• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.179-183
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• 2009

본 연구에서는 GCM 기후변화 전망 시나리오를 이용하여 유역단위의 기후변화를 추정하였다. 원시 GCM 시나리오를 지역화 시키기 위해서 인공신경망 모형을 사용하였다. GCM에서 모의되는 강수플럭스, 해면기압, 지표면 근처에서의 일 평균온도, 지표면으로부터 발생하는 잠열플럭스 등과 같은 22개의 변수는 인공신경망의 잠재적 예측인자로 사용되었으며, AWS에서 관측된 강수량과 온도는 예측변수로 사용되었다. 원시 GCM 데이터는 CCCma(Canadian Centre for Climate Modeling and Analysis)에서 제공되는 CGCM3.1/T63 20C3M 시나리오를 사용하였으며, 인공신경망 학습과정에서 사용된 기준시나리오(reference scenario)자료의 기간은 1997년부터 2000년까지의 데이터를 사용하였다. 인공신경망을 학습을 통하여 결정된 각 층사이의 가중치를 이용하여 이산화탄소 배출농도를 가정하여 생성된 CGCM3.1/T63 SRES B1 기후변화시나리오(project scenario)를 인공신경망의 입력값으로 하여 미래의 기온과 강수변화를 전망하였다. 신경망의 학습효과를 높이기 위하여 기온과 강수에 대한 평균 및 누적기간을 각각 일단위와 월단위로 설정하였다. 본 연구에서 사용된 인공신경망은 3층 퍼셉트론(다층 퍼셉트론)을 사용하였으며, 학습방법으로는 역전파알고리즘(back-propagation algorithm)을 이용하였다. 민감도분석을 통하여 선택된 예측인자는 소양강댐유역(1011, 1012소유역)에서의 인공신경망 예측인자로 활용되었으며, 2001년부터 2100년까지의 일 평균온도와 일 강수량의 변화경향을 추정하였다. 1011유역, 1012유역에서는 여름철의 온도변화경향이 겨울철에 비하여 높게 나타났다. 일 평균온도의 통계분석 결과 평균예측오차가 가장 적게 나타나는 지역은 1001유역으로 -0.08로 평균예측오차가 가장 적게 나타났으며, 인공신경망기법을 이용하여 스케일 상세화된 일 평균온도와 관측된 일 평균온도가 얼마나 잘 일치하는지를 확인할 수 있는 1012유역에서 CORR이 0.74로 가장 높게 나타났다.

• Korean Journal of Remote Sensing
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• v.6 no.2
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• pp.75-88
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• 1990

NOAA 위성의 narrow-band AVHRR(Advanced Very. High Resolution Radiometer) 적 외선 채널과 broad-band 0.2 - 50$\mu\textrm{m}$ 영역의 ERBE(Earth Radiation Budget Experiment) scanning instrument에 의하여 관측된 radiance로부터 추정된 지구의 대기 외장파복사량 (Outgoing Longwave Radiation:OLR) 이 비교조사되었다. 이를 위하여 1985년 4월, 7월, 10월과 1986년 1월에 위성에서 관측된 radiance를 각각 이용하였고 비교된 OLR은 위도와 경도가 각각 2.5$^{\circ}$ 간격으로 구분된 grid내에서 일치(collocate)시켜 지역별(zonal), 그리고 전지구(global)규모 로 비교되었다. ERBE와 AVHRR에 의하여 각각 추정된 OLR값의 차(ERBE minus AVHRR)에 의한 분석 결과는 주간의 경우 -1~2 W/m$^2$의 값과 야간의 경우 4~7 W/m$^2$의 값으로 비교적 좋은 일치를 보였지만 이들의 RMS는 하절(6월)에 12 W/m$^2$와 동절(12월)에 5 W/m$^2$의 값으로 다소 높은 차이를 보였다. 한편, 이들 OLR값을 관측지역에 따라 큰 차 이를 나타내어 사막지역과 아열대고기압(subtropical ocean)대에서는 상반된 결과를 보였다. 이들 지역에 대한 차이는 지역적 기온구조와 지표온도의 영향을 다 고려하지 못하고 OLR 측정치를 도 출하는 대기복사모델(radiation model)의 regional systematic bias에서 기인된 것으로 해석된다. 즉 사막의 지표역전층에 대한 AVHRR과 해면의 대기구조에 대한 ERBE의 OLR은 상반된 영향을 보였다.

• Korean Journal of Soil Science and Fertilizer
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• v.17 no.2
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• pp.108-113
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• 1984

To find out the differences in micro-meteorological changes in the rice plant canopy at the different growing stages, Seokwang-byo, a high yielding variety, was cultivated with three planting densities of 50, 80 and 110 hills per $3.3m^2$ in 1982, and Seokwangbyo and Chucheong-byo, a local variety, were planted with a density of 80 hills per $3.3m^2$. Air temperature in plant canopies, water and soil temperatures were continuously monitored throughout the growing period. The relationship between solar radiation interception and leaf area indices at different height in the canopy also was studied. The results were as follows: 1. Air temperature in the densely planted canopy was 1 to $1.5^{\circ}C$ higher than that in the sparsely planted one at the early growing stage, but was inverted after 60 days of transplanting. The vertical distribution of temperature in the canopies showed that air temperature at 10 cm height from the ground was higher than that at 30 cm height. The temperature inversion occurred showing lower temperature at the 10 cm height than at the 30 cm height. 2. The highest temperature of a day in the canopy occurred at 14:00 to 15:00 Korean Standard Time same as that of air temperature, but approached to the solar noon time as the plants grew thick. 3. The air temperature in the canopy became higher than water temperature when the leaf area indices were 4.6 for Chucheongbyo and 5.2 for Seokwangbyo, and the light penetration ratios were 40 percents. 4. Light extinction coefficients of the 50 to 70 cm layer of the canopies were 0.3 to 0.5 but decreased at the lower layers. 5. Albedo of the canopies was 0.4 in the morning and evening while that was about 0.25 at noon. The difference in albedo between Seokwangbyo and Chucheongbyo could be recognized with the difference in leaf structure.

• Journal of Bio-Environment Control
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• v.24 no.3
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• pp.217-225
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• 2015

Local heating system providing hot air locally to growing parts including shoot apex and flower cluster which were temperature-sensitive organs of cherry tomato was developed to reduce energy consumption for greenhouse heating without decline of crop growth. Growing part following local heating system was composed of double duct distributer which connected inner and outer ducts with hot air heater and winder which moved ducts up and down following growing parts with plant growth. Growing part local heating system was compared with conventional bottom duct heating system with respect to distributions of air and leaf surface temperatures according to height, growth characteristics and energy consumption. By growing part local heating, air temperature around growing part was maintained $0.9{\sim}2.0^{\circ}C$ higher than that of lower part of crop and leaf surface temperature was also stratified according to height. Investigations on crop growth characteristics and crop yield showed no statistically significant difference except for plant height between bottom duct heating and growing part local heating. As a result, the growing part local heating system consumed 23.7% less heating energy than the bottom duct heating system without decrease of crop yield.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.242-249
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• 2006

Potential impacts of the future climate change on crop production can be inferred by crop simulations at a landscape scale, if the climate data may be provided at appropriate spatial scales. Northern Gyunggi Province is one of the few prospective regions in South Korea for growing quality soybeans. Any geographical shift of production areas under the changing climate may influence the current land planning policy in this region. A soybean growth simulation was performed at 342 land units in northern Gyunggi province to test the potential geographical shift of the current production areas for quality soybeans in the near future (form 2011 to 2100). The land units for soybean cultivation were selected by the land use, the soil characteristics, and the minimum arable land area. Daily maximum and minimum temperature, precipitation, the number of rain days and solar radiation were extracted for each land unit from the future digital climate models (DCM, 2011-2040, 2041-2070, 2071-2100). Daily weather data for 30 years were randomly generated for each land unit for each normal year by using a well-known statistical method. They were used to run CROPGRO-Soybean model to simulate the growth, phonology, and yields of 3 cultivars representing different maturity groups grown at 342 land units. According to the model calculations, the warming trend in this region will accelerate the flowering and physiological maturity of all cultivars, resulting in a 7 to 9 days reduction in overall growing season and a 1 to 15% reduction in grain yield of early to medium maturity cultivars. There was a slight increase in grain yield of the late maturing cultivar under the projected climate by 2070, but a decreasing tend was dominant by the year 2100.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.697-706
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• 2013

In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.195-209
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• 2003

Spatial-Stochastic Neural Networks Model(SSNNM) is used to estimate long-term streamflow in the parallel reservoir groups. SSNNM employs two kinds of backpropagation algorithms, based on LMBP and BFGS-QNBP separately. SSNNM has three layers, input, hidden, and output layer, in the structure and network configuration consists of 8-8-2 nodes one by one. Nodes in input layer are composed of streamflow, precipitation, pan evaporation, and temperature with the monthly average values collected from Andong and Imha reservoir. But some temporal differences apparently exist in their time series. For the SSNNM training procedure, the training sets in input layer are generated by the PARMA(1,1) stochastic model and they covers insufficient time series. Generated data series are used to train SSNNM and the model parameters, optimal connection weights and biases, are estimated during training procedure. They are applied to evaluate model validation using observed data sets. In this study, the new approaches give outstanding results by the comparison of statistical analysis and hydrographs in the model validation. SSNNM will help to manage and control water distribution and give basic data to develop long-term coupled operation system in parallel reservoir groups of the Upper Nakdong River.

• Journal of Bio-Environment Control
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• v.15 no.2
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• pp.125-137
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• 2006

Pleurotus eryngii is one of the most promising mushrooms produced on the domestic farms. The quality as well as quantity of Eryngii is sensitively affected by micro climate factors such as temperature, relative humidity, $CO_2$ concentration, and light intensity. To safely produce high-quality Eryngii all the yew round, it is required that the environmental factors be carefully controlled by well designed structures equipped with various facilities and control systems. At the commercial mushroom cultivation houses of permanent frame type (A, B), this study was carried out to find out reasonable range of each environmental factor and yield together with economic and safe structures influencing on the optimal productivity of Eryngii. This experiment was conducted for about two-year ken Nov. 2003 to Dec. 2005 in cultivation house. Ambient temperature during the experiment period was not predominantly different from that of a normal year. The capacity of the hot water boiler and the piping systems were not enough. Because the capacity of electric heater and air circulation were not enough, air temperatures in cultivation house before improvement of system were maintained somewhat lower than setting temperature, and maximum air temperature difference between the upper and lower growth stage during a heating time period was about 5.1. But the air temperatures after system improvement were maintained within the limits range of setting temperature without happening stagnant of air. Air temperature distribution was generally distributed uniform. Relative humidity in cultivation house before , improvement was widely ranged about $44{\sim}100%$. But as the relative humidity after improvement was ranged approximately $80{\sim}100%$, it was maintained within the range of relative humidity recommended. And $CO_2$ concentration was maintained about $400{\sim}3,300mg{\cdot}L^{-1}$ range. The illuminance in cultivation house was widely distributed in accordance with position, and it was maintained lower than the recommended illuminance range $100{\sim}200lx$. The acidity of midium was some lower range than the recommend acidity range of pH $5.5{\sim}6.5$. The yield was relatively ununiform. In case of bottle capacity of 1,300cc, the mushroom of the lowest grade was less than 3%. The consumption electric energy was quite different according to the cultivation season. The electric energy consumed during heating season was much more than that of cooling season.