, and compared to two other soil textures. The results exhibited similar characteristics as those of the other textures, where a significant change in soil thermal conductivity was. Using the thermal conductivity data sets, a model representing heat and mass transfer in soil was used to study the apparent thermal conductivity due to vapour migration. In addition, a computer simulation of a ground source heat pump system was developed, where the experimental data was used to investigate the impact of water content and soil texture variation on the GSHP performance. It was observed that the GSHP energy consumption varied more prominently when the soil wetness varied from dryness to full saturation and less significantly when the soil type varied from coarse to finer texture.


Author(s):  
C. H. Yang ◽  
A. Müterthies

Abstract. Understanding soil moisture is essential for earth and environmental sciences especially in geology, hydrology, and meteorology. Remote sensing techniques are widely applied to large-scale monitoring tasks. Among them, DInSAR using multi-temporal spaceborne SAR images is able to derive surface movement up to mm level over an area. One of the factors inducing the movement is variation of soil moisture. Based on this, a semi-empirical approach can be tailored to retrieve the underground water content. However, the derived movement is often contaminated with other irrelevant noise. Besides, a time-series analysis could not be simply implemented without additional fusion and calibration. In this paper, we propose a novel modelling based on advanced DInSAR to solve these problems. The irrelevant noise will be removed as parts of the modelled elements in the DInSAR processing. A forward model on a scene is built by regressing the measured soil moisture on the DInSAR-derived movement series. We tested our approach using Sentinel-1 images in the grasslands of organic soil within State of Brandenburg, Germany. The Pearson correlation coefficients between the measured soil moistures and the DInSAR-derived movements are up to 0.91. The mean square errors of the predicted soil moistures compared with the measurements reach 3.03 % (volumetric water content) at best. Our study shows a promising new concept to develop a global monitoring of soil moisture in the future.


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