• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.s02
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• pp.13-33
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• 1989

Agroclimate should be analyzed and evaluated accurately to make better use of available chimatic resources for the establishment of optimum cropping systems. Introducing of appropriate cultivars and their cultivation techniques into classified agroclimatic zone could contribute to the stability and costs of crop production. To classify the agroclimatic zones, such climatic factors as temperature, precipitation, sunshine, humidity and wind were considered as major influencing factors on the crop growth and yield. For the classification of rice agroclimatic zones, precipitation and drought index during transplanting time, the first occurrence of effective growth temperature (above 15$^{\circ}C$) and its duration, the probability of low temperature occurrence, variation in temperature and sunshine hours, and climatic productivity index were used in the analysis. The agroclimatic zones for rice crop were classified into 19 zones as follows; (1) Taebaek Alpine Zone, (2) Taebaek Semi-Alpine Zone, (3) Sobaek Mountainous Zone, (4) Noryeong Sobaek Mountainous Zone, (5) Yeongnam Inland Mountainous Zone, (6) Northern Central Inland Zone, (7) Central Inland Zone, (8) Western Soebaek Inland Zone, (9) Noryeong Eastern and Western Inland Zone, (10) Honam Inland Zone, (ll) Yeongnam Basin Zone, (12) Yeongnam Inland Zone, (13) Western Central Plain Zone, (14) Southern Charyeong Plain Zone, (15) South Western Coastal Zone, (16) Southern Coastal Zone, (17) Northern Eastern Coastal Zone, (18) Central Eastern Coastal Zone, and (19) South Eastern Coastal Zone. The classification of agroclimatic zones for cropping systems was based on the rice agroclimatic zones considering zonal climatic factors for both summer and winter crops and traditional cropping systems. The agroclimatic zones were identified for cropping systems as follows: (I) Alpine Zone, (II) Mountainous Zone, (III) Central Northern Inland Zone, (IV) Central Northern West Coastal Zone, (V) Cental Southern West Coastal Zone, (VI) Gyeongbuk Inland Zone, (VII) Southern Inland Zone, (VIII) Southern Coastal Zone, and (IX) Eastern Coastal Zone. The agroclimatic zonal characteristics of climatic disasters under rice cultivation were identified: as frequent drought zones of (11) Yeongnam Basin Zone, (17) North Eastern Coastal Zone with the frequency of low temperature occurrence below 13$^{\circ}C$ at root setting stage above 9.1％, and (2) Taebaek Semi-Alpine Zone with cold injury during reproductive stages, as the thphoon and intensive precipitation zones of (10) Hanam Inland Zone, (15) Southern West Coastal Zone, (16) Southern Coastal Zone with more than 4 times of damage in a year and with typhoon path and heavy precipitation intensity concerned. Especially the three east coastal zones, (17), (18), and (19), were subjected to wind and flood damages 2 to 3 times a year as well as subjected to drought and cold temperature injury.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Horticultural Science & Technology
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• v.30 no.6
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• pp.650-656
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• 2012

The aims of the experiment, conducted over three years in the Central Europe field conditions, were (1) to investigate the effect of growing period (plantings in the middle and at the end of August: $1^{st}$ and $2^{nd}$ term, respectively) on yield and chemical composition of the non-heading Chinese cabbage (Brassica rapa var. chinensis) cultivars 'Taisai', 'Pak Choy White', and 'Green Fortune', and (2) to develop regression models to evaluate the changes in crop yields as a function of weather conditions. A highest marketable yield was obtained from 'Taisai' (65.71 and 77.20 $t{\cdot}ha^{-1}$), especially in the $2^{nd}$ term of production. Low yield, observed for 'Pak Choy White' was due to its premature bolting. Almost 39% ($1^{st}$ term) and 70% ($2^{nd}$ term) of plants of this cultivar formed inflorescence shoots before harvest. The highest dry matter level was observed in the leaf petioles of 'Taisai', while 'Green Fortune' was the most abundant of carotenoids and L-ascorbic acid. The content of soluble sugars was the lowest for 'Pak Choy White'. In a phase of harvest maturity, more of the analyzed constituents were gathered by plants from earlier plantings, and differences were as follows: 4.7% (dry matter), 26.3% (carotenoids) and 22.1% (L-ascorbic acid), in comparison to $2^{nd}$ term of production. Significant increase of soluble sugars level was observed for plants from later harvest. The regression model for marketable yield of Chinese cabbage cultivar 'Taisai' as a function of maximum air temperature can predict the yield with accuracy 68%. The models for yield or bolting of 'Pak Choy White', based on extreme air temperatures and sunshine duration, were more precise (98%). It should be pointed out that Taisai could be recommended for later growing period in Central Europe conditions with regard to maximum yield potential. 'Green Fortune' was notable for its uniform yielding. To obtained plants of higher nutritional value, earlier time of cultivation should be suggested. Described models can be successfully applied for an approximate simulation of Chinese cabbage yielding.

• Korean Journal of Medicinal Crop Science
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• v.20 no.5
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• pp.359-364
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• 2012

This study were carried out to find bolting response of cultivation in different regions and to isolate FLC (FLOWERING LOCUS C) homologs in Angelica gigas Nakai. The mean temperature of different regions, ordering in altitude, were as follows: 100 m > 350 m > 530 m > 700 m. The largest amount of rainfall was occurred in the region of 350 m while the longest time of sunshine was occurred in the region of 100 m. The content of soil chemical properties in regions showed pH 6.2 ~ 7.4, T-N 0.17 ~ 26, organic mater $1{\sim}32gkg^{-1}$, $P_2O_5$ ${151{\sim}664_{mgkg}}^{-1}$, exchangeable potassium and calcium and magnesium were 0.78 ~ 1.15, 3.9 ~ 10.0, ${0.7{\sim}3.2_{cmol}}^{+kg-1}$. L5 line of A. gigas was occurred in bolting at all regions, but the bolting ratio was 60.0% in 700 m region with non-mulching treatment. Manchu of A. gigas was not occurred in bolting at all regions. The accumulation bolting ratio of L5 line by non-mulching was higher than that of mulching as 90.4% and 72.8% in 100 m region. The MADS-box transcription factor FLC is one of the well-known examples as a strong floral repressor. We decided to isolate FLC homologs from A. gigas as a starting point of flowering mechanism research of this plant. We have isolated two RT-PCR products which showed very high amino acid sequence homology to Arabidopsis FLC.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.58-69
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• 2007

The vegetation area that occupies 76% in land surface of the earth can give a considerable impact on water resources, environment and ecological system by future climate change. The purpose of this study is to predict future vegetation cover information from NDVI (Normalized Difference Vegetation Index) extracted from satellite images. Current vegetation information was prepared from monthly NDVI (March to November) extracted from NOAA AVHRR (1994 - 2004) and Terra MODIS (2000 - 2004) satellite images. The NDVI values of MODIS for 5 years were 20% higher than those of NOAA. The interrelation between NDVIs and monthly averaged climate factors (daily mean, maximum and minimum temperature, rainfall, sunshine hour, wind velocity, and relative humidity) for 5 river basins of South Korea showed that the monthly NDVIs had high relationship with monthly averaged temperature. By linear regression, the future NDVIs were estimated using the future mean temperature of CCCma CGCM2 A2 and B2 climate change scenario. The future vegetation information by NOAA NDVI showed little difference in peak value of NDVI, but the peak time was shifted from July to August and maintained high NDVIs to October while the present NDVI decrease from September. The future MODIS NDVIs showed about 5% increase comparing with the present NDVIs from July to August.

• 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.

• The Korean Journal of Archival Studies
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• no.63
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• pp.167-225
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• 2020

The author conducted a records research named 'daily life and records.' The purpose of the research was to find an archive, if possible, that would be effective in promoting exchange and cooperation among people in their daily lives, and to distinguish what type of archive it would be, as well as how to let it naturally take place in their ordinary lives. For 4 months (August-December 2019) with 100 college students in their 20s, trial and error were repeated. There was no separate laboratory for the research, and it used regular school hours at universities. Although it is true that there was a control through power by the college system, the plot was centered on the sunshine policy. To human being there is a voluntary and positive attitude. If anyone begins to take this attitude it is difficult to stop such action. Through emotional support, this voluntary action was encouraged to take root. The experiment was an attempt to doubt the obvious, and to search for something new. From afar, this may seem irrelevant to archives. However, for the author who is a professional archivist, it was a time of records through control by Records principles. By organizing into a form of story, its archival implications are observed.

• Journal of Korean Society of Forest Science
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• v.85 no.1
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• pp.15-23
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• 1996

Preparing the era of forest resources management requires studies on forest fire. This study attempted to develop forest fire occurrence model using meteorological characteristics for the practical purposes of forecasting forest fire danger rate. To accomplish this goal, the relationships between forest fire occurrence and meteorological characteristics are estimated. In the process, the forest fire occurrence pattern of the study region(Taegu-Kyungpook) is categorized by employing qualification IV method. The study region was divided into three areas such as, Taegu, Andong and Pohang area. The meteorological variables emerged as affective to forest fire occurrence are relative humidity, longitude of sunshine, and duration of precipitation. To estimate the probability of forest fire danger, forest fire occurrence of three areas are regressed on the time series data of affective meteorological variables using logistic and probit model. The effectiveness of the models estimated are tested and showed acceptable degree of goodness. Those models developed would be helpful to increase the efficiency of forest fire management such as detection of forest fire occurrence and effective disposition of forest fire fight equipments.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.4
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• pp.113-120
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• 2008

The increase in average air temperature over the past 100 years in northern Asia including Korea is the greatest (about ${1.5}^{\circ}C$) among the various regions of the world. Considering a further warming projected by the IPCC, fluctuations of agro-climatic indices under climate change must precede an evaluation of vulnerability. The purpose of this study is to analyze how climate changes represented by global warming have altered agro-climatic indices in Korea over various time scales. Drought index during the rice-transplanting period of 15 May to 5 June has changed toward the favorable with recently increased precipitation in the Taebaek Alpine and Semi-Alpine Zone, and Yeongnam Basin and Inland Zone. The frequency of low temperature occurrence below $13^{\circ}C$ during the rice transplanting has decreased, while climatic production index (CPI) has fallen because of the decreased sunshine hour and increased temperature during the rice ripening period. We therefore concluded that the recent change of climate conditions was against the rice productivity in Korea.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.17-27
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• 2019

In this study, we conducted a study to predict the photovoltaic power by constructing the sensor based meteorological data observation system and the accurate terrain data obtained by using LiDAR surveying. The average sunshine hours in 2018 is 4.53 hours and the photovoltaic power is 2,305 MWh. In order to analyze the effect of photovoltaic power on the installation angle of solar modules, we installed module installation angle at $10^{\circ}$ intervals. As a result, the generation time was 4.24 hours at the module arrangement angle of $30^{\circ}$, and the daily power generation and the monthly power generation were the highest, 3.37 MWh and 102.47 MWh, respectively. Therefore, when the module arrangement angle is set to $30^{\circ}$, the generation efficiency is increased by about 4.8% compared with the module angle of $50^{\circ}$. As a result of analyzing the influence of the seasonal photovoltaic power by the installation angle of the solar module, it was found that the photovoltaic power was high in the range of $40^{\circ}{\sim}50^{\circ}$, where the module angle was large from November to February when the weather was cold. From March to October, it was found that the photovoltaic power amount is $10^{\circ}{\sim}30^{\circ}$ with small module angle.