scholarly journals An Empirical Model for Estimating Soil Thermal Diffusivity from Texture, Bulk Density, and Degree of Saturation

2018 ◽  
Vol 19 (2) ◽  
pp. 445-457 ◽  
Author(s):  
Xiaoting Xie ◽  
Yili Lu ◽  
Tusheng Ren ◽  
Robert Horton
Keyword(s):  
Thermal Diffusivity ◽  
Bulk Density ◽  
Empirical Model ◽  
Soil Texture ◽  
Degree Of Saturation ◽  
Model Parameters ◽  
Near Surface ◽  
New Model ◽  
Soil Thermal Diffusivity ◽  
Mean Square Errors

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.

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
Keyword(s):  
Thermal Conductivity ◽  
Water Content ◽  
Empirical Model ◽  
Large Scale ◽  
Soil Heat Flux ◽  
Soil Thermal Conductivity ◽  
New Model ◽  
Scale Models ◽  
Complex Models ◽  
Mean Square Errors

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.

scholarly journals Estimating Soil Thermal Diffusivity Using Pedotransfer Functions with Nonlinear Regression

2018 ◽  
Vol 12 (1) ◽  
pp. 164-173
Author(s):  
Ahmed Yehia Mady ◽  
Evgeny Shein
Keyword(s):  
Organic Matter ◽  
Thermal Diffusivity ◽  
Water Content ◽  
Bulk Density ◽  
Nonlinear Regression ◽  
Quadratic Equation ◽  
Soil Physical Properties ◽  
Pedotransfer Functions ◽  
Soil Thermal Diffusivity ◽  
Exponential Equations

Background and Objective:Pedotransfer Functions (PTFs) are widely used for estimating soil thermal diffusivity. Some attempts have been made to indirectly predict soil thermal diffusivity from the easy available fundamental soil physics properties. The aim of the work was to validate usage PTFs with Nonlinear Regression (NLR) for estimating soil thermal diffusivity (KD), moreover was to select the best predictor variables used for determination of PTFs.Materials and Methods:Soil thermal diffusivity was measured at different values of water content using Kondratieff method. The parameters of the quadratic equation, which described the relation between thermal diffusivity and water content, were determined by the fitting curve and using PTFs (exponential equations) based on soil physical properties. The Combination of different soil physical properties used as PTF model’s independent variables was tested. Three classes of PTFs were proposed using NLR to estimate KDwere: KDPTF-1 (Sand+ Silt+ Clay), KDPTF-2 (Sand+ Silt+ Clay + Bulk density), and KDPTF-3 (Sand+ Silt+ Clay+ Bulk density + Organic matter).Results:The best class of PTF could be used for calculating the parameters of the quadratic equation and soil thermal diffusivity, was KDPTF-1 which taking into account the percentage of sand, silt and clay, RMSE=2.94×10-8m2/s, and GMER =1.05.Conclusion:The quadratic and exponential equations were representing the nonlinear regression equations, which could be used for estimating soil thermal diffusivity at different values of water content from easily available data on soil texture, bulk density, and organic matter content.

A generally applicable pedotransfer function that estimates field capacity and permanent wilting point from soil texture and bulk density

2008 ◽  
Vol 88 (5) ◽  
pp. 761-774 ◽  
Author(s):  
J. A. P. Pollacco
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Bulk Density ◽  
Soil Texture ◽  
The United States ◽  
Pedotransfer Functions ◽  
Pedotransfer Function ◽  
Data Set ◽  
Wide Range ◽  
Fitting Parameters

Hydrological models require the determination of fitting parameters that are tedious and time consuming to acquire. A rapid alternative method of estimating the fitting parameters is to use pedotransfer functions. This paper proposes a reliable method to estimate soil moisture at -33 and -1500 kPa from soil texture and bulk density. This method reduces the saturated moisture content by multiplying it with two non-linear functions depending on sand and clay contents. The novel pedotransfer function has no restrictions on the range of the texture predictors and gives reasonable predictions for soils with bulk density that varies from 0.25 to 2.16 g cm-3. These pedotransfer functions require only five parameters for each pressure head. It is generally accepted that the introduction of organic matter as a predictor improves the outcomes; however it was found by using a porosity based pedotransfer model, using organic matter as a predictor only modestly improves the accuracy. The model was developed employing 18 559 samples from the IGBP-DIS soil data set for pedotransfer function development (Data and Information System of the International Geosphere Biosphere Programme) database that embodies all major soils across the United States of America. The function is reliable and performs well for a wide range of soils occurring in very dry to very wet climates. Climatical grouping of the IGBP-DIS soils was proposed (aquic, tropical, cryic, aridic), but the results show that only tropical soils require specific grouping. Among many other different non-climatical soil groups tested, only humic and vitric soils were found to require specific grouping. The reliability of the pedotransfer function was further demonstrated with an independent database from Northern Italy having heterogeneous soils, and was found to be comparable or better than the accuracy of other pedotransfer functions found in the literature. Key words: Pedotransfer functions, soil moisture, soil texture, bulk density, organic matter, grouping

Predicting the longitudinal modulus of elasticity of Sitka spruce from cellulose orientation and abundance

Holzforschung ◽  
2010 ◽  
Vol 64 (4) ◽  
Author(s):  
J. Paul McLean ◽  
Robert Evans ◽  
John R. Moore
Keyword(s):  
Bulk Density ◽  
Modulus Of Elasticity ◽  
Microfibril Angle ◽  
Sitka Spruce ◽  
Picea Sitchensis ◽  
Model Parameters ◽  
Bending Tests ◽  
Wood Stiffness ◽  
Using Data ◽  
Longitudinal Modulus

Abstract Sitka spruce (Picea sitchensis) is the most widely planted commercial tree species in the United Kingdom and Ireland. Because of the increasing use of this species for construction, the ability to predict wood stiffness is becoming more important. In this paper, a number of models are developed using data on cellulose abundance and orientation obtained from the SilviScan-3 system to predict the longitudinal modulus of elasticity (MOE) of small defect-free specimens. Longitudinal MOE was obtained from both bending tests and a sonic resonance technique. Overall, stronger relationships were found between the various measures of cellulose abundance and orientation and the dynamic MOE obtained from the sonic resonance measurements, rather than with the static MOE obtained from bending tests. There was only a moderate relationship between wood bulk density and dynamic MOE (R2=0.423), but this relationship was improved when density was divided by microfibril angle (R2=0.760). The best model for predicting both static and dynamic MOE involved the product of bulk density and the coefficient of variation in the azimuthal intensity profile (R2=0.725 and 0.862, respectively). The model parameters obtained for Sitka spruce differed from those obtained in earlier studies on Pinus radiata and Eucalyptus delegatensis, indicating that the model might require recalibration before it can be applied to different species.

A new sedimentary cover model for the southern area of the East European Platform and the Pre-Caucasus based on decompensation gravity anomalies data

2021 ◽  
Author(s):  
Mikhail Kaban ◽  
Alexei Gvishiani ◽  
Roman Sidorov ◽  
Alexei Oshchenko ◽  
Roman Krasnoperov
Keyword(s):  
East European Platform ◽  
Sedimentary Cover ◽  
Gravity Anomalies ◽  
Sedimentary Basins ◽  
The Caucasus ◽  
Near Surface ◽  
New Model ◽  
Origin And Evolution ◽  
East European ◽  
Bouguer Anomalies

<p><span>A new model has been developed for the density and thickness of the sedimentary cover in a vast region at the junction of the southern part of the East European Platform, the Pre-Caucasus and some structures adjacent to the south, including the Caucasus. Structure and density of sedimentary basins was studied by employing the approach based on decompensation of gravity anomalies. Decompensative correction for gravity anomalies reduces the effect of deep masses providing compensation of near-surface density anomalies, in contrast to the conventional isostatic or Bouguer anomalies. . The new model of sediments, which implies their thickness and density, gives a more detailed description of the sedimentary thickness and density and reveals new features which were not or differently imaged by previous studies. It helps in better understanding of the origin and evolution of the basins and provides a background for further detailed geological and geophysical studies of the region.</span></p>

FULL-WAVEFORM INVERSION WITH SOURCE AND RECEIVER COUPLING EFFECTS CORRECTION

Geophysics ◽  
2021 ◽  
pp. 1-37
Author(s):  
Xinhai Hu ◽  
Wei Guoqi ◽  
Jianyong Song ◽  
Zhifang Yang ◽  
Minghui Lu ◽  
...  
Keyword(s):  
Waveform Inversion ◽  
Nonlinear Least Squares ◽  
Real Data ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Linear Inversion ◽  
Near Surface ◽  
Model Parameter ◽  
Full Waveform ◽  
Coupling Factors

Coupling factors of sources and receivers vary dramatically due to the strong heterogeneity of near surface, which are as important as the model parameters for the inversion success. We propose a full waveform inversion (FWI) scheme that corrects for variable coupling factors while updating the model parameter. A linear inversion is embedded into the scheme to estimate the source and receiver factors and compute the amplitude weights according to the acquisition geometry. After the weights are introduced in the objective function, the inversion falls into the category of separable nonlinear least-squares problems. Hence, we could use the variable projection technique widely used in source estimation problem to invert the model parameter without the knowledge of source and receiver factors. The efficacy of the inversion scheme is demonstrated with two synthetic examples and one real data test.

scholarly journals A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Lin Lin ◽  
Fang Wang ◽  
Shisheng Zhong
Keyword(s):  
Engine Performance ◽  
Grey Model ◽  
Model Parameters ◽  
Bayesian Regularization ◽  
Safety Engineering ◽  
The Novel ◽  
Mean Square ◽  
Approximation Model ◽  
Mean Square Errors ◽  
Fitting Accuracy

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

scholarly journals Thermal conductivity of unsaturated clay-rocks

2010 ◽  
Vol 14 (1) ◽  
pp. 91-98 ◽  
Author(s):  
D. Jougnot ◽  
A. Revil
Keyword(s):  
Thermal Conductivity ◽  
Porous Material ◽  
Solid Phase ◽  
Model Parameters ◽  
Electrical Parameters ◽  
New Model ◽  
Unsaturated Clay ◽  
Unsaturated Porous Material ◽  
Clay Rocks ◽  
Good Agreement

Abstract. The parameters used to describe the electrical conductivity of a porous material can be used to describe also its thermal conductivity. A new relationship is developed to connect the thermal conductivity of an unsaturated porous material to the thermal conductivity of the different phases of the composite, and two electrical parameters called the first and second Archie's exponents. A good agreement is obtained between the new model and thermal conductivity measurements performed using packs of glass beads and core samples of the Callovo-Oxfordian clay-rocks at different saturations of the water phase. We showed that the three model parameters optimised to fit the new model against experimental data (namely the thermal conductivity of the solid phase and the two Archie's exponents) are consistent with independent estimates. We also observed that the anisotropy of the effective thermal conductivity of the Callovo-Oxfordian clay-rock was mainly due to the anisotropy of the thermal conductivity of the solid phase.

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