• Journal of the Korean Solar Energy Society
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• v.28 no.1
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• pp.57-64
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• 2008

According to the Fourth Assessment Report of Intergovernmental Panel on Climate Change(IPCC) Working Group III, climate change is already in progress around the world, and it is necessary to execute mitigation in order to minimize adverse impacts. This paper suggests future climate change needs, employing IPCC Special Report on Emissions Scenarios(SRES) to predict temperature rises over the next 100 years. This information can be used to develop sustainable architecture applications for energy efficient buildings and renewable energy. Such climate changes could also affected the resent supplies of renewable energy sources. This paper discusses one recent Fourth Assessment Report of IPPC (Mitigation of Climate Change) and the Hadley Centre climate simulation of relevant data series for South Korea.

• Journal of Environmental Impact Assessment
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• v.30 no.5
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• pp.271-296
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• 2021

Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

• 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 Korean Society on Water Environment
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• v.29 no.2
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• pp.155-164
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• 2013

To confirm the relationship between climate change and Stephanodiscus in Mulgeum station of Nakdong River, Korea, this study was conducted. The temperature in crease by climate change was observed in the study site, where the temperature was gradually increased in most seasons, except for summer season. The mass proliferation of Stephanodiscus constantly appeared in every year, especially between November and March, and when Stephanodiscus abundance was above 90% in phytoplankton biomass. Among this period, phytoplankton biomass was high related with water temperature ($r^2$=0.249, P<0.01) than nutrient factors such as nitrogen and phosphorus in the study site. Finally, temperature by climate change can be regarded as the affecting factor for chl. a variation, because temperature was strongly related with water temperature ($r^2$=0.748, P<0.01). From 1997 to 2010, the annual maximum phytoplankton biomass was recorded in the range of temperature from $4.8^{\circ}C$ to $8.4^{\circ}C$, and the range was regarded as the temperature condition for the optimal growth of Stephanodiscus in the study site. On the optimal growth temperature, the trend of monthly average temperature corresponded to the trend of chl. a variation from November to March. In future, the increase of temperature by climate change can prolong Stephanodiscus blooming period in winter and spring seasons.

• Journal of Korean Society on Water Environment
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• v.35 no.3
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• pp.234-247
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• 2019

In a deep lake and reservoir, thermal stratification is of great importance for characteristics of hydrodynamic mixing of the waterbody, and thereby influencesvertical distribution of dissolved oxygen, substances, nutrients, and the phytoplankton community. The purpose of this study, was to project the effect of a future climate change scenario on water temperature, stratification strength, and thermal stability in the Soyanggang Reservoir in the Han River basin of South Korea, using a suite of mathematical models; SWAT, HEC-ResSim, and CE-QUAL-W2(W2). W2 was calibrated with historical data observed 2005-2015. Using climate data generated by HadGEM2-AO with the RCP 4.5 scenario, SWAT predicted daily reservoir inflow 2016-2070, and HEC-ResSim simulated changes in reservoir discharge and water level, based on inflow and reservoir operation rules. Then, W2 was applied, to predict long-term continuous changes of water temperature, in the reservoir. As a result, the upper layer (5 m below water surface) and lower layer (5 m above bottom) water temperatures, were projected to rise $0.0191^{\circ}C/year$(p<0.05) and $0.008^{\circ}C/year$(p<0.05), respectively, in response to projected atmospheric temperature rise rate of $0.0279^{\circ}C/year$(p<0.05). Additionally, with increase of future temperature, stratification strength of the reservoir is projected to be stronger, and the number of the days when temperature difference of the upper layer and the lower layer becomes greater than $5^{\circ}C$, also increase. Increase of water temperature on the surface of the reservoir, affected seasonal growth rate of the algae community. In particular, the growth rate of cyanobacteria increased in spring, and early summer.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.193-201
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• 2015

The aim of this study is to find preferred climate condition for outdoor water activity and to estimate future change of preferred season for the activity following the climate change. We chose urban public swimming pools, Hangang park swimming pools, which do not have any attractions except pools and allow people to make decision to visit pools in the morning solely based on the weather conditions as study sites. We identified the preferred climate conditions by analyzing the relationship between number of visitors and temperature, wind chill temperature and discomfort indexes. According to the result, the preferred temperature range was from $23.51^{\circ}C$ to $37.56^{\circ}C$, the wind chill temperature range was from $25.90^{\circ}C$ to $39.43^{\circ}C$, the discomfort index range was from 71.61 to 88.98 and the precipitation range was below 22.8 mm per day. When the temperature range is applied as the preferred season, in present, the length of the season is 127 days, from end of May to end of September. However, if temperature increase resulting from lower emission scenario (RCP 6.0), the season would be extended to 162 days, from early May to middle of October. If temperature is increasing under high emission scenario (RCP 8.5), the length of the season would be extended to 173 days from early May to end of October. In addition, the period of between end of July and early August, which is currently the most preferred season, would not be favored anymore due to high temperature. The result of this study further suggests the necessity of climate change adaptation activities.

• Atmosphere
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• v.26 no.4
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• pp.665-672
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• 2016

In this study, we examine future changes in the Hadley cell (HC) strength using CMIP5 climate change simulations. The current study is an extension of a previous study by Seo et al. that used all 30 available models. Here, we select 18-23 well-performing models based on their significant internal sensitivity of the interannual HC strength variation to the latitudinal temperature gradient variation. The model projections along with simple scaling analysis show that the inter-model variability in the HC strength change is a result of the inter-model spread in the meridional temperature gradient across the subtropics for both DJF and JJA, not by the tropopause height or gross static stability change. The HC strength is expected to weaken significantly during DJF, while little change is expected in the JJA HC strength. Compared to the calculations with all model members, selected model statistics increase the linear correlation between the changes in HC strength and meridional temperature gradient by 13~23%, confirming the robust sensitivity of the HC strength to the meridional temperature gradient. Two scaling equations for the selected models predict changes in HC strength better than all-member predictions. In particular, the prediction improvement in DJF is as high as 30%. The simple scaling relations successfully predict both the ensemble-mean changes and model-to-model variations in the HC strength for both seasons.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.5
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• pp.37-48
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• 2013

The objective of this study is to assess the impact of potential climate change on the hydrological components, especially on the streamflow, evapotranspiration and snowmelt, by using the Soil Water Assessment Tool (SWAT) for 17 Hanriver middle watersheds of South Korea. For future assessment, the SWAT model was calibrated in multiple sites using 4 years (2006-2009) and validated by using 2 years (2010-2011) daily observed data. For the model validation, the Nash-Sutcliffe model efficiency (NSE) for streamflow were 0.30-0.75. By applying the future scenarios predicted five future time periods Baseline (1992-2011), 2040s (2021-2040), 2060s (2041-2060), 2080s (2061-2080) and 2100s (2081-2100) to SWAT model, the 17 middle watersheds hydrological components of evapotranspiration, streamflow and snowmelt were evaluated. For the future precipitation and temperature of RCP 4.5 scenario increased 41.7 mm (2100s), $+3^{\circ}C$ conditions, the future streamflow showed +32.5 % (2040s), +24.8 % (2060s), +50.5 % (2080s) and +55.0 % (2100s). For the precipitation and temperature of RCP 8.5 scenario increased 63.9 mm (2100s), $+5.8^{\circ}C$ conditions, the future streamflow showed +35.5 % (2040s), +68.9 % (2060s), +58.0 % (2080s) and +63.6 % (2100s). To determine the impact on snowmelt for Hanriver middle watersheds, snowmelt parameters of SWAT model were determined through evaluating observed streamflow data during snowmelt periods (November-April). The results showed that average SMR (snowmelt / runoff) of 17 Hanriver middle watersheds was 62.0 % (Baseline). The annual average SMR were 42.0 % (2040s), 39.8 % (2060s), 29.4 % (2080s) and 27.9 % (2100s) by applying RCP 4.5 scenario. Also, the annual average SMR by applying RCP 8.5 scenario were 40.1 % (2040s), 29.4 % (2060s), 18.3 % (2080s) and 12.7 % (2100s).

• Journal of Korean Society on Water Environment
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• v.40 no.3
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• pp.121-129
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• 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.2
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• pp.1-13
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• 2015

The Coupled Model Intercomparison Project Phase 5 (CMIP5), coordinated by the World Climate Research Programme in support of the Intergovernmental Panel on Climate Change (IPCC) AR5, is the most recent, provides projections of future climate change using various global climate models under four major greenhouse gas emission scenarios. There is a wide selection of climate models available to provide projections of future climate change. These provide for a wide range of possible outcomes when trying to inform managers about possible climate changes. Hence, future agrometeorological indicators estimation will be much impacted by which global climate model and climate change scenarios are used. Decision makers are increasingly expected to use climate information, but the uncertainties associated with global climate models pose substantial hurdles for agricultural resources planning. Although it is the most reasonable that quantifying of the future uncertainty using climate change scenarios, preliminary analysis using reasonable factors for selecting a subset for decision making are needed. In order to narrow the projections to a handful of models that could be used in a climate change impact study, we could provide effective information for selecting climate model and scenarios for climate change impact assessment using maximum/minimum temperature, precipitation, reference evapotranspiration, and moisture index of nine Representative Concentration Pathways (RCP) scenarios.