• Ecology and Resilient Infrastructure
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• v.2 no.3
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• pp.198-205
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• 2015

Impacts of climate change on aquatic ecosystems range from changes in physiological processes of aquatic organisms to species distribution. In this study, MaxEnt that has high prediction power without nonoccurrence data was used to simulate fish distribution changes in the Geum river watershed according to climate change. The fish distribution in 2050 and 2100 was predicted with RCP 8.5 climate change scenario using fish occurrence data (a total of 47 species, including 17 endemic species) from 2007 to 2009 at 134 survey points and 9 environmental variables (monthly lowest, highest and average air temperature, monthly precipitation, monthly lowest, highest and average water temperature, altitude and slope). The fitness of MaxEnt modeling was successful with the area under the relative operating characteristic curve (AUC) of 0.798, and environmental variables that showed a high level of prediction were as follows: altitude, monthly average precipitation and monthly lowest water temperature. As climate change proceeds until 2100, the probability of occurrence for Odontobutis interrupta and Acheilognathus yamatsuatea (endemic species) decreases whereas the probability of occurrence for Microphysogobio yaluensis and Lepomis macrochirus (exotic species) increases. In particular, five fish species (Gnathopogon strigatus, Misgurnus mizolepis, Erythroculter erythropterus, A. yamatsuatea and A. koreensis) were expected to become extinct in the Geum river watershed in 2100. In addition, the species rich area was expected to move to the northern part of the Geum river watershed. These findings suggest that water temperature increase caused by climate change may disturb the aquatic ecosystem of Geum river watershed significantly.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.169.2-169.2
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• 2010

The research assesses the performance of the water-to-water heat pump system installed in Cheongju water treatment plant for cooling and heating ventilation. In summer season monthly averaged COP is ranged from 3.85 to 4.56 according to the water source temperature, and the performance is increased as the raw water temperature is dropped. While, heating performance is increased for the high temperature water source, and the monthly averaged COP is changed from 2.92 to 3.82. The correlation of the water source temperature and the heat pump performance shows a linear tendency by the simple regression of average data. In heating, the COP of heat pump system linearly rises according to the water source temperature. In comparison, the COP in cooling linearly reduces as the raw water temperature is raised. The goodness of fit at the simple regression shows the coefficient of determination 82% in cooling, 46% in heating. The electric cost of water-to-water heat pump is reduced by 40% compared to that of air source heat pump.

• KIEAE Journal
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• v.8 no.5
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• pp.49-54
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• 2008

The experimental of temperature, humidity and velocity was taken from the underground pit which utilized the system of ground heat source quite similar to the cool-pit system. Also, through CFD analysis, one could review the effectiveness of analysis of future alternatives. Furthermore, the temperature range of mock up cool-pit system was analyzed by inputting the weather data of annual average soil temperature provided by KMA(Korea Meteorological Administration) into the fluid simulation of anticipated heat distribution. Firstly, the difference between the temperature of air exhaust of the pit or the temperature of air supply of the compressor room and the experimental data for the month of May from the CFD analysis came out to be $0.6^{\circ}C$ and $0.9^{\circ}C$ respectively with tolerance of 3.1% and 4.7%. Secondly, the difference between the temperature of air exhaust of the Pit or the temperature of air supply of the compressor room and the experimental data for the month of July from the CFD analysis came out to be $0.8^{\circ}C$ and $1.1^{\circ}C$ respectively with tolerance of 3.3% and 4.5%. Thirdly, for the month of May, the difference between the experimental data taken for the air exhaust of the Pit or the air supply of the compressor room and soil temperature provided by KMA for monthly and yearly average temperature of Jeonju region came out be $1.9^{\circ}C$ and $1.8^{\circ}C$ respectively with tolerance of 10.7% and 9.8%. Fourthly, for the month of July, the difference between the experimental data taken for the air exhaust of the Pit or the air supply of the compressor room and soil temperature provided by KMA for monthly and yearly average temperature of Jeonju region came out be $1.1^{\circ}C$ and $1.4^{\circ}C$ respectively with tolerance of 4.5% and 5.8%. The result of above experiments allowed us to establish CFD model set up as a verification tool that is based on experimental data collected within the Pit area. Also, one could confirm the possibility to apply weather data of soil temperature provided by KMA in order to anticipate proper value for CFD analysis.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.1-8
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• 2024

Persistent droughts due to climate change will intensify water shortage problems in Korea. According to the 1st National Water Management Plan, the shortage of domestic and industrial waters is projected to be 0.07 billion m³/year under a 50-year drought event. A long-term prediction of water demand is essential for effectively responding to water shortage problems. Unlike industrial water, which has a relatively constant monthly usage, domestic water is analyzed on monthly basis due to apparent monthly usage patterns. We analyzed monthly water usage patterns using water usage data from 2017 to 2021 in Chungcheong, South Korea. The monthly water usage rate was calculated by dividing monthly water usage by annual water usage. We also calculated the water distribution rate considering correlations between water usage rate and climate variables. The division method that divided the monthly water usage rate by monthly average temperature resulted in the smallest absolute error. Using the division method with average temperature, we calculated the water distribution rates for the Chungcheong region. Then we predicted future water usage rates in the Chungcheong region by multiplying the average temperature of the SSP5-8.5 scenario and the water distribution rate. As a result, the average of the maximum water usage rate increased from 1.16 to 1.29 and the average of the minimum water usage rate decreased from 0.86 to 0.84, and the first quartile decreased from 0.95 to 0.93 and the third quartile increased from 1.04 to 1.06. Therefore, it is expected that the variability in monthly water usage rates will increase in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.252-261
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• 2018

This study was conducted to develop a method to generate daily maximum and minimum temperatures using monthly data. We analyzed 30-year daily weather data of the 23 meteorological stations in South Korea and elucidated the parameters for predicting annual trend (center value ($\hat{U}$), amplitude (C), deviation (T)) and daily fluctuation (A, B) of daily maximum and minimum temperature. We use national average values for C, T, A and B parameters, but the center value is derived from the annual average data on each stations. First, daily weather data were generated according to the occurrence of rainfall, then calibrated using monthly data, and finally, daily maximum and minimum daily temperatures were generated. With this method, we could generate daily weather data with more than 95% similar distribution to recorded data for all 23 stations. In addition, this method was able to generate Growing Degree Day(GDD) similar to the past data, and it could be applied to areas not subject to survey. This method is useful for generating daily data in case of having monthly data such as climate change scenarios.

• Atmosphere
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• v.16 no.4
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• pp.333-342
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• 2006

The long-term trends of global solar irradiance, air temperature, specific humidity and cloudiness measured at King Sejong station, Antarctica, during the period of 1988-2004, have been investigated. A statistically insignificant decrease, -0.21 $Wm^{-2}yr^{-1}$ (-0.26 %$yr^{-1}$, P<0.5) in global solar irradiance was found in an analysis from the time series of the monthly mean values, except for the increasing trends only in two months of January and June. The trends in irradiance are directly and inversely associated with the cloudiness trends in annual and monthly means. The trends in surface air temperature show a slight warming, $0.03^{\circ}Cyr^{-1}$ (1.88 %$yr^{-1}$, P<0.5) on the annual average, with cooling trend in the summer months and the warming in the winter. The exact relationship, if any, between the irradiance and temperature trends is not known. No significant tendency was found in specific humidity for the same periods. Recent (1996-2004) erythermal ultraviolet irradiance shows decreasing trend in annual mean, -0.15 $mWm^{-2}yr^{-1}$ (-1.18 %$yr^{-1}$, P<0.1) which is about five times the trends of global solar irradiance. The ratio of erythermal ultraviolet to global solar irradiance shows remarkable seasonal variations with annual mean value of 0.01 % and a peak in October and November, showing the increase of ultraviolet irradiance resulting from the Antarctic ozone hole. The sensitivity of global solar irradiance to the change in cloudiness is roughly $13%oktas^{-1}$ which is about twice of the value at the South Pole due to the difference in the average surface reflectance between the two stations. Much more sensitive values of $59%oktas^{-1}$ was found for erythermal UV irradiance than for the global solar irradiance.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Journal of Environmental Science International
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• v.7 no.2
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• pp.191-194
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• 1998

In this research, two stochastic models are considered to detect and estimate the effect of air temperature change due to Industrialization In Ulsan area. Using the monthly mean minimum air temperature anomalies, the data are divided Into pre-Industrialization part and Industrialization one for analysis. The ARM(autoregressive moving-average) model and intervention model have been applied to the data for the analysis. The results show that the variability of minimum temperature anomalies are very significant In 1989, and also significant In 1971 when the Industrialization have started. Therefore, It Is stochastically possible to estimate the time when the affection of Increase of the temperature concerning Industrialization to climate change In Usm area has happened.

• Journal of the Korean Wood Science and Technology
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• v.47 no.6
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• pp.721-731
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• 2019

Air drying depends on species, density, dimension of wood, the geographical location of the air drying yard, and the meteorological factors of air drying site. If there are four seasons with large difference in temperature and humidity like in Korea, the research of the meteorological factors is required in air drying site. In this study, effective air drying days (EADD) of 24 regions in Korea were calculated by using the average monthly temperature, relative humidity, and wind speed. The EADD in 24 regions in Korea was ranged from 239 days to 291 days, with an average 265 days. This result is 5 days increased compared to the average of EADD calculated using the meteorological factors from 1955 to 1984. The results of multiple regression analysis on the EADD and meteorological factors showed that EADD affected in the order of temperature, relative humidity, and wind speed. As a result of dividing Korea into 4 zones of EADD, the zones of EADD were moved northward compared to previous study due to global warming. As basic data for predicting the moisture content (MC) distribution of Korean red pine logs during air drying conducted in Seoul, the average monthly temperature, relative humidity and wind speed for three years from 2016 to 2018 were presented, and the corresponding changes of the equilibrium MC were analyzed.

• Journal of Korean Institute of Industrial Engineers
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• v.7 no.1
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• pp.21-31
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• 1981

This paper develops a short term forecasting model for household electric power consumption in Seoul, which can be used for the effective planning and control of utility management. The model developed is based on exponentially weighted moving average model and incorporates monthly average temperature as an exogeneous factor so as to enhance its forecasting accuracy. The model is empirically compared with the Winters' three parameter model which is widely used in practice and the Box-Jenkins model known to be one of the most accurate short term forecasting techniques. The result indicates that the developed hybrid exponential model is better in terms of accuracy measured by average forecast error, mean squared error, and autocorrelated error.