• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.891-905
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• 2011

Climate vulnerability index is usually defined as a function of the climate exposure, sensitivity, and adaptive capacity, which requires adequate selection of proxy variables of each variable. We selected and used 9 proxy variables related to climate exposure in the literature, and diagnosed the adequacy of them for application in Korean peninsula. The selected proxy variables are: four variables from temperature, three from precipitation, one from wind speed, and one from relative humidity. We collected climate data over both previous year (1981~2010) and future climate scenario (A1B scenario of IPCC SERES) for 2020, 2050, and 2100. We introduced the spatial and temporal diagnostic statistical parameters, and evaluated both spatial and time variabilities in the relative scale. Of 9 proxy variables, effective humidity indicated the most sensitive to climate change temporally with the biggest spatial variability, implying a good proxy variable in diagnostics of climate change vulnerability in Korea. The second most sensitive variable is the frequency of strong wind speed with a decreasing trend, suggesting that it should be used carefully or may not be of broad utility as a proxy variable in Korea. The A1B scenario of future climate in 2020, 2050 and 2100 matches well with the extension of linear trend of observed variables during 1981~2010, indicating that, except for strong wind speed, the selected proxy variables can be effectively used in calculating the vulnerability index for both past and future climate over Korea. Other local variabilities for the past and future climate in association with climate exposure variables are also discussed here.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.226-226
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• 2015

The complex climate system regarding human actions is well represented through global climate models (GCMs). The output from GCMs provides useful information about the rate and magnitude of future climate change. Especially, the temperature variable is most reliable among other GCM outputs. However, hydrological variables (e.g. precipitation) from GCM outputs for future climate change contain too high uncertainty to use in practice. Therefore, we propose a method that simulates temperature variable with increasing in a certain level (e.g. 0.5oC or 1.0oC increase) as a global warming scenario from observed data. In addition, a hydrometeorological variable can be simulated employing block-wise sampling technique associated with the temperature simulation. The proposed method was tested for assessing the future change of the seasonal precipitation in South Korea under global warming scenario. The results illustrate that the proposed method is a good alternative to levy the variation of hydrological variables under global warming condition.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.585-600
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• 2011

South Korean central government has launched the first comprehensive climate change policies in 1999, and they have been renewed every three year. The third policies ended in 2007. However, it is quite rare to analyze whether the climate change policies are effective against climate change. In this context, this paper aims at analyzing the effectiveness of climate change policy which was launched for seven years from 1999 to 2007 in South Korea. The effectiveness analysis of policy can be done in terms of the individual policy and/or all policies being synthesized as a comprehensive unit. Employing the latter methodology, this paper analyzed the effectiveness on the basis of economic growth as independent variable, greenhouse gas emission as dependent variable, and energy use and its process as intervening variable. Seven analytic indicators covering the three variables were selected on the basis of two points in time before and after climate change policy having been launched. The seven indicators were analyzed in terms of three aspects. They were the change in the state of each indicator, the effectiveness of climate change policy from 1999 to 2007, and the effectiveness process from 1999 to 2007. The effectiveness process was analyzed in terms of the relational context and its flow processing path. Economic growth was advanced remarkably with increase in the total consumption of energy. As a result, greenhouse gas emission increased. However, energy efficiency increased with significant decrease in energy intensity, carbon intensity, and energy elasticity. The expansion of new and renewable energy over total energy supply was not effective significantly on the decrease in greenhouse gas emission. The processing path of climate change policy being effective advanced toward increase in energy efficiency through energy intensity rather than toward sustainable development. Such a way of the effectiveness of climate change policy implies that most policies focused on adaptation rather than on mitigation.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.161-161
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• 2023

Bias correction of values is a necessary step in downscaling coarse and systematically biased global climate models for use in local climate change impact studies. In addition to univariate bias correction methods, many multivariate methods which correct multiple variables jointly - each with their own mathematical designs - have been developed recently. While some literature have focused on the inter-comparison of these multivariate bias correction methods, none have focused extensively on the effect of diverse configurations (i.e., different combinations of input variables to be corrected) of climate variables, particularly high-dimensional ones, on the ability of the different methods to remove biases in uni- and multivariate statistics. This study evaluates the impact of three configurations (inter-variable, inter-spatial, and full dimensional dependence configurations) on four state-of-the-art multivariate bias correction methods in a national-scale domain over South Korea using a gridded approach. An inter-comparison framework evaluating the performance of the different combinations of configurations and bias correction methods in adjusting various climate variable statistics was created. Precipitation, maximum, and minimum temperatures were corrected across 306 high-resolution (0.2°) grid cells and were evaluated. Results show improvements in most methods in correcting various statistics when implementing high-dimensional configurations. However, some instabilities were observed, likely tied to the mathematical designs of the methods, informing that some multivariate bias correction methods are incompatible with high-dimensional configurations highlighting the potential for further improvements in the field, as well as the importance of proper selection of the correction method specific to the needs of the user.

• Journal of Fisheries and Marine Sciences Education
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• v.21 no.1
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• pp.121-133
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• 2009

This study tested the relations of schools organizational climate and teachers' job stresses, perceived by 913 teachers from 45 elementary, junior- and senior-high schools. Pearson's correlation analysis for the relations between the sub-factors of both organizational climate and job stresses and cannonical correlation analysis for the relative contribution of individual variable of organizational climate upon job stress were applied for the test. The results of Pearson's correlation analysis showed that while 'intimacy', 'esprit', 'considerations', and 'production emphasis' climate had negative correlations with job stress sub-factors, 'disengagement' and 'aloofness' climate had positive correlation. 'Student guidance', a sub-factor of job stresses, did not have statistically significant correlation with any sub-factors of organizational climate. Findings from cannonical correlation analysis showed 2 significant cannonical functions to explain the relations between the sets of variables. 'Disengagement' from organizational climate positively contributed with 'authority forfeiture' and 'dissention and conflict' of the job stresses variables.

• Journal of the Korean Geographical Society
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• v.47 no.3
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• pp.459-470
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• 2012

This study attempts to estimate the economic effects of extreme climate events on agriculture with the case of Gangwon-do, drawing upon the Ricardian approach based upon the panel data on extreme climate events, soil and geography, farmland prices, and economic and social variables for the 11 municipal units of Gangwon-do during the period of 1993-2010. Our empirical analysis shows that the heavy rainfall-related extreme climate variable negatively affects the prices of rice paddy and dry farm field. The summer-related extreme temperature variables have negative economic impacts on the land values of both farmlands, while the winter-related ones positively affect them except for the extreme cold wave variable.

• Water Engineering Research
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• v.6 no.2
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• pp.63-71
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• 2005

Nowadays Climate disasters are frequently happening due to occasional occurrences of EI Nino and La Nina events and among them, water shortage is one of the serious problems. To cope with this problem, climate model simulations can give very helpful information. To utilize the climate model for enhancing the water resources planning techniques, probabilistic measures of the effectiveness of global climate model (GCM) simulations of an indicator variable for discriminating high versus low regional observations of a target variable are proposed in this study. The objective of this study is to present the various analysis methods to find the suitable application methods of GCM information for Korean water resources planning. The basic formulation uses the significance probability of the Kolmogorov-Smirnov test for detecting differences between two variables. The various methods for adopting correct association, changing the window size, discrimination condition, and the use of temporally down scaled data were proposed to find out the suitable way for Korean water resources planning.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.303-303
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• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.249-249
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• 2023

Spring drought forecasting in South Korea is essential due to the sknewness of rainfall which could lead to water shortage especially in spring when managed without prediction. Therefore, drought forecasting over South Korea was performed in the current study by thoroughly searching appropriate predictors from the lagged global climate variable, mean sea level pressure(MSLP), specifically in winter season for forecasting time lag. The target predictand defined as accumulated spring precipitation(ASP) was driven by the median of 93 weather stations in South Korea. Then, it was found that a number of points of the MSLP data were significantly cross-correlated with the ASP, and the points with high correlation were regionally grouped. The grouped variables with three regions: the Arctic Ocean (R1), South Pacific (R2), and South Africa (R3) were determined. The generalized linear model(GLM) was further applied for skewed marginal distribution in drought prediction. It was shown that the applied GLM presents reasonable performance in forecasting ASP. The results concluded that the presented regionalization of the climate variable, MSLP can be a good alternative in forecasting spring drought.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.444-452
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• 2022

This research aims to investigate the relationship between safety climate and safety perception and safety behavior. Safety perception of the relationship is considered to have a mediating effect. Previous literature has tended to regard safety perception as an independent variable at the same level as the safety climate, which can be said to depend on behavioralism to approach the causal relationship to an one-way perspective. The survey was administrated through full- service carries in Korea such as Korean Air and Asiana Airlines, and low-cost carriers such as JeJu air, Jin air, and Air Pusan. It can identify a mediator of safety perception between safety climate and safety behavior. There are significant indirect effects of each value, which means mediators values of safety perception of safety climate variables and safety behavior. The study highlights that airlines should focus on the importance of their psychological aspects to strengthen the safety behavior of flight attendants and the value of organizational efforts to mature safety perceptions, suggesting some implications of theoretical and practical aspects.