• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.246-253
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• 2000

The optimal equations to predict the soil tempratures of twelve cities in the region of the southern part of the Korea such as Changhung, Cheju, Chinju, Kwangju, Masan, Miryang, Mokpo, Muan, Pusan, Sogwipo, Ulsan, Yoosu, were suggested as function of time and soil depth and the time dependent variation and soil depth dependent distribution of temperature were analyzed for the back data of the geothermal energy utilization system design and agricultural usages. The equation form is $T(x,\;t)\;=\;T_{m}\;-\;T_{so}{\cdot}Exp(-\xi){\cdot}cos{\omega}(t\;-\;t_{o}\;-\;x\;/\sqrt{2{\alpha}{\omega}}$) and it can predict the soil temperatures well with the correlation factor of 0.98 or upwards for most data. The range of mean soil temperature was $14.99~18.53^{\circ}C$ and soil surface temperature swing, 11.65~14.54 days, soil thermal diffusivity, $0.025~0.069\;m^2/day$ except Mokpo of $0.100\;m^2/day$, and phase shift, 19.66~27.81 days. During about thirty years from 1960s to 1990s, the mean soil temperature was increased by $0.04~1.25^{\circ}C$. The temperature difference depending on soil depth was not significant.

• Journal of Biosystems Engineering
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• v.23 no.2
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• pp.135-146
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• 1998

The equation to predict the soil temprature of Pusan and Chinju city as a function of time and soil depth for the geothermal energy utilization system and agriculture was devised. The equation was $T(x,t)\;=\;Tm\;-\;To{\cdot}ExP(-{\xi}){\cdot}cos{{\omega}{\cdot}[t-to-x/(2{\cdot}{\alpha}{\cdot}{\omega})^{0.5}]}$ with the soil thermal diffusivity, ${\alpha},\;of\;0.4\;\textrm{m}^2/day,\;0.0375\;\textrm{m}^2/day$ and phase zero point, to, of 24 days, 22.4 days in Pusan and Chinju city, respectively, during ten years from 1987 to 1996. The predicted and measured soil temperatures agreed well with the coefficient of determination of 0.95 at the soil depth of 0.0, 0.5, 1.0, 3.0, 5.0 m. The maximum and minimum temperature in Pusan 3.7, $30.1^{\circ}C$ at soil surface and 14.3, $18.0^{\circ}C$ at the depth of 5.0 m. The total mean temperature of soil in Pusan and Chinju city was about 16.3, $16.0^{\circ}C$, respectively.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.02a
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• pp.190-195
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• 2000

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.137-147
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• 2012

Land surface temperature in ecohydrology is a variable that links surface structure to soil processes and yet its spatial prediction across landscapes with variable surface structure is poorly understood. And there are an insufficient number of soil temperature monitoring stations. In this study, a grid-based land surface temperature prediction model is proposed. Target sites are Andong and Namgang dam region. The proposed model is run in the following way. At first, geo-referenced site specific air temperatures are estimated using a kriging technique from data collected from 60 point weather stations. Then surface soil temperature is computed from the estimated geo-referenced site-specific air temperature and normalized difference vegetation index. After the model is calibrated with data collected from observed remote-sensed soil temperature, a soil temperature map is prepared based on the predictions of the model for each geo-referenced site. The daily and monthly simulated soil temperature shows that the proposed model is useful for reproducing observed soil temperature. Soil temperatures at 30 and 50 cm of soil depth are also well simulated.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.43 no.2
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• pp.113-119
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• 1998

Spatial patterns of soil temperature on sloping lands are related to the amount of solar irradiance at the surface. Since soil temperature is a critical determinant of many biological processes occurring in the soil, an accurate prediction of soil temperature distribution could be beneficial to agricultural and environmental management. However, at least two problems are identified in soil temperature prediction over natural sloped surfaces. One is the complexity of converting solar irradiances to corresponding soil temperatures, and the other, if the first problem could be solved, is the difficulty in handling large volumes of geo-spatial data. Recent developments in geographic information systems (GIS) provide the opportunity and tools to spatially organize and effectively manage data for modeling. In this paper, a simple model for conversion of solar irradiance to soil temperature is developed within a GIS environment. The irradiance-temperature conversion model is based on a geophysical variable consisting of daily short- and long-wave radiation components calculated for any slope. The short-wave component is scaled to accommodate a simplified surface energy balance expression. Linear regression equations are derived for 10 and 50 cm soil temperatures by using this variable as a single determinant and based on a long term observation data set from a horizontal location. Extendability of these equations to sloped surfaces is tested by comparing the calculated data with the monthly mean soil temperature data observed in Iowa and at 12 locations near the Tennessee - Kentucky border with various slope and aspect factors. Calculated soil temperature variations agreed well with the observed data. Finally, this method is applied to a simulation study of daily mean soil temperatures over sloped corn fields on a 30 m by 30 m resolution. The outputs reveal potential effects of topography including shading by neighboring terrain as well as the slope and aspect of the land itself on the soil temperature.

• Atmosphere
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• v.31 no.3
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• pp.327-340
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• 2021

In this study, the impact of soil moisture initialization in GloSea5, the operational climate prediction system of the Korea Meteorological Administration (KMA), has been investigated for the period of 1991~2010. To overcome the large uncertainties of soil moisture in the reanalysis, JRA55 reanalysis and CMAP precipitation were used as input of JULES land surface model and produced soil moisture initial field. Overall, both mean and variability were initialized drier and smaller than before, and the changes in the surface temperature and pressure in boreal summer and winter were examined using ensemble prediction data. More realistic soil moisture had a significant impact, especially within 2 months. The decreasing (increasing) soil moisture induced increases (decreases) of temperature and decreases (increases) of sea-level pressure in boreal summer and its impacts were maintained for 3~4 months. During the boreal winter, its effect was less significant than in boreal summer and maintained for about 2 months. On the other hand, the changes of surface temperature were more noticeable in the southern hemisphere, and the relationship between temperature and soil moisture was the same as the boreal summer. It has been noted that the impact of land initialization is more evident in the summer hemispheres, and this is expected to improve the simulation of summer heat wave in the KMA's operational climate prediction system.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.649-654
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• 2009

Soil temperature is an important tool in predicting a change of climate and agricultural environment together with the change of atmospheric temperature. In this paper, we examine changing patterns of soil temperature measured in 0.5m under ground from 1932 to 1990 and atmospheric temperature from 1961 to 2008, and derive a relationship between atmospheric temperature and soil temperature. Using this model, we predict unmeasured soil temperature in Daegu area and soil temperature is found to be increasing about $0.028^{\circ}C$per a year. Prediction of soil temperature is an important indicator for climate change in Daegu and will be useful information to help us take precautions for global warming, etc.

• Journal of the Korean Solar Energy Society
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• v.26 no.4
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• pp.1-7
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• 2006

This study is to develop a model to predict the soil temperature variation in Korea Institute of Energy Research using its thermal properties, such as thermal conductivity and diffusivity. Soil depth temperature variation is very important in the design of a proper Ground Source Heat Pump (GSHP) system. This is because the size of the borehole depends on the soil temperature distribution, and this can decrease GSHP system cost. If the thermal diffusivity and thermal conductivity are known, the soil temperature can be predicted by either the Krarti equation or the Spitler equation. Then a comparison with the Krarti equation and Spitler equation data with the real measured data can be performed. Also, the thermal properties can be reasonably approximated by performing a fit of the Krarti and Spitler equations with measured temperature data. This was done and, as a result, the Krarti equation and Spitler equation predicted values very close to the measured data. Although there is about a $0.5^{\circ}C$ difference between the deep subsurface prediction (16m - 60m), with this equation, were expected to have model this Non-Homogeneous Soil Temperature phenomenon properly. So, it has been shown that a prediction of non-homogeneous soil temperature variation influenced by solar radiation can be achieved with a model.

• Atmosphere
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• v.26 no.1
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• pp.35-45
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• 2016

Initialization of the global seasonal forecast system is as much important as the quality of the embedded climate model for the climate prediction in sub-seasonal time scale. Recent studies have emphasized the important role of soil moisture initialization, suggesting a significant increase in the prediction skill particularly in the mid-latitude land area where the influence of sea surface temperature in the tropics is less crucial and the potential predictability is supplemented by land-atmosphere interaction. This study developed a new soil moisture initialization method applicable to the KMA operational seasonal forecasting system. The method includes first the long-term integration of the offline land surface model driven by observed atmospheric forcing and precipitation. This soil moisture reanalysis is given for the initial state in the ensemble seasonal forecasts through a simple anomaly initialization technique to avoid the simulation drift caused by the systematic model bias. To evaluate the impact of the soil moisture initialization, two sets of long-term, 10-member ensemble experiment runs have been conducted for 1996~2009. As a result, the soil moisture initialization improves the prediction skill of surface air temperature significantly at the zero to one month forecast lead (up to ~60 days forecast lead), although the skill increase in precipitation is less significant. This study suggests that improvements of the prediction in the sub-seasonal timescale require the improvement in the quality of initial data as well as the adequate treatment of the model systematic bias.

• Journal of Biosystems Engineering
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• v.25 no.6
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• pp.497-502
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• 2000

For the analysis of geothermal energy utilization in agriculture the relations between soil temperature and geographical variables such as latitude longitude and sea level in Korea were analyzed and the regression equations were suggested among them. The measured soil temperature data for four years in eighteen cities were used to get the soil temperature fitting equation depending on the soil depth and the time of year in each city. The mean correlation coefficient for those data fitting was 0.980. the correlation coefficient of regression analysis for the mean soil temperature($T_{m}$) on the geographical variables such as latitude longitude and height above sea level was 0.958 and those for soil surface temperature amplitude(Tss) and phase constant(tp) were 0.889, 0.835, respectively. The relation between the apparent thermal diffusivity of the soil and the three geographical variables was not significant. The regression equations for the mean soil temperature($T_{m}$) soil surface temperature amplitude(Tss) and phase constant(tp) adopting latitude($X_{1}$) longitude($X_2$) height above sea level($X_3$) were as follows : $T_{m}$=50.049 - $0.849X_1$-$0.03131X_2$-$0.00622X_3$Tss=-6.970 +$0.584X_1$+$0.00530X_2$-$0.00214X_3$tp=70.353 - $1.404X_1$+ $0.02098X_2$+ $0.00312X_3$