• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.518-518
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• 2016

Agriculture is the lifeblood of the economy in Ghana, employs about 42% of the population work force and accounts for 30% of the Gross Domestic Product (GDP). Corn (maize) is the major cereal crop grown as staple food under rain fed conditions, covers over 92% of the total agricultural area, and contributes 54% of the caloric intake. Issues of hunger and food insecurity for the entire nation are associated with corn scarcity and low production. The climate changes are expected to affect corn production in Ghana. This study evaluated variations of corn yields based on different climate conditions of rain-fed area in the Dangbe East District of Ghana. AquaCrop model has been used to simulate corn growing cycles in study area for this purpose. The main goal for this study was to predict yield of corn using selected climatic parameters from 1992 to 2013 using different climate scenarios. The Model was calibrated and validated using observed field data, and the simulated grain yields matched well with observed values for the season under production giving an R squared (R2)of 0.93 and Nash-Sutcliff Error(NSE) of 0.21. Study results showed that rainfall reduction in the range of -5% to -20% would reduce the yield from 1.315ton/ha to 0.421ton/ha (-21. 3%) whereas increasing temperature from 1% to 7% would result in the maximum yield reduction of -20.6% (1.315 to 1.09 ton/ha.). On the other hand, increasing rainfall from 5-20% resulted in yield increment of 68% (1.315-2.209 ton/ha) and decreasing temperature produce 7% increase in yield ( 1.315 to 1.401ton/ha). These results provide useful information to adopt strategies by the Government of Ghana and farmers for improving national food security under climate change.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.1-8
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• 2019

Developing reliable soil moisture prediction techniques at agricultural regions is a pivotal issue for sustaining stable crop productions. In this study, a physically-based SWAP(Soil-Water-Atmosphere-Plant) model was suggested to estimate soil moisture dynamics at the study sites. ROSETTA was also integrated to derive the soil hydraulic properties(${\alpha}$, n, ${\Theta}_r$, ${\Theta}_s$, $K_s$) as the input variables to SWAP based on the soil information(Sand, Silt and Clay-SSC, %). In order to predict the soil moisture dynamics in future, the mid-term TIGGIE(THORPEX Interactive Grand Global Ensemble) and long-term S2S(Subseasonal to Seasonal) weather forecasts were used, respectively. Our proposed approach was tested at the six study sites of RDA(Rural Development Administration). The estimated soil moisture values based on the SWAP model matched the measured data with the statistics of Root Mean Square Error(RMSE: 0.034~0.069) and Temporal Correlation Coefficient(TCC: 0.735~0.869) for validation. When we predicted the mid-/long-term soil moisture values using the TIGGE(0~15 days)/S2S(16~46 days) weather forecasts, the soil moisture estimates showed less variations during the TIGGE period while uncertainties were increased for the S2S period. Although uncertainties were relatively increased based on the increased leading time of S2S compared to those of TIGGE, these results supported the potential use of TIGGE/S2S forecasts in evaluating agricultural drought. Our proposed approach can be useful for efficient water resources management plans in hydrology, agriculture, etc.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.331-337
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• 2016

Water vapor is main absorption factor of outgoing longwave radiation. So, it is essential to monitoring the changes in the amount of water vapor and to understanding the causes of such changes. In this study, we monitor temporal variability of Total Precipitable Water (TPW) which observed by satellite. Among climate variables, precipitation play an important part to analyze temporal variability of water vapor because it is produced by water vapor. And El $Ni{\tilde{n}}o$ is one of climate variables which appear regularly in comparison with the others. Through them, we analyze relationship between temporal variability of TPW and climate variable. In this study, we analyzed long-term change of TPW from Moderate-Resolution Imaging Spectroadiometer (MODIS) data and change of precipitation in middle area of Korea peninsula quantitatively. After these analysis, we compared relation of TPW and precipitation with El $Ni{\tilde{n}}o$. The aim of study is to research El $Ni{\tilde{n}}o$ has an impact on TPW and precipitation change in middle area of Korea peninsula. First of all, we calculated TPW and precipitation from time series analysis quantitatively, and anomaly analysis is performed to analyze their correlation. As a result, TPW and precipitation has correlation mostly but the part had inverse correlation was found. This was compared with El $Ni{\tilde{n}}o$ of anomaly results. As a result, TPW and precipitation had inverse correlation after El $Ni{\tilde{n}}o$ occurred. It was found that El $Ni{\tilde{n}}o$ have a decisive effect on change of TPW and precipitation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.108-125
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• 2015

The agro-climatic index is one of the ways to assess the climate resources of particular agricultural areas on the prospect of agricultural production; it can be a key indicator of agricultural productivity by providing the basic information required for the implementation of different and various farming techniques and practicalities to estimate the growth and yield of crops from the climate resources such as air temperature, solar radiation, and precipitation. However, the agro-climate index can always be changed since the index is not the absolute. Recently, many studies which consider uncertainty of future climate change have been actively conducted using multi-model ensemble (MME) approach by developing and improving dynamic and statistical downscaling of Global Climate Model (GCM) output. In this study, the agro-climatic index of Korean Peninsula, such as growing degree day based on $5^{\circ}C$, plant period based on $5^{\circ}C$, crop period based on $10^{\circ}C$, and frost free day were calculated for assessment of the spatio-temporal variations and uncertainties of the indices according to climate change; the downscaled historical (1976-2005) and near future (2011-2040) RCP climate sceneries of AR5 were applied to the calculation of the index. The result showed four agro-climatic indices calculated by nine individual GCMs as well as MME agreed with agro-climatic indices which were calculated by the observed data. It was confirmed that MME, as well as each individual GCM emulated well on past climate in the four major Rivers of South Korea (Han, Nakdong, Geum, and Seumjin and Yeoungsan). However, spatial downscaling still needs further improvement since the agro-climatic indices of some individual GCMs showed different variations with the observed indices at the change of spatial distribution of the four Rivers. The four agro-climatic indices of the Korean Peninsula were expected to increase in nine individual GCMs and MME in future climate scenarios. The differences and uncertainties of the agro-climatic indices have not been reduced on the unlimited coupling of multi-model ensembles. Further research is still required although the differences started to improve when combining of three or four individual GCMs in the study. The agro-climatic indices which were derived and evaluated in the study will be the baseline for the assessment of agro-climatic abnormal indices and agro-productivity indices of the next research work.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.21-31
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• 2020

Environmental pollution and climate change in Vietnam are now becoming a major concern. This situation is increasing the pressure on the companies to improve their social responsibility in production and business activities and disclose the environmental information to meet the requirements of stakeholders. This study investigates the internal and external factors of the company that affects the environmental information disclosure of listed companies on the Vietnam stock market as business sector, firm size, corporate manager perceptions, profitability, financial leverage, community pressure, pressures from stakeholders, government pressure influencing environmental information disclosure. Analytical data collected through the survey of 120 listed companies on the Ho Chi Minh City Stock Exchange (HOSE). By testing Cronbach's Alpha, exploratory factor analysis (EFA) and logistic regression analysis, the results of the study show that the level of environmental information disclosure of listed companies on the stock market in Vietnam depends heavily on government regulations, followed by the pressure from stakeholders, community pressure, views of business managers, companies size, business sector, and particularly profitability and financial leverage factors that have a negative relationship with environmental information disclosure.

• The Magazine of the IEIE
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• v.35 no.11
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• pp.19-30
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• 2008

• Journal of the Society of Disaster Information
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• v.13 no.1
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• pp.6-14
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• 2017

The opportunity provided for design method and strategy of sustainable campus on the waterfront, is the purpose of this paper. Waterfront campus is an important issue as it seeks to revive the sustainability and to renew the facilities. This paper reviews an assessment of its success for waterfront campus in 10 principles such as waterfront, water and safety, climate & energy, green building and transportation, green labs and recycling, health and food, social economic sustainability, fund, human, smart, also concludes with the establishment of space making for the waterfront campus for future educational facilities on the waterfront; implementation of waterfront campus maserplan; building sustainable campus in adaptation to climate change; creative and resilient cooperation.

• The Journal of Fisheries Business Administration
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• v.46 no.2
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• pp.31-41
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• 2015

This study aimed to analyze the relationship between sea surface temperature as a climatic element and catch amount of offshore and coastal fisheries in Korea using annual time series data from 1970 to 2013. It also tried to predict the future changes in catch amount of fisheries by climate change. Time series data on variables were estimated to be non-stationary from unit root tests, but one long-term equilibrium relation between variables was found from a cointegration test. The result of Granger causality test indicated that the sea surface temperature would cause directly changes in catch amount of offshore and coastal fisheries. The result of regression analysis on sea surface temperature and catch amount showed that the sea surface temperature would have negative impacts on the catch amount of offshore and coastal fisheries. Therefore, if the sea surface temperature would increase, all other things including the current level of fishing effort being equal, the catch amount of offshore and coastal fisheries was predicted to decrease.

• Journal of Environmental Science International
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• v.26 no.5
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• pp.585-590
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• 2017

The Diameter at Breast Height (DBH) provides information about the volume growth of a tree. In this study, we estimated the relative growth rates of Castanea crenata and Pinus rigida as 4.07% and 3.73%, respectively. Although the difference was low, we demonstrated that the growth rate of C. crenata is slightly faster than that of P. rigida. After calculating the relative growth rate for each section, we found that the relative growth of C. crenata decreased with time. However, the relative growth rate of P. rigida showed an overall increase. The gap widths of both species showed an increasing trend. However, the gradient of the two species was different. The gradient of C. crenata was approximately 12.0, but that of P. rigida was approximately 4.7. This means that the volume growth of C. crenata was faster than that of P. rigida during 4 years. However, this was relatively a short period for measuring the volume growth pattern, and we believe that additional useful information can be obtained by conducting long-term ecological monitoring. Results of canonical correspondence analysis showed that among the climate variables, temperature was significantly related to the gap widths for both species.