• Korean Journal of Agricultural Science
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• v.38 no.2
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• pp.219-225
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• 2011

Changes in biota, species distribution range shift and catastrophic climate influence due to recent global warming have been observed during the last century. Since global warming affects various sectors, such as agriculture and vegetation, it is important to predict more accurate impact of future climate change. The purpose of this study is to examine the observed distribution of Abeliophyllum distichum in the Korean peninsula. For this purpose, two period (present and future) climate data were used. Mean data between 1950 and 2000, were used as the present value and the year 2050 and 2080 data from A1B senario in IPCC SRES were used for the future value. Potential habitation is analyzed by MaxEnt(Maximum Entropy model), and Abeliophyllum distichum's coordinates data were used as a dependent variable and independent variables are composed of environmental data such as BioClim, altitude, aspect and slope. The result of six types GCM mean calculation, the potential habitability decreased by 40-60% of the average existing distribution. The methodogies and results of this research can be applicable to the climate changing adaptation stratiegies for the biodiversity conservation.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.32-43
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• 2014

To identify major causes of the global environment changes arising from extreme and unusual weather patterns occurring these days, and to foresee future environmental changes, it is highly important to shed light on the correlation between climate changes and global environment system. To investigate the correlation between climate changes and global environment system, it calls for establishing an integrated climate-environment DB for analyzing comparatively the data on climatic changes and global environment system. In the preceding studies, we researched an XML-based integrated climate-environment DB and developed a management system for the DB. However, the existing integrated climate-environment DB, designed and installed only for individual PCs, does not allow multiple users 'simultaneous access. Accordingly, it fails to systematically update and sharing data which is being generated continuingly. Hence, this study aims to develop an easy-to-use GIS-based integrated DB management system by improving the existing integrated climate-environment DB through the adoption of the client/server model. For this, this study collected and analyzed climate and environment data prior to designing and building a DBMS-based integrated DB. In addition, in order for multidisciplinary researchers to easily get access and apply the integrated DB, this study designed and developed a GIS-based integrated DB management system using a client/server model which facilitates connections with multiple PCs. The GIS-based integrated climate-environment DB management system makes it easier to efficiently manage and locate scattered climate-environment data. It is also expected that the DB system will bring the effects in saving time and cost by avoiding the overlapping generation of data in the areas of integrated climate-environment research.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.169-169
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• 2020

According to the IPCC analysis, severe climate changes are projected to occur in Korea as the temperature is expected to rise by 3.2 ℃, the precipitation by 15.6% and the sea level by 27cm by 2050. It is predicted that the occurrence of abnormal climate phenomena - especially those such as increase of concentrated precipitation and extreme heat in the summer season and severe drought in the winter season - that have happened in Korea in the past 30 years (1981-2010) will continuously be intensified and accelerated. As a result, the impact on and vulnerability of the water management sector is expected to be exacerbated. This research aims to predict the climate change impacts on streamflow of Daecheong Lake area of Geum River in South Korea during the summer and winter seasons, which show extreme meteorological events, and ultimately develop an integrated policy model in response. We projected and compared the streamflow changes of Daecheong Lake area of Geum River in South Korea in the near future period (2020-2040) and the far future period (2041-2060) with the reference period (1991-2010) using the HEC-HMS model. The data from a global climate model HadGEM2-AO, which is the fully-coupled atmosphere-ocean version of the Hadley Centre Global Environment Model 2, and RCP scenarios (RCP4.5 and RCP8.5) were used as inputs for the HEC-HMS model to identify the river basins where cases of extreme flooding or drought are likely to occur in the near and far future. The projections were made for the summer season (July-September) and the winter season(November-January) in order to reflect the summer monsoon and the dry winter. The results are anticipated to be used by policy makers for preparation of adaptation plans to secure water resources in the nation.

• Ocean Systems Engineering
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• v.7 no.4
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• pp.319-328
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• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• Atmosphere
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• v.30 no.3
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• pp.311-318
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• 2020

Impacts on the atmospheric circulation and ocean system over the Mediterranean during boreal summer are investigated using Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations (from 1911 to 2005). As the climate warms, global and remote effects lead to a strengthening in descending motion, an increase in sea surface temperature (SST) and surface dryness, but a decrease in marine primary production over the Western Mediterranean. The global effect is estimated from interannual variability over the global mean SST and the remote effect is driven by diabatic forcing generated from the South and East Asian summer monsoons. On the other hand, a local contribution leads to the strengthened descending motion and increased surface dryness over the Eastern Mediterranean, whereas the marine primary production over this region tends to increase due to possibly the urban wastewater and sewage. Our result suggests that particular attention needs to be paid to conserve the marine ecosystem over the Mediterranean.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.47-47
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• 2023

Drought occurs due to lack of water resources over an extended period and its intensity has been magnified globally by climate change. In recent years, drought over South Korea has also been intensed, and the prediction was inevitable for the water resource management and water industry. Therefore, drought forecasting over South Korea was performed in the current study with the following procedure. First, accumulated spring precipitation(ASP) driven by the 93 weather stations in South Korea was taken with their median. Then, correlation analysis was followed between ASP and Df4m, the differences of two pair of the global winter MSLP. The 37 Df4m variables with high correlations over 0.55 was chosen and sorted into three regions. The selected Df4m variables in the same region showed high similarity, leading the multicollinearity problem. To avoid this problem, a model that performs variable selection and model fitting at once, least absolute shrinkage and selection operator(LASSO) was applied. The LASSO model selected 5 variables which showed a good agreement of the predicted with the observed value, R²=0.72. Other models such as multiple linear regression model and ElasticNet were also performed, but did not present a performance as good as LASSO. Therefore, LASSO model can be an appropriate model to forecast spring drought over South Korea and can be used to mange water resources efficiently.

• Atmosphere
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• v.21 no.1
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• pp.17-33
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• 2011

A dynamical downscaling system for seasonal forecast has been constructed based on a regional climate model, and its predictability was investigated for 10 years' wintertime (December-January-February; DJF) climatology in East Asia. Initial and lateral boundary conditions were obtained from the operational seasonal forecasting data, which are realtime output of the Global Data Assimilation and Prediction System (GDAPS) at Korea Meteorological Administration (KMA). Sea surface temperature was also obtained from the operational forecasts, i.e., KMA El-Nino and Global Sea Surface Temperature Forecast System. In order to determine the better configuration of the regional climate model for East Asian regions, two sensitivity experiments were carried out for one winter season (97/98 DJF): One is for the topography blending and the other is for the cumulus parameterization scheme. After determining the proper configuration, the predictability of the regional forecasting system was validated with respect to 850 hPa temperature and precipitation. The results showed that mean fields error and other verification statistics were generally decreased compared to GDAPS, most evident in 500 hPa geopotential heights. These improved simulation affected season prediction, and then HSS was better 36% and 11% about 850 hPa temperature and precipitation, respectively.

• Atmosphere
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• v.18 no.1
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• pp.33-41
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• 2008

This study provides an impact assesment of climate change on energy consumption, based on active-deal scenario. This approach assumes that the amount of electric energy consumption depends on human spontaneous acts against local (REC) has ben developed by using monthly mean temperature and monthly amount of electric energy consumption in the 6 major cities over the 19-205 period. The statistical model is utilized to estimate the past and future REEC, and to assess the economic benefits and damage in energy consumption sector. For an estimation of the future REEC, climate change scenario, which is generated by National Institute of Meteorological Research, is utilized in this study. According to the model, it is estimated that over the standard period (1999~2005), there might be economic benefits of about 31 bilion Won/year in Seoul due to increasing temperature than in the 1980s. The REC is also predicted to be gradually reduced across the Korean peninsula since the 2020s. These results suggest that Korea will gain economic benefits in the REC sector during the 21st century as temperature increases under global warming scenarios.

• Atmosphere
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• v.16 no.3
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• pp.215-223
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• 2006

Among observational, local-environmental, and large-scale factors causing significant changes in climate records, the site relocations and the replacement of the instruments are well-known nonclimatic factors for the analysis of climatic trends, climatic variability, and for the detection of anthropogenic climate change such as heat-island effect and global warming. Using dataset that were contaminated by these nonclimatic factors can affect seriously the assessment of climatic trends and variability, and the detection of the climatic change signal. In this paper, the inhomogeneities, which have been caused by relocation of the observation site, in the climate data of Korea Meteorological Administration (KMA) were examined using two-phase regression model. The observations of pan evaporation and wind speed are more sensitive to site relocations than those of other meteorological elements, such as daily mean, maximum and minimum temperatures, with regardless to region.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.413-413
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• 2023

Wetlands are a critical component of the global carbon cycle and are essential in mitigating climate change. Accurately quantifying wetland carbon emissions is crucial for understanding and predicting the impact of wetlands on the global carbon budget. The uncertainty quantifying carbon in wetlands may comes from the ecosystem's hydrological, biochemical, and microbiological variability. The Community Land Model is a sophisticated and flexible land surface model that offers several configuration options such as energy and water fluxes, vegetation dynamics, and biogeochemical cycling, necessitating careful consideration for the alternative configurations before model implementation to develop a practical model framework. We conducted a systematic literature review, analyzing the alternatives, focusing on the carbon stock pools configurations and the parameters with significant sensitivity for carbon quantification in wetlands. In addition, we evaluated the feasibility and availability of in situ observation data necessary for validating the different alternatives. This analysis identified the most suitable option for our study site, the Binbong Wetland, in Korea.