• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.14-14
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• 2022

Greenhouse gas emissions are one of the critical factors that drive change in rice cropping systems. Within this changing system, less water irrigation and chemical fertilizer are seriously considered, as well combining precision farming technologies with irrigation control. Water and phosphorus (P) fertilizer are two of the most critical inputs in rice cultivation. Due to the lack of water availability in the system, P fertilizer is not available, especially in acidic soil conditions. Moreover, the various types of abiotic stresses, such as drought, high temperature, salinity, submergence, and limited fertilizer result in significant yield loss in the system. Even in the late stage of growth, the waves caused by diseases and insects make the field more unfruitful. Therefore, agronomists and breeders need to identify the secondary phenotypes to estimate the yield loss of when stress appears. The prediction will be clearer if we have a set of markers tagging the causal variation and the associated precise phenotype indices. Although there have been various studies for abiotic stress tolerance, we still lack functional molecular markers and phenotype indices. This is due to the underlying challenges caused by environmental factors in highly unpredictable regional and yearly environmental conditions in the field system. Pupl (phosphorus uptake 1) is still known as the first QTL associated with phosphorus uptake and have been validated in different field crops. Interestingly, some pyramiding lines of Pupl and other QTLs for other stress tolerances showed preferable phenotypes in the yield. Precise physiological studies with the help of genomics are on-going and some results will be discussed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.515-525
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• 2014

Recently, because of increasing in downpour and typhoon, which are caused by climate changes, those sedimentation disasters, such as landslide and debris flow, have become frequent. Those sedimentation disasters take place in natural slope. In order to predict debris flow damage range within wide area, the response model is more appropriate than numerical analysis. However, to make a prediction using Random Walk Model, the regional parameters is needed to be decided, since the regional environments conditions are not always same. This random Walk Model is a probability model with easy calculation method, and simplified slope factor. The objective of this study is to calculate the optimal parameters of Random Walk Model for Umyeon mountain in Seoul, where the large debris flow has occurred in 2011. Debris flow initiation zones and sedimentation zones were extracted through field survey, aerial photograph and visual reading of debris flow before and after its occurrence via LiDAR DEM.

• Journal of Environmental Impact Assessment
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• v.19 no.2
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• pp.169-177
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• 2010

One of the most obvious climatic manifestations of urbanization in Korea is a trend towards higher air temperature. The trends of long-term annual temperature generally well describe the warming of urban areas. The increase of air temperature in urban area has been observed to the present since the meteorological observations in Korea began. The objective of this study is to explore the actual increase and the regional long-term trends of air temperature attributed to urbanization in the Korean Peninsula. Therefore, temperatures of the selected urban areas were compared with that of the surrounding rural areas, with the results varying by the application of the estimates of each region. The second objective is to separate the long-term trend of surface air temperature of global warming from urbanization and to find the actual temperature increase from urbanization in Korean peninsula. For the data analysis, daily air temperatures observed by the Korea Meteorological Administration (KMA) during between from 1961 and 2005 were used at five rural sites and cities. The re-analyzed surface air temperatures by the National Centers for Environmental Prediction (NCEP) was also carried out to compare the result from the observed air temperature in the Korean climate domain. In this study, the urban areas in Korea showed high increase rate of air temperature with $0.4^{\circ}C$ per decade during past 50 year period, while rural sites as Chupungryung with the $0.2^{\circ}C$ decadal increase rate. The analyses reflect that the urban area shows the high rate of temperature increase with $1.39^{\circ}C$ of regression value at the urban area, Seoul, and $0.43^{\circ}C$ at the rural site, Chupungnyeong during the period of 30 years. The temperature increas due to the urbanization only showed the increase range between $0.44^{\circ}C$ and $0.86^{\circ}C$, and the observed decrease in diurnal temperature range at five urban areas during the 30 years period.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.11-22
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• 2010

According to IPCC 4th Assessment Report, concentration of carbon dioxide has been increasing by 30% since Industrial Revolution. Most of IPCC $CO_2$ emission scenarios estimate that the concentration will reach up to double of its present level within 100-year if the current tendency continues. The global warming has resulted in the agro-climate change over the Korean Peninsula as well. Accordingly, it is necessary to understand the future agro-climate induced by the increase of greenhouse gases in terms of the agro-climatic indices in the Korean peninsula. In this study, the future climate is simulated by an atmosphere/ocean/land surface/sea ice coupled general circulation climate model, Pusan National University Coupled General Circulation Model(hereafter, PNU CGCM), and by a regional weather prediction model, Weather Research and Forecasting Model(hereafter, WRF) for the purpose of a dynamical downscaling. The changes of the vegetable period and the crop growth period, defined as the total number of days of a year exceeding daily mean temperature of 5 and 10, respectively, have been analyzed. Our results estimate that the beginning date of vegetable and crop growth periods get earlier by 3.7 and 17 days, respectively, in spring under the $CO_2$-doubled climate. In most of the Korean peninsula, the predicted frost days in spring decrease by 10 days. Climatic production index (CPI), which closely represent the productivity of rice, tends to increase in the double $CO_2$ climate. Thus, it is suggested that the future $CO_2$ doubled climate might be favorable for crops due to the decrease of frost days in spring, and increased temperature and insolation during the heading date as we expect from the increased CPI.

• Journal of the Korea Institute of Building Construction
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• v.15 no.5
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• pp.515-525
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• 2015

Currently, according to the climate change, serious damage by typhoon has been occurred in the world. In this respect, the research on the prediction model to minimize the damage from various natural disaster has been conducted in several developed countries. In the case of U.S, various models to predict building damage costs have been used widely in many organizations such as insurance companies and governments. In South Korea, although studies regarding damage prediction model according to typhoon have been conducted, the scope has been only limited to consider the property of typhoon. However, it is necessary to consider various factors such as typhoon information, geography, construction environment, and socio-economy factors to predict the damages. Therefore, to address this issue, first, correlation analysis is conducted between various variables based on the data of typhoon from 2003 to 2012. Second, the damage prediction model by using regression analysis is developed based on suggested influence factors. The findings of this study can be utilized to develop the model for predicting the damage costs of buildings by typhoon like HAZUS-MH of US.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.69-75
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• 2019

The frequency of natural disasters and the scale of damage are increasing due to the abnormal weather phenomenon that occurs worldwide. Especially, damage caused by natural disasters in coastal areas around the world such as Earthquake in Japan, Hurricane Katrina in the United States, and Typhoon Maemi in Korea are huge. If we can predict the damage scale in response to disasters, we can respond quickly and reduce damage. In this study, we developed damage prediction functions for Wind waves caused by sea breezes and waves during various natural disasters. The disaster report (1991 ~ 2017) has collected the history of storm and typhoon damage in coastal areas in Korea, and the amount of damage has been converted as of 2017 to reflect inflation. In addition, data on marine weather factors were collected in the event of storm and typhoon damage. Regression analysis was performed through collected data, Finally, predictive function of the sea turbulent damage by the sea area in 74 regions of the country were developed. It is deemed that preliminary damage prediction can be possible through the wind damage prediction function developed and is expected to be utilized to improve laws and systems related to disaster statistics.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.67-74
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• 2024

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method's accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.3
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• pp.23-27
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• 2008

The new conjunctive surface-subsurface flow model at a large scale was developed by using a 1-D Diffusion Wave (DW) model for surface flow interacting with the 3-D Volume Averaged Soil-moisture Transport (VAST) model for subsurface flow for the comprehensive terrestrial water and energy predictions in Land Surface Models (LSMs). A selection of numerical implementation schemes is employed for each flow component. The 3-D VAST model is implemented using a time splitting scheme applying an explicit method for lateral flow after a fully implicit method for vertical flow. The 1-D DW model is then solved by MacCormack finite difference scheme. This new conjunctive flow model is substituted for the existing 1-D hydrologic scheme in Common Land Model (CLM), one of the state-of-the-art LSMs. The new conjunctive flow model coupled to CLM is tested for a study domain around the Ohio Valley. The simulation results show that the interaction between surface flow and subsurface flow associated with the flow routing scheme matches the runoff prediction with the observations more closely in the new coupled CLM simulations. This improved terrestrial hydrologic module will be coupled to the Climate extension of the next-generation Weather Research and Forecasting (CWRF) model for advanced regional, continental, and global hydroclimatological studies and the prevention of disasters caused by climate changes.

• Journal of the Korean association of regional geographers
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• v.18 no.4
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• pp.473-493
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• 2012

Malaria infection is sensitively influenced by regional meteorological conditions along with global climate change. Remote sensing techniques have become an important tool for extraction of climatic and environmental factors, including rainfall, temperature, surface water, soil moisture, and land use, which are directly linked to the habitat qualities of malaria mosquitoes. Improvement of sensor fidelity with higher spatial and spectral resolution, new multinational sensor development, and decreased data cost have nurtured diverse remote sensing applications in malaria research. In 1984, eradication of endemic malaria was declared in Korea, but reemergence of malaria was reported in mid-1990s. Considering constant changes in malaria cases since 2000, the epidemiological management of the disease needs careful monitoring. Geographically, northmost counties neighboring North Korea have been ranked high in the number of malaria cases. High infection rates in these areas drew special attention and led to a hypothesis that malaria dispersion in these border counties might be caused by north-origin, malaria-bearing adult mosquitoes. Habitat conditions of malaria mosquitoes are important parameters for prediction of the vector abundance. However, it should be realized that malaria infection and transmission is a complex mechanism, where non-environmental factors, including human behavior, demographic structure, landscape structure, and spatial relationships between human residence and the vector habitats, are also significant considerations in the framework of medical geography.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.373-383
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• 2020

In this study we established an operational storm-surge system for the northwestern pacific ocean, based on the NEMO (Nucleus for European Modeling of the Ocean). The system consists of the tide and the surge models. For more accurate storm surge prediction, it can be completed not only by applying more precise depth data, but also by optimal parameterization at the boundaries of the atmosphere and ocean. To this end, we conducted several sensitivity experiments related to the application of available bathymetry data, ocean bottom friction coefficient, and wind stress and air pressure on the ocean surface during August~September 2018 and the case of typhoon SOULIK. The results of comparison and verification are presented here, and they are compared with POM (Princeton Ocean Model) based Regional Tide Surge forecasting Model (RTSM). The results showed that the RTSM_NEMO model had a 29% and 20% decrease in Bias and RMSE respectively compared to the RTSM_POM model, and that the RTSM_NEMO model had a lower overall error than the RTSM_POM model for the case of typhoon SOULIK.