scholarly journals A generalized model between thermal conductivity and matric suction of soils

2021 ◽  
Author(s):  
Sen Lu
Keyword(s):  
Thermal Conductivity ◽  
Soil Water ◽  
Land Surface ◽  
Matric Suction ◽  
Dew Point ◽  
Soil Thermal Conductivity ◽  
Pulse Technique ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Mean Square Errors

<p>Soil thermal conductivity (λ) is an important physical property in land surface parameterization. The soil thermal conductivity (λ) and matric suction of soil water (h, the negative of matric potential) relationship has been widely used in land surface models for estimating soil temperature and heat flux following the McCumber and Pielke (1981, MP81) λ-h model. However, few datasets are available for evaluating the accuracy and feasibility of the MP81 λ-h model under various soil and moisture conditions. In this study, we developed a new λ-h model and compared its performance with that of the MP81 model using measurements on 18 soils with a wide range of textures, water contents and bulk densities. The heat pulse technique was used to measure λ, and the suction table, micro-tensiometers, pressure plate device, and the dew point potentiometer were applied to obtain soil water retention curves at the appropriate suction ranges. In the range of pF (the common logarithm of h in cm)≤3, the λ-h relationships were highly nonlinear and varied strongly with soil texture and bulk density. In the dry range (i.e., pF > 3), there existed a universal λ-h relationship for all soil textures and bulk densities, and an exponential function was established to describe the relationship. Independent evaluations using λ-h data on five intact soil samples showed that the new model produced accurate λ data from pF values with root mean square errors (RMSE) with the range of 0.03–0.18Wm−1 K−1. While, large errors (RMSEs within 0.17–0.36Wm−1 K−1) were observed with λ estimates from the MP81 model. </p>

The relationship between thermal conductivity and matric suction of soils

2021 ◽  
Author(s):  
Sen Lu
Keyword(s):  
Thermal Conductivity ◽  
Soil Water ◽  
Land Surface ◽  
Matric Suction ◽  
Dew Point ◽  
Pulse Technique ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Mean Square Errors ◽  
The Relationship

<p>The soil thermal conductivity (λ) and matric suction of soil water (h, the negative of matric potential) relationship has been widely used in land surface models for estimating soil temperature and heat flux following the McCumber and Pielke (1981, MP81) λ-h model. However, few datasets are available for evaluating the accuracy and feasibility of the MP81 λ-h model under various soil and moisture conditions. In this study, we developed a new λ-h model and compared its performance with that of the MP81 model using measurements on 18 soils with a wide range of textures, water contents and bulk densities. The heat pulse technique was used to measure λ, and the suction table, micro-tensiometers, pressure plate device, and the dew point potentiometer were applied to obtain soil water retention curves at the appropriate suction ranges. In the range of pF (the common logarithm of h in cm)≤3, the λ-h relationships were highly nonlinear and varied strongly with soil texture and bulk density. In the dry range (i.e., pF > 3), there existed a universal λ-h relationship for all soil textures and bulk densities, and an exponential function was established to describe the relationship. Independent evaluations using λ-h data on five intact soil samples showed that the new model produced accurate λ data from pF values with root mean square errors (RMSE) with the range of 0.03–0.18W m<sup>−1</sup> K<sup>−1</sup>. While, large errors (RMSEs within 0.17–0.36W m<sup>−1</sup> K<sup>−1</sup>) were observed with λ estimates from the MP81 model. </p>

scholarly journals Coupling between the JULES land-surface scheme and the CCATT-BRAMS atmospheric chemistry model (JULES-CCATT-BRAMS1.0): applications to numerical weather forecasting and the CO<sub>2</sub> budget in South America

2013 ◽  
Vol 6 (1) ◽  
pp. 453-494 ◽  
Author(s):  
D. S. Moreira ◽  
S. R. Freitas ◽  
J. P. Bonatti ◽  
L. M. Mercado ◽  
N. M. É. Rosário ◽  
...  
Keyword(s):  
South America ◽  
Atmospheric Chemistry ◽  
Land Surface ◽  
Amazon Basin ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Dew Point ◽  
Surface Model ◽  
Co2 Concentrations ◽  
Wide Range

Abstract. This article presents the development of a new numerical system denominated JULES-CCATT-BRAMS, which resulted from the coupling of the JULES surface model to the CCATT-BRAMS atmospheric chemistry model. The performance of this system in relation to several meteorological variables (wind speed at 10 m, air temperature at 2 m, dew point temperature at 2 m, pressure reduced to mean sea level and 6 h accumulated precipitation) and the CO2 concentration above an extensive area of South America is also presented, focusing on the Amazon basin. The evaluations were conducted for two periods, the wet (March) and dry (September) seasons of 2010. The statistics used to perform the evaluation included bias (BIAS) and root mean squared error (RMSE). The errors were calculated in relation to observations at conventional stations in airports and automatic stations. In addition, CO2 concentrations in the first model level were compared with meteorological tower measurements and vertical CO2 profiles were compared with aircraft data. The results of this study show that the JULES model coupled to CCATT-BRAMS provided a significant gain in performance in the evaluated atmospheric fields relative to those simulated by the LEAF (version 3) surface model originally utilized by CCATT-BRAMS. Simulations of CO2 concentrations in Amazonia and a comparison with observations are also discussed and show that the system presents a gain in performance relative to previous studies. Finally, we discuss a wide range of numerical studies integrating coupled atmospheric, land surface and chemistry processes that could be produced with the system described here. Therefore, this work presents to the scientific community a free tool, with good performance in relation to the observed data and re-analyses, able to produce atmospheric simulations/forecasts at different resolutions, for any period of time and in any region of the globe.

scholarly journals Vegetation dynamics and soil water balance in a water-limited Mediterranean ecosystem on Sardinia, Italy

2008 ◽  
Vol 12 (6) ◽  
pp. 1257-1271 ◽  
Author(s):  
N. Montaldo ◽  
J. D. Albertson ◽  
M. Mancini
Keyword(s):  
Soil Water ◽  
Land Surface ◽  
Vegetation Dynamics ◽  
Mediterranean Ecosystems ◽  
Surface Fluxes ◽  
Annual Rainfall ◽  
Woody Vegetation ◽  
Wide Range ◽  
Hydrometeorological Conditions

Abstract. Mediterranean ecosystems are commonly heterogeneous savanna-like ecosystems, with contrasting plant functional types (PFTs, e.g. grass and woody vegetation) competing for water. Mediterranean ecosystems are also commonly characterized by strong inter-annual rainfall variability, which influences the distributions of PFTs that vary spatially and temporally. An extensive field campaign in a Mediterranean setting was performed with the objective to investigate interactions between vegetation dynamics, soil water budget and land-surface fluxes in a water-limited ecosystem. Also a vegetation dynamic model (VDM) is coupled to a 3-component (bare soil, grass and woody vegetation) Land surface model (LSM). The case study is in Orroli, situated in the mid-west of Sardegna within the Flumendosa river basin. The landscape is a mixture of Mediterranean patchy vegetation types: trees, including wild olives and cork oaks, different shrubs and herbaceous species. Land surface fluxes, soil moisture and vegetation growth were monitored during the May 2003–June 2006 period. Interestingly, hydrometeorological conditions of the monitored years strongly differ, with dry and wet years in turn, such that a wide range of hydrometeorological conditions can be analyzed. The coupled VDM-LSM model is successfully tested for the case study, demonstrating high model performance for the wide range of eco-hydrologic conditions. Results demonstrate also that vegetation dynamics are strongly influenced by the inter-annual variability of atmospheric forcing, with grass leaf area index changing significantly each spring season according to seasonal rainfall amount.

Adaptive Thermal Conductivity Metamaterials: Enabling Active and Passive Thermal Control

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Austin A. Phoenix ◽  
Evan Wilson
Keyword(s):  
Thermal Conductivity ◽  
Thermal Management ◽  
Passive Control ◽  
Coefficient Of Thermal Expansion ◽  
Thermal Strain ◽  
Thermal Control ◽  
Variable Thermal Conductivity ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Nonlinear Thermal Conductivity

The novel adaptive thermal metamaterial developed in this paper provides a unique thermal management capability that can address the needs of future spacecraft. While advances in metamaterials have provided the ability to generate materials with a broad range of material properties, relatively little advancement has been made in the development of adaptive metamaterials. This metamaterial concept enables the development of materials with a highly nonlinear thermal conductivity as a function of temperature. Through enabling active or passive control of the metamaterials bulk effective thermal conductivity, this metamaterial that can improve the spacecraft's thermal management systems performance. This variable thermal conductivity is achieved through induced contact that results in changes in the F path length and the conductive path area. The contact can be generated internally using thermal strain from shape memory alloys, bimetal springs, and mismatches in coefficient of thermal expansion (CTE) or it can be generated externally using applied mechanical loading. The metamaterial can actively control the temperature of an interface by dynamically changing the bulk thermal conductivity controlling the instantaneous heat flux through the metamaterial. The design of thermal stability regions (regions of constant thermal conductivity versus temperature) into the nonlinear thermal conductivity as a function of temperature can provide passive thermal control. While this concept can be used in a wide range of applications, this paper focuses on the development of a metamaterial that achieves highly nonlinear thermal conductivity as a function of temperature to enable passive thermal control of spacecraft systems on orbit.

scholarly journals On parameterization of heat conduction in coupled soil water and heat flow modelling

2012 ◽  
Vol 7 (No. 4) ◽  
pp. 125-137 ◽  
Author(s):  
J. Votrubová ◽  
M. Dohnal ◽  
T. Vogel ◽  
M. Tesař
Keyword(s):  
Thermal Conductivity ◽  
Soil Water ◽  
Heat Transport ◽  
Preferential Flow ◽  
Thermal Conditions ◽  
Industrial Applications ◽  
Soil Thermal Conductivity ◽  
Vegetation Season ◽  
Simulation Results ◽  
The Impact

Soil water and heat transport plays an important role in various hydrologic, agricultural, and industrial applications. Accordingly, an increasing attention is paid to relevant simulation models. In the present study, soil thermal conditions at a mountain meadow during the vegetation season were simulated. A dual-continuum model of coupled water and heat transport was employed to account for preferential flow effects. Data collected at an experimental site in the &Scaron;umava Mountains, southern Bohemia, during the vegetation season 2009 were employed. Soil hydraulic properties (retention curve and hydraulic conductivity) determined by independent soil tests were used. Unavailable hydraulic parameters were adjusted to obtain satisfactory hydraulic model performance. Soil thermal properties were estimated based on values found in literature without further optimization. Three different approaches were used to approximate the soil thermal conductivity function, &lambda;(&theta;): (i) relationships provided by Chung and Horton (ii) linear estimates as described by Loukili, Woodbury and Snelgrove, (iii) methodology proposed by C&ocirc;t&eacute; and Konrad. The simulated thermal conditions were compared to those observed. The impact of different soil thermal conductivity approximations on the heat transport simulation results was analysed. The differences between the simulation results in terms of the soil temperature were small. Regarding the surface soil heat flux, these differences became substantial. More realistic simulations were obtained using &lambda;(&theta;) estimates based on the soil texture and composition. The differences between these two, related to neglecting vs. considering &lambda;(&theta;) non-linearity, were found negligible.

The effect of soil temperature and soil water content on thermal properties in an artificial forestland and a natural regrowth grassland

2020 ◽  
Author(s):  
Tangtang Zhang ◽  
Xin Ma
Keyword(s):  
Thermal Conductivity ◽  
Thermal Properties ◽  
Soil Temperature ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Condition ◽  
Soil Thermal Conductivity ◽  
Soil Thermal Properties ◽  
Increasing Temperature

&lt;p&gt;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.&lt;/p&gt;

Soil thermal conductivity dataset

2021 ◽  
Author(s):  
Hailong He
Keyword(s):  
Thermal Conductivity ◽  
Land Surface ◽  
Pulse Method ◽  
Time Domain Reflectometry ◽  
Soil Thermal Conductivity ◽  
Thermal Probe ◽  
Line Heat Source ◽  
Steady State Method ◽  
Needle Probe ◽  
Transient Heat Flow

&lt;p&gt;Soil thermal conductivity (STC) is required parameter for coupled water and heat transport for land surface models. However, unlike soil hydraulic properties, no global dataset is available for STC. The objective of this study was to collate literature data and to take new measurements in order to establish a big STC dataset that would facilitate the evaluation and development of STC models. We collected over 8000 STC measurements made on over 400 soil types around the world following rigid filtering criteria and processes. All the STC data in the dataset were based on transient-heat-flow methods (e.g., non-steady-state method, line-heat source, needle probe, thermal probe, dual and single probe heat pulse method, thermo-time domain reflectometry). Each soil contains at least five water contents in addition to known soil physical properties such as texture and bulk density. This presentation will give a brief introduction about the STC dataset as well as call for contributions to it.&lt;/p&gt;

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 Calibration of non-ideal thermal conductivity sensors

2013 ◽  
Vol 2 (1) ◽  
pp. 151-156 ◽  
Author(s):  
N. I. Kömle ◽  
W. Macher ◽  
G. Kargl ◽  
M. S. Bentley
Keyword(s):  
Thermal Conductivity ◽  
Granular Material ◽  
Temperature Measurement ◽  
Temperature Response ◽  
Electrical Current ◽  
Diameter Ratio ◽  
Conductivity Sensor ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Length To Diameter Ratio

Abstract. A popular method for measuring the thermal conductivity of solid materials is the transient hot needle method. It allows the thermal conductivity of a solid or granular material to be evaluated simply by combining a temperature measurement with a well-defined electrical current flowing through a resistance wire enclosed in a long and thin needle. Standard laboratory sensors that are typically used in laboratory work consist of very thin steel needles with a large length-to-diameter ratio. This type of needle is convenient since it is mathematically easy to derive the thermal conductivity of a soft granular material from a simple temperature measurement. However, such a geometry often results in a mechanically weak sensor, which can bend or fail when inserted into a material that is harder than expected. For deploying such a sensor on a planetary surface, with often unknown soil properties, it is necessary to construct more rugged sensors. These requirements can lead to a design which differs substantially from the ideal geometry, and additional care must be taken in the calibration and data analysis. In this paper we present the performance of a prototype thermal conductivity sensor designed for planetary missions. The thermal conductivity of a suite of solid and granular materials was measured both by a standard needle sensor and by several customized sensors with non-ideal geometry. We thus obtained a calibration curve for the non-ideal sensors. The theory describing the temperature response of a sensor with such unfavorable length-to-diameter ratio is complicated and highly nonlinear. However, our measurements reveal that over a wide range of thermal conductivities there is an almost linear relationship between the result obtained by the standard sensor and the result derived from the customized, non-ideal sensors. This allows for the measurement of thermal conductivity values for harder soils, which are not easily accessible when using standard needle sensors.

Sign in / Sign up

Export Citation Format

Share Document