• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.6
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• pp.190-197
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• 2012

The purpose of this study was to evaluate thermal environment and heat budget of light thin layer green roof through an experiment in order to quantify its heat budget. Two concrete model boxes($1.2m(W){\times}1.2m(D){\times}1.0m(H)$) were constructed: One experiment box with Zoysia japonica planted on substrate depth of 10cm and one control box without any plant. Between June 6th and 7th, 2012, outside climatic conditions(air temperature, relative humidity, wind direction, wind speed), evapotranspiration, surface and ceiling temperature, heat flux, and heat budget of the boxes were measured. Daily maximum temperature of those two days was $29.4^{\circ}C$ and $30^{\circ}C$, and daily evapotranspiration was $2,686.1g/m^2$ and $3,312.8g/m^2$, respectively. It was found that evapotranspiration increased as the quantity of solar radiation increased. A surface and ceiling temperature of those two boxes was compared when outside air temperature was the greatest. and control box showed a greater temperature in both cases. Thus it was found that green roof was effective in reducing temperature. As results of heat budget analysis, heat budget of a green roof showed a greater proportion of net radiation and latent heat while heat budget of the control box showed a greater proportion of sensible heat and conduction heat. The significance of this study was to analyze heat budget of green roof temperature reduction. As substrate depth and types, species and seasonal changes may have influences on temperature reduction of green roof, further study is necessary.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.366-376
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• 2023

This study was conducted to develop a model for predicting the growth of kimchi cabbage using image data and environmental data. Kimchi cabbages of the 'Cheongmyeong Gaual' variety were planted three times on July 11th, July 19th, and July 27th at a test field located at Pyeongchang-gun, Gangwon-do (37°37' N 128°32' E, 510 elevation), and data on growth, images, and environmental conditions were collected until September 12th. To select key factors for the kimchi cabbage growth prediction model, a correlation analysis was conducted using the collected growth data and meteorological data. The correlation coefficient between fresh weight and growth degree days (GDD) and between fresh weight and integrated solar radiation showed a high correlation coefficient of 0.88. Additionally, fresh weight had significant correlations with height and leaf area of kimchi cabbages, with correlation coefficients of 0.78 and 0.79, respectively. Canopy coverage was selected from the image data and GDD was selected from the environmental data based on references from previous researches. A prediction model for kimchi cabbage of biomass, leaf count, and leaf area was developed by combining GDD, canopy coverage and growth data. Single-factor models, including quadratic, sigmoid, and logistic models, were created and the sigmoid prediction model showed the best explanatory power according to the evaluation results. Developing a multi-factor growth prediction model by combining GDD and canopy coverage resulted in improved determination coefficients of 0.9, 0.95, and 0.89 for biomass, leaf count, and leaf area, respectively, compared to single-factor prediction models. To validate the developed model, validation was conducted and the determination coefficient between measured and predicted fresh weight was 0.91, with an RMSE of 134.2 g, indicating high prediction accuracy. In the past, kimchi cabbage growth prediction was often based on meteorological or image data, which resulted in low predictive accuracy due to the inability to reflect on-site conditions or the heading up of kimchi cabbage. Combining these two prediction methods is expected to enhance the accuracy of crop yield predictions by compensating for the weaknesses of each observation method.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.5
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• pp.579-596
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• 1997

A simulation study was carried out to delineate potential effects of the lake-induced climate change on crop productivity around Lake Imha which was formed after a multi-purpose dam construction in Andong, Korea. Twenty seven cropping zones were identified within the 30 km by 25 km study area. Five automated weather stations were installed within the study area and operated for five years after the lake formation. A geostatistical method was used to calculate the monthly climatological normals of daily maximum and minimum temperature, solar radiation and precipitation for each cropping zone before and after the dam construction. Daily weather data sets for 30 years were generated for each cropping zone from the monthly normals data representing "No lake" and "After lake" climatic scenarios, respectively. They were fed into crop models (ORYZA1 for rice, SOYGRO for soybean, CERES-maize for corn) to simulate the yield potential of each cropping zone. Calculated daily maximum temperature was higher after the dam construction for the period of October through March and lower for the remaining months except June and July. Decrease in daily minimum temperature was predicted for the period of April through August. Monthly total radiation was predicted to decrease after the lake formation in all the months except February, June, and September and the largest drop was found in winter. But there was no consistent pattern in precipitation change. According to the model calculation, the number of cropping zones which showed a decreased yield potential was 2 for soybean and 6 for corn out of 27 zones with a 10 to 17% yield drop. Little change in yield potential was found at most cropping zones in the case of paddy rice, but interannual variation was predicted to increase after the lake formation. the lake formation.

• Journal of the Korean earth science society
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• v.36 no.3
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• pp.210-221
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• 2015

The terrain features and surface characteristics are the most important elements not only in meteorological modeling but also in air quality modeling. The diurnal evolution of local climate over complex terrain may be significantly controlled by the ground irregularities. Such topographic features can affect a thermally driven flow, either directly by causing changes in the wind direction or indirectly, by inducing significant variations in the ground temperature. Over a complex terrain, these variations are due to the nonuniform distribution of solar radiation, which is highly determined by the ground geometrical characteristics, i.e. slope and orientation. Therefore, the accuracy of prediction of regional scale circulation is strong associated with the accuracy of land-use and topographic information in meso-scale circulation assessment. The objective of this work is a numerical simulation using MM5-A2C model with the detailed topography and land-use information as the surface boundary conditions of the air flow field in mountain regions. Meteorological conditions estimated by MM5-A2C command a great influence on the dispersion of mountain areas with the reasonable feature of topography where there is an important difference in orographic forcing.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Journal of Bio-Environment Control
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• v.29 no.1
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• pp.9-19
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• 2020

Convective heat transfer is the main component of greenhouse energy loss because the energy loss by this mechanism is greater than those of the other two components (radiative and conductive). Previous studies have examined the convective heat transfer coefficients under natural conditions, but they are not applicable to symmetric thermal screens with zero porosity, and such screens are largely produced and used in Korea. However, the properties of these materials have not been reported in the literature, which causes selectivity issues for users. Therefore, in this study, three screens having similar color and zero porosity were selected, and a mathematical procedure based on radiation balance equations was developed to determine their convective heat transfer coefficients. To conduct the experiment, a hollow wooden structure was built and the thermal screen was tacked over this frame; the theoretical model was applied underneath and over the screen. Input parameters included three components: 1) solar and thermal fluxes; 2) temperature of the screen, black cloth, and ambient air; and 3) wind velocity. The convective heat transfer coefficients were determined as functions of the air-screen temperature difference under open-air environmental conditions. It was observed from the outcomes that the heat transfer coefficients decreased with the increase of the air-screen temperature difference provided that the wind velocity was nearly zero.

• Environmental and Resource Economics Review
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• v.15 no.4
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• pp.693-712
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• 2006

A pre-feasibility test is done for renewable energy hybrid power systems at off~grid islands in which the current power supply is provided only by diesel generation. We apply Homer (Hybrid Optimization Model for Electric Renewables) which was developed by the National Renewable Energy Laboratory (NREL) for the analysis to identify the cost-minimizing combination of power generating facilities for the given load profiles. Chuja-Do, Geomun-Do and Youngsan-Do have been selected for our analysis considering the wind resources data of the Korea Institute of Energy Research (KIER). Information on wind speed, solar radiation and temperature is also used for the analysis. System component cost information from overseas market has been used due to the lack of domestic information. Site specific Load profile for electricity demand for those islands are reconstructed based on the partial survey results obtained form other sources. The LCOE of the least cost hybrid power systems for Chuja-Do, Geomun-Do and Youngsan-Do are $0.278/kWh, $0.234/kWh and $0.353/kWh, respectively Considering the fact that diesel generation is being subsidized at the price of $0.300/kWh by the government, first 2 cases are economically feasible for the introduction of renewable energy hybrid systems to those islands. But the third case of Youngsan-Do does not meet the criteria. The basic differences of these pre-feasibility test results are from the differences of the site specific renewable energy conditions, especially wind resources. In summary, promoting hybrid systems in the off-grid remote island should be based on the economic feasibility test results. Not all the off-grid islands are feasible for introducing this renewable energy hybrid system.

• Korean Journal of Soil Science and Fertilizer
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• v.14 no.1
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• pp.8-16
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• 1981

A mathematical model based on the water flow equation was developed with the Ohm's analogy and the partial differential equations. Simulation of water uptake was performed by numerically solving the equations with the aid of a differential equation solver, DGEAR in IMSL package, in FORTRAN version. The input data necessary were climatological parameters (temperature, solar radiation, humidity and wind speed). plant parametors (leaf water potential, leaf area, root conductivity and root length density) and soil parameters (hydraulic conductivity and The graphical comparison of the simulated and measured water contents as the functions of time showed good agreement, but there still was some disparity due to possible inacouracy of the field measured parameters. The simulated soil evaporation showed about 2 mm/day early in the growing period and dropped to about 0.4 mm/day as the full canopy developed and the soil water depleted. During the dry period, soil evaporation was as low as 0.1 mm/day. The transpiration was as high as 5mm/day. Deep percolation calculated from the flux between the 180-cm layer was about 0.2mm/day and became smaller with time. After the soil water of upper layers depleted, the flux reversed showing capillary rise. The rate of the capillary rise reached about 0.07mm/day, which was too low to satisfy water uptake of the root system. Therefore, to increase use of water in deep soil, expansion of the root system is necessary.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.284-291
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• 2010

A simulation modeling for predicting the harvesting date with high potato solids consists of development of mathematical models. The mathematical model on potato growth and its development should be obtained by using agricultural elements which analyze relations of solar radiation quantity, temperature, photon quantity, carbon dioxide exchange rate, water stress and loss, relative humidity, light intensity, and wind etc. But more reliable way to predict harvesting date against climatic change employs in vivo energy consumption for growth and induction shape in a slight environmental adaptation. Therefore, to calculate in vivo energy loss, we take a concept of estimate of the amount of basal metabolism in each tuber on the basis of $Wm={\int}^m_tf(x)dt$ and $Tp=\frac{Tm{\cdot}Wm^{Tp}}{Wm^{Tm}}$. In the validation experiments, results of measuring solid accumulation of potato harvested at simulated date agreed fairly well with the actual measured values in each regional field during the growth period of 2005-2009. The calculation method could be used to predict an appropriate harvesting date for a production of high potato solids according to weather conditions.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.227-239
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• 2016

This study is to estimate Surface Energy Balance Algorithm for Land (SEBAL) daily spatial evapotranspiration (ET) comparing with eddy covariance flux tower ET in Seolmacheon mixed forest (SMK) and Cheongmicheon rice paddy (CFK). The SEBAL input data of Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) from Terra MODIS products and the meteorological data of wind speed, and solar radiation were prepared for 2 years (2012-2013). For the annual average flux tower ET of 302.8 mm in SMK and 482.0 mm in CFK, the SEBAL ETs were 183.3 mm and 371.5 mm respectively. The determination coefficients ($R^2$) of SEBAL ET versus flux tower ET for total periods were 0.54 in SMK and 0.79 in CFK respectively. The main reason of SEBAL ET underestimation for both sites was from the determination of hot pixel and cold pixel of the day and affected to the overestimation of sensible heat flux.