• International conference on construction engineering and project management
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• 2015.10a
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• pp.740-741
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• 2015

the downscaled air temperature data over study region for the projected 2001 - 2099 period were then ensemble averaged, and the ensemble averages of 6 realizations were compared against the corresponding historical downscaled data for the 1961 - 2000 period in order to assess the impact of climate change on air temperature over study region by graphical, spatial and statistical methods. In order to evaluate the seasonal trends under future climate change conditions, the simulated annual, annual DJF (December-January-February), and annual JJA (June-July-August) mean air temperature for 5 watersheds during historical and future periods were evaluated. From the results, it is clear that there is a rising trend in the projected air temperature and future air temperature would be warmer by about 3 degrees Celsius toward the end of 21^st century if the ensemble projections of air temperature become true. Spatial comparison of 30-year average annual mean air temperature between historical period (1970 - 1999) and ensemble average of 6-realization shows that air temperature is warmer toward end of 21^st century compared to historical period.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.1
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• pp.34-41
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• 2008

Future impact of anthropogenic climate-induced change on ecological regime has been an issue and information on water temperature is required for estimating coastal aquatic environment. One way to induce water temperature is to relate water temperature to air temperature and GCM is able to provide future air temperature data to do this. However, GCM data of low spatial resolution doesn't incorporate local or sitespecific air temperature in need of application, and downscaling processes are essential. In this study, a linear regression is used to relate nationally averaged air temperature to local area for the time period of 2000-2005. The RMSE for calibration (2000-2005) is 1.584, while the RMSE for validation is 1.675 for the year 2006 and 1.448 for the year 2007. The NSC for calibration (2000-2005) is 0.962, while the NSC for validation is 0.955 for the year 2006 and 0.963 for the year 2007. The results show that the linear regression is a good tool to relate local air temperature to nationally averaged air temperature with $1.0{\sim}2.0^{\circ}C$ of RMSE. The study will contribute to estimate future impact of climate-induced change on aquatic environment in Korean coastal zone.

• Journal of Environmental Policy
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• v.13 no.2
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• pp.21-38
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• 2014

In response to global warming, the effect of the air temperature on water temperature has been noticed. The change in water temperature in river environment results in the change in water quality and ecosystem, especially Dissolved Oxygen (DO) level, and shifts in aquatic biota. Efforts need to be made to predict future water temperature in order to understand the timing of the projected river temperature. To do this, the data collected by the Ministry of Environment and the Korea Meteororlogical Administration has been used to build a nonlinear relationship between air and water temperature. The logistic function that includes four different parameters was selected as a working model and the parameters were optimized using SCE algorithm. Weekly average values were used to remove time scaling effect because the time scale affects maximum and minimum temperature and then river environment. Generally speaking nonlinear logistic model shows better performance in NSC and RMSE and nonlinear logistic function is recommendable to build a relationship between air and water temperature in Korea. The results will contribute to determine the future policy regarding water quality and ecosystem for the decision-driving organization.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.27-39
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• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

• Journal of Environmental Science International
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• v.19 no.2
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• pp.237-245
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• 2010

The relationship between air temperatures and the fraction of urban areas (FUA) and their linear regression equation were estimated using land-use data provided by the water management information system (WAMIS) and air temperatures by the Korea Meteorology Administration (KMA) in the Seoul metropolitan area (SMA) during 1975 through 2000. The future FUA in the SMA (from 2000 to 2030) was also predicted by the urban growth model (i.e., SLEUTH) in conjunction with several dataset (e.g., urban, roads, etc.) in the WAMIS. The estimated future FUA was then used as input data for the linear regression equation to estimate an annual mean minimum air temperature in the future (e.g., 2025 and 2030). The FUA in the SMA in 2000 simulated by the SLEUTH showed good agreement with the observations (a high accuracy (73%) between them). The urban growth in the SMA was predicted to increase by 16% of the total areas in 2025 and by 24% in 2030. From the linear regression equation, the annual mean minimum air temperature in the SMA increased about $0.02^{\circ}C$/yr and it was expected to increase up to $8.3^{\circ}C$ in 2025 and $8.7^{\circ}C$ in 2030.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.9
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• pp.836-844
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• 2001

For the radiant floor heating system, the possibility of suboptimal prediction control was investigated by computer simulation and experiment. For this study, TRANSYS program was used and an experimental facility consisting of two rooms (3$\times$4.4$\times$2.8m) was built. The facility enabled simultaneous comparison of two different control strategies which implemented in a separate room. Results showed that outdoor air temperature prediction control was superior to the conventional outdoor air temperature compensation control for radiant floor heating system. However, more research for fine prediction of outside air temperature was required in the future.

• Journal of Korean Society on Water Environment
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• v.38 no.1
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• pp.19-30
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• 2022

Climate change has increased the average air temperature. Rising air temperature are absorbed by water bodies, leading to increasing water temperature. Increased water temperature will cause eutrophication and excess algal growth, which will reduce water quality. In this study, long-term trends of air and water temperatures in the Han-river basin over the period of 1997-2020 were discussed to assess the impacts of climate change. Future (~2100s) levels of air temperature were predicted based on the climate change scenarios (Representative concentration pathway (RCP) 2.6, 4.5, 6.0, and 8.5). The results showed that air and water temperatures rose at an average rate of 0.027℃ year^-1 and 0.038℃ year^-1 respectively, over the past 24 years (1997 to 2020). Future air temperatures under RCP 2.6, 4.5, 6.0, and 8.5 increased up to 0.32℃ 1.18℃, 2.14℃, and 3.51℃, respectively. An increasing water temperature could dissolve more minerals from the surrounding rock and will therefore have a higher electrical conductivity. It is the opposite when considering a gas, such as oxygen, dissolved in the water. Water temperature also governs the kinds of organisms that can live in rivers and lakes. Fish, insects, zooplankton, phytoplankton, and other aquatic species all have a preferred temperature range. As temperatures get too far above or below this preferred range, the number of individuals of the species decreases until finally there are none. Therefore, changes of water temperature that are induced by climate change have important implications on water supplies, water quality, and aquatic ecosystems of a watershed.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.822-833
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• 2010

Temperature increase due to climate changes causes change of water temperature in rivers which results in change of water quality etc. and the change of river ecosystem has a great impact on human life. Analyzing the impact of current climate changes on air and water temperature is an important thing in adapting to the climate changes. This study examined the effect of climate changes through analyzing air temperature trend for Nakdong river basin and analyzed the elasticity of air-water temperature to understand the effect of climate changes on water temperature. For analysis air temperature trend, collecting air temperature data from the National Weather Service on main points in Nakdong river basin, and resampling them at the units of year, season and month, used as data for air temperature trend analysis. Analyzing for elasticity of air-water temperature, the data were collected by the Water Environment Information system for water temperature, while air temperature data were collected at the National Weather Service point nearest in the water temperature point. And using the results of trend analysis and elasticity analysis, the effect of climate changes on water temperature was examined estimating future water temperature in 20 years and 50 years after. It is judged that analysis on mutual impact between factors such as heat budget, precipitation and evapotranspiration on river water temperature affected by climate changes and river water temperature is necessary.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.495-503
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• 2000

In this study the average air temperature in April at Taegu city is analyzed and forecasted by applying an intervention model of stochastic analysis. As it is unavailable to get the data of carbon dioxide emission from a city in Korea, the points of intervention have been decided from the analysis of the data observed to be the years of 1947 and 1970. As a result of the study the mean temperature of April in Taegu city is shown to increase for a long time in the future and will also remain 0.015$^{\circ}C$/year even after 70 years from now. Even though the air temperature changes in the future will vary depending on the future exhaustion of carbon dioxide, a significant increase of air temperature cannot be avoided if the emission of carbon dioxide continues at today's level.

• Journal of Environmental Impact Assessment
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• v.22 no.4
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• pp.329-343
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• 2013

According to previous studies, the increased air temperature can lead to change of thermal stratification structure of lakes and reservoirs. The changed thermal stratification may result in alteration of materials and energy flow. The objective of this study was to predict the effect of climate change on the water temperature and stratification structure of Daecheong Reservoir, located in Geum River basin of Korea, using a three-dimensional(3D) hydrodynamic model(ELCOM). A long-term(100 years) weather data set provided by the National Institute of Meteorological Research(NIMR) was used for forcing the 3D model. The model was applied to two different hydrological conditions, dry year(2001) and normal year(2004). It means that the effect of air temperature increase was only considered. Simulation results showed that the surface water temperature of the reservoir tend to increase in the future, and the establishment of thermal stratification can occur earlier and prolonged longer. As a result of heat flux analysis, the evaporative heat loss can increase in the future than now and before. However, the convective heat loss and net long wave radiation from water surface decreased due to increased air temperature.