Comparative study of the soil thermal regime in arid and semi-humid areas

Li, Zhenchao; Yang, Jun; Zheng, Zhong; Yu, Ye; Zhang, Tangtang; Hou, Xuhong; Wei, Zhigang

doi:10.1007/s12665-016-6354-2

Cited by 15 publications

(15 citation statements)

References 19 publications

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“…Surface spectral albedo outliers are generally high in winter as shown in figure 3. There were two snowfall events in February 2018 (9th and 19th, respectively), and snow cover considerably affects surface albedo since the albedo of snow is much higher than other land cover types (Li et al 2017a(Li et al , 2017b. The observed mean surface albedo of GR, UV, VIS and NIR reached 0.25, 0.12, 0.26 and 0.26, respectively, in winter.…”

Section: The Characteristics Of Surface Spectral Albedomentioning

confidence: 97%

“…The stable properties of the land surface in the Gobi allowed us to eliminate the inherent changes of surface albedo. The annual mean precipitation of the experiment site is only 39 mm and the potential evaporation reaches 3400 mm (Li et al 2017a(Li et al , 2017b. Such stable conditions of aerosol presence, cloud cover and other weather changes also helped minimize unwanted influences in the experiment.…”

Section: Design Of the Experimentsmentioning

confidence: 98%

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The influence of soil moisture and solar altitude on surface spectral albedo in arid area

Yang

Zhai

et al. 2020

Environ. Res. Lett.

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Using data collected from a specially designed experiment at the Dunhuang Station (40°10′N, 94°31′ E, 1150 m) from September 2017 to September 2018, we have characterized the influences of soil moisture and solar altitude on surface spectral albedo in an arid area. The specific settings of our experiment allowed us to minimize the influences of underlying surface, cloud cover, aerosol and weather conditions, and thus highlight the influence of soil moisture and solar altitude. During the timespan of the experiment, we observed the annual mean surface albedo of global radiation (GR), ultraviolet radiation (UV), visible radiation (VIS) and near-infrared radiation (NIR) to be 0.24, 0.11, 0.24 and 0.25. A significantly negative linear correlation between surface albedo and soil moisture was identified, with the correlation coefficients between GR, UV, VIS, NIR and soil moisture being −0.68, −0.75, −0.70 and −0.61. In addition, we identified an exponential relationship between surface albedo and solar altitude. The exponential regression coefficients are −0.21, −0.077, −0.53 and −0.21, respectively. From these analyses, we derived a new two-factor parametric formula for depicting the influence of soil moisture and solar altitude on surface spectral albedo. Using observation data, we demonstrate that the formula recapitulates the real-world relationship between soil moisture, solar altitude and surface spectral albedo with little deviation. These findings may help us gain a deeper understanding of improving land surface parameterizations and have potential implications for solar energy research and applications.

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Section: The Characteristics Of Surface Spectral Albedomentioning

confidence: 97%

Section: Design Of the Experimentsmentioning

confidence: 98%

The influence of soil moisture and solar altitude on surface spectral albedo in arid area

Yang

Zhai

et al. 2020

Environ. Res. Lett.

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“…To a first approximation, we assume

T_{Z} = T_{a}

with

T_{a}

(K) the ambient air temperature [ Shahraeeni and Or , ]. Implicit in equation is the assumption of a linearized soil temperature profile, originally varying exponentially with soil depth [ Heusinkveld et al ., ; Shahraeeni and Or , ], across a relatively shallow surface soil layer (i.e., the thermal decay depth) where more dramatic temperature changes occur [ Shahraeeni and Or , ; Li et al ., ]. This assumption (i.e.,

\partial T / \partial z = const .

) physically implies a constant conductive heat flux in the vertical direction that is canceled out between top and bottom faces of the vaporization zone across the thermal decay depth [ Shahraeeni and Or , ].…”

Section: Theoretical Considerationsmentioning

confidence: 99%

Near‐surface turbulence as a missing link in modeling evapotranspiration‐soil moisture relationships

Haghighi

Kirchner

2017

Water Resources Research

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Despite many efforts to develop evapotranspiration (ET) models with improved parametrizations of resistance terms for water vapor transfer into the atmosphere, estimates of ET and its partitioning remain prone to bias. Much of this bias could arise from inadequate representations of physical interactions near nonuniform surfaces from which localized heat and water vapor fluxes emanate. This study aims to provide a mechanistic bridge from land‐surface characteristics to vertical transport predictions, and proposes a new physically based ET model that builds on a recently developed bluff‐rough bare soil evaporation model incorporating coupled soil moisture‐atmospheric controls. The newly developed ET model explicitly accounts for (1) near‐surface turbulent interactions affecting soil drying and (2) soil‐moisture‐dependent stomatal responses to atmospheric evaporative demand that influence leaf (and canopy) transpiration. Model estimates of ET and its partitioning were in good agreement with available field‐scale data, and highlight hidden processes not accounted for by commonly used ET schemes. One such process, nonlinear vegetation‐induced turbulence (as a function of vegetation stature and cover fraction) significantly influences ET‐soil moisture relationships. Our results are particularly important for water resources and land use planning of semiarid sparsely vegetated ecosystems where soil surface interactions are known to play a critical role in land‐climate interactions. This study potentially facilitates a mathematically tractable description of the strength (i.e., the slope) of the ET‐soil moisture relationship, which is a core component of models that seek to predict land‐atmosphere coupling and its feedback to the climate system in a changing climate.

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“…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:…”

Section: Introductionmentioning

confidence: 99%

“…Using experimental measures of soil heat flux and soil temperature, several authors estimated the soil thermal conductivity by isolating λ in Equation (2). However, this procedure can lead to possible numerical divergences, as shown by Li et al [19,30] that eliminated data when soil temperature gradient or soil heat flux is very close to zero to avoid this problem.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Soil thermal conductivity is an important parameter for understanding soil heat transfer. It is difficult to measure in situ with available instruments. This work aims to propose a numerical model to estimate the thermal conductivity from the experimental measurements of soil heat flux and soil temperature. The new numerical model is based on the Fourier Law adding a constant empirical parameter to minimize the uncertainties contained in the data from field experiments. Numerically, the soil thermal conductivity is obtained by experimental linear data fitting by the Least Squares Method (LSM). This method avoids numerical indetermination when the soil temperature gradient or soil heat flux is very close to zero. The new model is tested against the different numerical methodology to estimate the soil heat flux and validated with field experimental data. The results indicate that the proposed model represents the experimental data satisfactorily. In addition, we show the influence of the different methodologies on evaluating the dependence of the thermal conductivity on the soil water content.

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Comparative study of the soil thermal regime in arid and semi-humid areas

Cited by 15 publications

References 19 publications

The influence of soil moisture and solar altitude on surface spectral albedo in arid area

The influence of soil moisture and solar altitude on surface spectral albedo in arid area

Near‐surface turbulence as a missing link in modeling evapotranspiration‐soil moisture relationships

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

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