• Journal of Korean Society on Water Environment
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• v.32 no.2
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• pp.173-182
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• 2016

The objective of this study is to quantitatively analyze climate change effects by using statistical trends and a watershed model in the Yongdam dam watershed. The annual average air temperature was found to increase with statistical significance. In particular, greater increases were observed in autumn. Also, this study was performed to evaluate the potential climate change in the streamflow and water temperature using a watershed model (HSPF) with RCP climate change scenarios. The streamflow of Geum river showed a decrease of 5.1% and 0.2%, respectively, in the baseline data for the 2040s and 2080s. The seasonal impact of future climate change on the streamflow showed a decrease in the summer and an increase in the winter. The water temperature of Geum river showed an average increase of 0.7~1.0℃. Especially, the water temperature of Geum river showed an increase of 0.3~0.5℃ in the 2040s and 0.5~1.2℃ in the 2080s. The seasonal impact of future climate change on the water temperature showed an increase in winter and spring, with a decrease in summer. Therefore, it was determined that a statistical analysis-based meteorological and quantitative forecast of streamflow and water temperature using a watershed model is necessary to assess climate change impact and to establish plans for future water resource management.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.4
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• pp.261-267
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• 2003

The present study investigates the fire suppression characteristics using a water mist fire suppression system. Numerical simulations of fire suppression with water mist are performed with considering the interaction of fire plume and water droplet, droplet evaporation, and combustion of pool fire. The predicted temperature fields of smoke layer are compared with that of measured data. Numerical results agree with the experimental results within 5$^{\circ}C$ in the case without water mist In the case of fire suppression with water mist, numerical results dose not predict well lot temperature field in the gradual cooling region after water mist injection. But the predicted results of initial fire suppression are in good agreement with that of measured data. The reason of the discrepancy between predicted and measured data is due to the variation of turning rate during the injection of water mist. The effect of burning rate on the fire suppression is left as future study.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.10
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• pp.1002-1008
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• 2001

The energy is the basis for almost all industrial activities and domestic needs. But recently there are increasing concerns internationally over environmental problems and consequent climate changes caused by the excessive use of fossil fuels. Furthermore the price of crude oil is increasing steadily with unstable supplies. In order to solve these national energy problems, the utilization of Ocean Energy is introduced as one of the best alterative technologies for the future. OTEC Power Plant has been installed at the West Inchon Power Plant Site. Temperature differences of$20~25^{\circ}C$ been utilized for plant operations, where R22 is used as a working fluid. The system is composed of low pressure turbine, plate type heat exchanger, and pumps. In the present investigation the experimental results, such as gross power, net power and objective function, are analysed when temperature differences change from the reference design point.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.983-989
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• 2018

The real-time monitoring of surface vegetation is essential for the management of droughts, vegetation growth, and water resources. The availability of land cover maps based on remotely collected data makes the monitoring of surface vegetation easier. The vegetation index in an area is likely to be proportional to meteorological elements there such as air temperature and precipitation. This study investigated relationship between vegetation index based on Moderate Resolution Image Spectroradiometer (MODIS) and ground-measured meteorological elements at the Yongdam catchment station. To do this, 16-day averaged data were used. It was found that the vegetation index is well correlated to air temperature but poorly correlated to precipitation. The study provides some intuition and guidelines for the study of the droughts and ecologies in the future.

• Journal of Environmental Impact Assessment
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• v.20 no.4
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• pp.435-442
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• 2011

In this study, a regression model was developed for prediction of inflow temperature to support an effective thermal stratification simulation of Yongdam Reservoir, using the relationship between gaged inflow temperature and air temperature. The effect of reproductability for thermal stratification was evaluated using EFDC model by gaged vertical profile data of water temperature(from June to December in 2005) and ex-developed regression models. Therefore, in the development process, the coefficient of correlation and determination are 0.96 and 0.922, respectively. Moreover, the developed model showed good performance in reproducing the reservoir thermal stratification. Results of this research can be a role to provide a base for building of prediction model for water quality management in near future.

• Water for future
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• v.26 no.2
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• pp.99-105
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• 1993

A practical flood forecasting model(FFM) is suggested. The output of the model is the results which the initial condition of meteorological parameters and soil moisture are projected on the future. The physically based station model for rainfall forecasting(RF) and the storage function model for runoff prediction(RP) are adopted respectively. Input variables for FFM are air temperature, pressure, and dew-point temperature at the ground level and the flow at the rising limb(FRL). The constant parameters for FFM are average of optimum values which the past storm events have. Also loss rate of rainfall can predicted by FRL.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.338-344
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• 2018

In this study, the surface air temperature (SAT) and the dew-point temperature (DPT) are applied as the covariance of the location parameter among three parameters of GEV distribution to reflect the non-stationarity of extreme rainfall due to climate change. Busan station is selected as the study site and the monthly maximum daily rainfall depth from May to October is used for analysis. Various models are constructed to select the most appropriate co-variate(SAT and DPT) function for location parameter of GEV distribution, and the model with the smallest AIC(Akaike Information Criterion) is selected as the optimal model. As a result, it is found that the non-stationary GEV distribution with co-variate of exp(DPT) is the best. The selected model is used to analyze the effect of climate change scenarios on extreme rainfall quantile. It is confirmed that the design rainfall depth is highly likely to increase as the future DPT increases.

• Atmosphere
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• v.22 no.1
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• pp.117-128
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• 2012

Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.46 no.6
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• pp.868-877
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• 2013

To understand the effects of marine environmental and meteorological parameters on laver Porphyra yezoensis production at Nakdong River Estuary, we analyzed marine environmental (water temperature, salinity, nutrients, etc.) and meteorological properties (air temperature, wind speed, precipitation, sunshine hours) with yearly and monthly variations in laver production over 10 years (2003-2013). Air and water temperature, wind speed, sunshine hours and precipitation were major factors affecting yearly variability in laver production at the Nakdong River Estuary. Lower air and water temperatures together with higher levels of nutrients and sunshine and stronger wind speeds resulted in higher laver harvests. Salinity and nitrogen did not show clear correlations with laver production, mainly due to the plentiful supply of nitrogen from river discharge and the low frequency of environmental measurements, which resulted in low statistical confidence. However, environmental factors affecting monthly laver production were related to the life cycle (culturing stage) of Porphyra yezoensis and were somewhat different from factors affecting annual laver production. In November, a young laver needs lower water temperatures for rapid growth, while a mature laver needs much stronger winds and more sunshine, as well as lower temperatures for massive production and effective photosynthesis, mostly in December and January. However, in spring (March), more stable environments with fewer fluctuations in air temperature are needed to sustain the production of newly deployed culture-nets ($2^{nd}$ time culture). These results indicate that rapid changes in weather and marine environments caused by global climate change will negatively affect laver production and, thus, to sustain the yield of and predict future variability in laver production at the Nakdong River estuary, environmental variation around laver culturing farms needs to be monitored with high resolution in space and time.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.