scholarly journals Effects of Soil Moisture on the Responses of Soil Temperatures to Climate Change in Cold Regions*

2013 ◽  
Vol 26 (10) ◽  
pp. 3139-3158 ◽  
Author(s):  
Zachary M. Subin ◽  
Charles D. Koven ◽  
William J. Riley ◽  
Margaret S. Torn ◽  
David M. Lawrence ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Soil Moisture ◽  
Active Layer ◽  
Pore Space ◽  
Heat Of Fusion ◽  
Soil Thermal Conductivity ◽  
Temperature Changes ◽  
Community Land Model ◽  
Permafrost Soils ◽  
Soil Temperatures

Abstract At high latitudes, changes in soil moisture could alter soil temperatures independently of air temperature changes by interacting with the snow thermal rectifier. The authors investigated this mechanism with model experiments in the Community Land Model 4 (CLM4) with prescribed atmospheric forcing and vegetation state. Under equilibrium historical conditions, increasing CO2 concentrations experienced by plants from 285 to 857 ppm caused local increases in soil water-filled pore space of 0.1–0.2 in some regions throughout the globe. In permafrost regions that experienced this moistening, vertical- and annual- mean soil temperatures increased by up to 3°C (0.27°C averaged over all permafrost areas). A similar pattern of moistening and consequent warming occurred in simulations with prescribed June–September (JJAS) rainfall increases of 25% over historical values, a level of increase commensurate with projected future rainfall increases. There was a strong sensitivity of the moistening responses to the baseline hydrological state. Experiments with perturbed physics confirmed that the simulated warming in permafrost soils was caused by increases in the soil latent heat of fusion per unit volume and in the soil thermal conductivity due to the increased moisture. In transient Representative Concentration Pathway 8.5 (RCP8.5) scenario experiments, soil warming due to increased CO2 or JJAS rainfall was smaller in magnitude and spatial extent than in the equilibrium experiments. Active-layer deepening associated with soil moisture changes occurred over less than 8% of the current permafrost area because increased heat of fusion and soil thermal conductivity had compensating effects on active-layer depth. Ongoing modeling challenges make these results tentative.

scholarly journals Sensitivity of Near-Surface Temperature Forecasts to Soil Properties over a Sparsely Vegetated Dryland Region

2014 ◽  
Vol 53 (8) ◽  
pp. 1976-1995 ◽  
Author(s):  
Jeffrey D. Massey ◽  
W. James Steenburgh ◽  
Sebastian W. Hoch ◽  
Jason C. Knievel
Keyword(s):  
Thermal Conductivity ◽  
Soil Moisture ◽  
Sandy Loam ◽  
Early Morning ◽  
Surface Model ◽  
Silt Loam ◽  
Soil Thermal Conductivity ◽  
Near Surface ◽  
Ground Heat Flux ◽  
Loam Soil

AbstractWeather Research and Forecasting Model forecasts over the Great Salt Lake Desert erroneously underpredict nocturnal cooling over the sparsely vegetated silt loam soil area of Dugway Proving Ground in northern Utah, with a mean positive bias error in temperature at 2 m AGL of 3.4°C in the early morning [1200 UTC (0500 LST)]. Positive early-morning bias errors also exist in nearby sandy loam soil areas. These biases are related to the improper initialization of soil moisture and parameterization of soil thermal conductivity in silt loam and sandy loam soils. Forecasts of 2-m temperature can be improved by initializing with observed soil moisture and by replacing Johansen's 1975 parameterization of soil thermal conductivity in the Noah land surface model with that proposed by McCumber and Pielke in 1981 for silt loam and sandy loam soils. Case studies illustrate that this change can dramatically reduce nighttime warm biases in 2-m temperature over silt loam and sandy loam soils, with the greatest improvement during periods of low soil moisture. Predicted ground heat flux, soil thermal conductivity, near-surface radiative fluxes, and low-level thermal profiles also more closely match observations. Similar results are anticipated in other dryland regions with analogous soil types, sparse vegetation, and low soil moisture.

Effect of Soil Moisture on Soil Temperature Field Near Buried Pipe

2012 ◽  
Vol 204-208 ◽  
pp. 650-653
Author(s):  
Jiang Li ◽  
Jun Ping Fu ◽  
Wu Gang Xie
Keyword(s):  
Thermal Conductivity ◽  
Temperature Field ◽  
Soil Moisture ◽  
Heat Exchanger ◽  
Moisture Content ◽  
Field Experiment ◽  
Soil Temperature ◽  
Soil Moisture Content ◽  
Thermal Conductivity Coefficient ◽  
Soil Thermal Conductivity

System effectiveness and useful life of heat pump are directly affected by whether the design of ground heat exchanger is reasonable or not. The efficiency of heat exchanger has a close relationship with soil thermal conductivity coefficient and heat diffusivity, while soil moisture content affects soil thermal conductivity coefficient and soil temperature field. In this paper, we perform numerical simulation on CFD software. Then we study the soil temperature changes through field experiment in different soil moisture content on field experiment and finally obtained the relationships of the moisture content with the single U ground soil temperature field.

scholarly journals An improved representation of physical permafrost dynamics in the JULES land surface model

2015 ◽  
Vol 8 (1) ◽  
pp. 715-759 ◽  
Author(s):  
S. Chadburn ◽  
E. Burke ◽  
R. Essery ◽  
J. Boike ◽  
M. Langer ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Active Layer ◽  
Land Surface ◽  
Climate Models ◽  
Surface Soil ◽  
Active Layer Thickness ◽  
Mean Square ◽  
Near Surface ◽  
Soil Temperatures

Abstract. It is important to correctly simulate permafrost in global climate models, since the stored carbon represents the source of a potentially important climate feedback. This carbon feedback depends on the physical state of the permafrost. We have therefore included improved physical permafrost processes in JULES, which is the land-surface scheme used in the Hadley Centre climate models. The thermal and hydraulic properties of the soil were modified to account for the presence of organic matter, and the insulating effects of a surface layer of moss were added, allowing for fractional moss cover. We also simulate a higher-resolution soil column and deeper soil, and include an additional thermal column at the base of the soil to represent bedrock. In addition, the snow scheme was improved to allow it to run with arbitrarily thin layers. Point-site simulations at Samoylov Island, Siberia, show that the model is now able to simulate soil temperatures and thaw depth much closer to the observations. The root mean square error for the near-surface soil temperatures reduces by approximately 30%, and the active layer thickness is reduced from being over 1 m too deep to within 0.1 m of the observed active layer thickness. All of the model improvements contribute to improving the simulations, with organic matter having the single greatest impact. A new method is used to estimate active layer depth more accurately using the fraction of unfrozen water. Soil hydrology and snow are investigated further by holding the soil moisture fixed and adjusting the parameters to make the soil moisture and snow density match better with observations. The root mean square error in near-surface soil temperatures is reduced by a further 20% as a result.

scholarly journals Extreme gradient boosting regression model for soil thermal conductivity

2021 ◽  
Vol 25 (Spec. issue 1) ◽  
pp. 1-7
Author(s):  
Ahmet Yurttakal
Keyword(s):  
Thermal Conductivity ◽  
Soil Moisture ◽  
Regression Model ◽  
Test Data ◽  
Granular Structure ◽  
Gradient Boosting ◽  
Soil Thermal Conductivity ◽  
Extreme Gradient Boosting ◽  
Complicated Process ◽  
Boosting Algorithm

The thermal conductivity estimation for the soil is an important step for many geothermal applications. But it is a difficult and complicated process since it involves a variety of factors that have significant effects on the thermal conductivity of soils such as soil moisture and granular structure. In this study, regression was performed with the extreme gradient boosting algorithm to develop a model for estimating thermal conductivity value. The performance of the model was measured on the unseen test data. As a result, the proposed algorithm reached 0.18 RMSE, 0.99 R2, and 3.18% MAE values which state that the algorithm is encouraging.

scholarly journals An improved representation of physical permafrost dynamics in the JULES land-surface model

2015 ◽  
Vol 8 (5) ◽  
pp. 1493-1508 ◽  
Author(s):  
S. Chadburn ◽  
E. Burke ◽  
R. Essery ◽  
J. Boike ◽  
M. Langer ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Active Layer ◽  
Land Surface ◽  
Climate Models ◽  
Surface Soil ◽  
Active Layer Thickness ◽  
Mean Square ◽  
Near Surface ◽  
Soil Temperatures

Abstract. It is important to correctly simulate permafrost in global climate models, since the stored carbon represents the source of a potentially important climate feedback. This carbon feedback depends on the physical state of the permafrost. We have therefore included improved physical permafrost processes in JULES (Joint UK Land Environment Simulator), which is the land-surface scheme used in the Hadley Centre climate models. The thermal and hydraulic properties of the soil were modified to account for the presence of organic matter, and the insulating effects of a surface layer of moss were added, allowing for fractional moss cover. These processes are particularly relevant in permafrost zones. We also simulate a higher-resolution soil column and deeper soil, and include an additional thermal column at the base of the soil to represent bedrock. In addition, the snow scheme was improved to allow it to run with arbitrarily thin layers. Point-site simulations at Samoylov Island, Siberia, show that the model is now able to simulate soil temperatures and thaw depth much closer to the observations. The root mean square error for the near-surface soil temperatures reduces by approximately 30%, and the active layer thickness is reduced from being over 1 m too deep to within 0.1 m of the observed active layer thickness. All of the model improvements contribute to improving the simulations, with organic matter having the single greatest impact. A new method is used to estimate active layer depth more accurately using the fraction of unfrozen water. Soil hydrology and snow are investigated further by holding the soil moisture fixed and adjusting the parameters to make the soil moisture and snow density match better with observations. The root mean square error in near-surface soil temperatures is reduced by a further 20% as a result.

scholarly journals Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11040
Author(s):  
Xiaogang Ma ◽  
Jiming Jin ◽  
Lingjing Zhu ◽  
Jian Liu
Keyword(s):  
Thermal Conductivity ◽  
Tibetan Plateau ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Soil Layer ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
Soil Thermal Conductivity ◽  
Community Land Model

This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0–10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.

scholarly journals Temperature Changes in the Vicinity of Thermally Loaded Structure Embedded in the Soil: Effect of Sand Content and Saturation Degree

2017 ◽  
Vol 39 (2) ◽  
pp. 61-71
Author(s):  
Adrian Różański
Keyword(s):  
Thermal Conductivity ◽  
Solid Phase ◽  
Rapid Development ◽  
Degree Of Saturation ◽  
Soil Thermal Conductivity ◽  
Soil Medium ◽  
Temperature Changes ◽  
Soil Skeleton ◽  
Soil Conductivity ◽  
Loaded Structure

Abstract Due to the rapid development of geothermal technologies, the problem of efficient and proper evaluation of soil thermal conductivity becomes extremely important. Factors mostly affecting the soil conductivity are the conductivity of solid phase and the degree of saturation. The former one is mainly affected by the mineral composition, in particular, by the content of quartz whose conductivity is the highest one among all the minerals forming soil skeleton. Organic matter, because of its relatively low conductivity, influences the solid conductivity as well. The problem addressed in the paper is the influence of mentioned factors on temperature changes in the vicinity of thermally loaded structure embedded in the soil medium. Numerical simulations are carried out for different values of soil thermal conductivity resulting from various quartz contents and degrees of saturation. In addition, a weak coupled - heat and water transport - problem is considered.

scholarly journals On a problems related to a concept of soil thermal diffusivity and estimation of its dependence on soil moisture

2020 ◽  
Vol 10 (2) ◽  
pp. 68-85
Author(s):  
M. V. Glagolev ◽  
A. F. Sabrekov
Keyword(s):  
Thermal Conductivity ◽  
Soil Moisture ◽  
Thermal Diffusivity ◽  
Heat Equation ◽  
Least Squares Method ◽  
Thermal Capacity ◽  
Diffusivity Coefficient ◽  
Soil Thermal Conductivity ◽  
Thermal Diffusivity Coefficient ◽  
Soil Thermal Diffusivity

Two problems in the theory of soil thermal conductivity are considered. First, the concept of the thermal diffusivity coefficient is discussed. It was shown that this coefficient can be used for model predictions only in a certain special cases. In the general case (when the soil thermal capacity and thermal conductivity vary in space and/or in time), the thermal diffusivity does not naturally appear. It could be artificially introduced into the heat equation but, in any case, to solve this equation (i.e., to calculate the dynamics of the soil temperature), this one parameter is not sufficient. It is necessary to set both the heat capacity and thermal conductivity as a functions of spatial and temporal coordinates or as a functions of environmental factors (e.g. soil moisture) depending on these coordinates. In this regard, the widespread misconception of the supposed sufficiency of one parameter (soil thermal diffusivity as a ratio of soil thermal conductivity to thermal capacity) for solving the heat equation using numerical methods is discussed. The examples of the common difference schemes used in computational practice show that this is not the case. Secondly, the condition number for the problem of parameters identification for the dependence of the soil thermal diffusivity coefficient on humidity for one well-known equation is considered. It is shown on real examples, that this problem is often ill-conditioned when solved by the least-squares method. However, sometimes its stability can be significantly improved if simple constraints are set for certain parameters (least-squares method with constraints). В работе рассматриваются две проблемы, возникающие в теории теплопроводности почв. Во-первых, обсуждается понятие коэффициента температуропроводности в свете того, что оно появляется только в отдельных весьма частных случаях, а в общем случае (когда теплоемкость и теплопроводность изменяются по пространству и/или с течением времени) коэффициент температуропроводности естественным образом вообще не возникает. Для такой среды с переменными (по пространству и во времени) свойствами он может быть искусственно введен в уравнение динамики температурного поля, но, в любом случае, для решения этого уравнения (т.е. для расчета динамики температурного поля) недостаточно одного параметра необходимо задать и теплоемкость, и теплопроводность как функции пространственной и временной координат или как функции факторов среды (например, влажности), зависящих от этих координат. В связи с этим обсуждается и распространенное заблуждение о якобы достаточности одного параметра (коэффициента температуропроводности как отношения теплопроводности к теплоемкости) при решении вышеуказанного уравнения численными методами. На примерах основных разностных схем, применяемых в вычислительной практике, показано, что это не так. Во-вторых, рассматривается число обусловленности задачи идентификации параметров одного изветного уравнения зависимости коэффициента температуропроводности от влажности. На конкретных примерах показано, что данная задача при ее решении обычным методом наименьших квадратов часто является плохо обусловленной. Однако иногда ее обусловленность удается существенно улучшить при наложении простейших ограничений на искомые параметры (метод наименьших квадратов с ограничениями). Текст статьи на русском языке см. на вкладке Дополнительные файлы

scholarly journals Impact of gravels and organic matter on the thermal properties of grassland soils in southern France

2015 ◽  
Vol 2 (1) ◽  
pp. 737-765
Author(s):  
J.-C. Calvet ◽  
N. Fritz ◽  
C. Berne ◽  
B. Piguet ◽  
W. Maurel ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Organic Matter ◽  
Soil Moisture ◽  
Soil Organic Matter ◽  
Soil Temperature ◽  
Soil Thermal Conductivity ◽  
Volumetric Fraction ◽  
Southern France ◽  
The Impact ◽  
Conductivity Models

Abstract. Soil moisture is the main driver of temporal changes in values of the soil thermal conductivity. The latter is a key variable in land surface models (LSMs) used in hydrometeorology, for the simulation of the vertical profile of soil temperature in relation to soil moisture. Shortcomings in soil thermal conductivity models tend to limit the impact of improving the simulation of soil moisture in LSMs. Models of the thermal conductivity of soils are affected by uncertainties, especially in the representation of the impact of soil properties such as the volumetric fraction of quartz (q), soil organic matter, and gravels. As soil organic matter and gravels are often neglected in LSMs, the soil thermal conductivity models used in most LSMs represent the mineral fine earth, only. Moreover, there is no map of q and it is often assumed that this quantity is equal to the volumetric fraction of sand. In this study, q values are derived by reverse modelling from the continuous soil moisture and soil temperature sub-hourly observations of the Soil Moisture Observing System – Meteorological Automatic Network Integrated Application (SMOSMANIA) network at 21 grassland sites in southern France, from 2008 to 2015. The soil temperature observations are used to retrieve the soil thermal diffusivity (Dh) at a depth of 0.10 m in unfrozen conditions, solving the thermal diffusion equation. The soil moisture and Dh values are then used together with the measured soil properties to retrieve soil thermal conductivity (λ) values. For ten sites, the obtained λ value at saturation (λsat) cannot be retrieved or is lower than the value corresponding to a null value of q, probably in relation to a high density of grass roots at these sites or to the presence of stones. For the remaining eleven sites, q is negatively correlated with the volumetric fraction of solids other than sand. The impact of neglecting gravels and organic matter on λsat is assessed. It is shown that these factors have a major impact on λsat.

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