• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.274-274
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• 2019

• Journal of Environmental Impact Assessment
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• v.19 no.1
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• pp.15-28
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• 2010

Because deterioration of air quality and urban heat island directly harm health of citizens, Health Impact Assessment (HIA) and Environmental Impact Assessment (EIA) for urban development projects needs to conduct analysis of their impacts objectively. This study aims to review appropriate methods for assessment of air quality used at each stage of urban development and to investigate prediction and assessment methods of urban heat island. In addition, by evaluating impacts of climate change following supposed urban construction performed in the central area of Korea on public health, it examines usefulness of HIA for urban construction. When urban heat island prediction and HIA method suggested in this study are applied to an imaginary city, they predict urban heat island properly and the impacts of climate changes on public health inside the city could be determined clearly by calculating life-climate index and bio-climate index related with thermal environment from the model.

• 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.

• Atmosphere
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• v.32 no.1
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• pp.17-25
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• 2022

As climate change due to global warming continues to be accelerated, various extreme events become more intense, more likely to occur and longer-lasting on a much larger scale. Recent studies show that global warming acts as the primary driver of extreme events and that heat-related extreme events should be attributed to anthropogenic global warming. Among them, both terrestrial and marine heat waves are great concerns for human beings as well as ecosystems. Taking place around the world, one of those events appeared over East Sea in July 2021 with record-breaking high temperature. Meanwhile, climate condition around East Sea was favorable for anomalous warming with less total cloud cover, more incoming solar radiation, and shorter period of Changma rainfall. According to the results of wave activity flux analysis, highly activated meridional mode of teleconnection that links western North Pacific to East Asia caused localized warming over East Sea to become stronger.

• Atmosphere
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• v.34 no.3
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• pp.319-323
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• 2024

It has been recognized that interannual relationship between Northeast Asian and western North Pacific (WNP) summer monsoon intensities has a negative correlation with a statistical significance. This teleconnection can be understood by the responses to the stationary Rossby wave, which is forced by variability of the western North Pacific summer monsoon intensity. In addition, the relationship between two monsoon intensities have a large variability on decadal time-scale associated with adjacent climate variability. The study for the recent changes in these long-term relationships has not been reported so far. This study suggests the recent relationship between Northeast Asian and WNP summer monsoons with an extension of the analysis period in the previous studies. Based on the reanalysis datasets, this study also shows atmospheric teleconnection changes associated with El Nino in summertime during the different decadal periods. This study also suggests the possible reasons for the analysis results in terms of teleconnection changes.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.309-310
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• 2019

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2015.08a
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• pp.89-93
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• 2015

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.24-30
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• 2024

This paper investigates the identification and modeling of a climate greenhouse. Given real climate data from greenhouse installed in the LAPER laboratory in Tunisia, the objective of this paper is to propose a solution of the problem of nonlinear time variant inputs and outputs of greenhouse internal climate. Based on fuzzy logic technique combined with least mean squares (lms) a robust greenhouse climate model for internal temperature prediction is proposed. The simulation results are presented to demonstrate the effectiveness of the identification approach and the power of the implemented Takagi-Sugeno Fuzzy model based Algorithm.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.