• Journal of Environmental Science International
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• v.4 no.5
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• pp.437-446
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• 1995

The temporal and spatial distribution of atmospheric transmissivity and depletion rate of solar radiation are investigated, and are compared to the concentration of several components of air pollution. The length of the data span is 11 years from 1983 to 1993. The data of radiation and sunshine rate recorded at 20 meteorological standard stations were used, and in order to investigate a relationship between the depletion rate of solar radiation and air pollution, the concentration data of air pollution observed in Seoul, Pusan, Taegu, Taejon and Kwangju were compiled from 1991 to 1993. Regression coefficient a and b vary from 0.100 to 0.209, from 0.464 to 0.691, and their means are 0.163 and 0.533, respectively. Climatological atmospheric transmissivity is ranged from 0.68 to 0.83, and its mean is 0.75. Atmospheric transmissivity is relatively low in Pusan, Taejon, Kwangju and Inchon which have large population and are highly industrialized. However, that in Chinju, Mokpo, Cheju and Sosan appears to be large compared to the aforementioned stations. Insolation rate of clear days varies from 0.71 to 0.58, and its mean is 0.63. Insolation rate of Kangnung and Chinju are higher than those of Seoul and Pusan by 5%. From the correlation coefficients between depletion rate of solar radiation and air pollution concentration, the most significant factors related to the depletion rate of solar radiation is appeared to be TSP followed by $SO_2$. Ozone shows a negative correlation, End $NO_2$ does not show a obvious correlation with the depletion rate of solar radiation.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.231-231
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• 2020

Unlike conventional treatment technologies, the performance of nature-based facilities were susceptible to seasonal changes and climatological variabilities. This study evaluated the effects of seasonal variables on the treatment performance of constructed wetlands (CWs). Two CWs treating runoff and discharge from agricultural and livestock areas were monitored to determine the efficiency of the systems in reducing particulates, organics, and nutrients in the influent. For all four seasons, the mean effluent suspended solids concentration in the agricultural CW (ACW) increased by -2% to -39%. The occurrence of algal blooms in the system during summer and fall seasons resulted to the greatest increase in the amount of suspended materials in the overlying water. unlike ACW, the livestock CW (LCW) performed efficiently throughout the year, with mean suspended solids removal amounting to 61% to 68%. Algal blooms were still present in LCW seasonally; however, the constant inflow in the system limited the proliferation of phytoplankton through continuous flushing. The total nitrogen (TN) and total phosphorus (TP) removal efficiencies in ACW were higher during the summer (21% to 25%) and fall (8% to 21%) seasons since phytoplankton utilize nitrogen and phosphorus during the early stages of phytoplankton blooms. In the case of LCW, the most efficient reduction in TN (24%) and TP (54%) concentrations were also noted in summer, which can be attributed to the favorable environmental conditions for microbial activities. The mean removal of organics in ACW was lowest during summer season (-52% to 35%), wherein the onset of algal decay triggered a relative increase in organic matter and stimulate bacterial growth. The removal of organics in LCW was highest (54 % to 55%) during the fall and winter seasons since low water temperatures may limit the persistence of various algal species. Variations in environmental conditions due to seasonal changes can greatly affect the performance of CW systems. This study effectively established the contributory factors affecting the feasibility of utilizing CW systems for treating agricultural and livestock discharges and runoff.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.7
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• pp.952-961
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• 2003

In the first part of the study, rates of estrus occurrence and success of A.I. service in the Thai-native and Friesian crossbred, and purebred Friesian cows fed in the National Dairy Training and Applied Research Institute in Chiang Mai, Thailand were traced monthly throughout a year. An electric fan and a water sprinkler cooled the stall for the purebred cows during the hot season (March-September). Both rates in pure Friesians were at their highest in the cold-dry season (October- February), but they decreased steadily during the hot-dry season (March-May) and were at their lowest in the hot-wet season (June-September). Seasonal change of a similar pattern was observed in the incidence of estrus, but not in the success rate of insemination in the crossbred cows. By the use of reproductive data, compiled in the same institute, on the 75 % cross- and purebred Friesian cows, and climatological data in Chiang Mai district, effects of ambient temperature and humidity on the reproductive traits of cows were examined by regression analysis in the second half of the study. Significant relationships in the crossbred, expressed by positive-linear and parabola regressions, were found between reproductive parameters such as days to the first estrus (DTFE), A.I. service (DTFAI), and conception, the number of A.I. services required for conception and some climatic factors. However, regarding this, no consistent or intelligible results were obtained in purebred cows, perhaps because electric fans and water sprinklers were used for this breed in the hot season. Among climatic factors examined, the minimum temperature (MINT) in early lactation affected reproductive activity most conspicuously. As the temperature during one or two months prior to the first estrus and A.I. service rose, DTFE and DTFAI steadily became longer, although, when MINT depleted below $17-18^{\circ}C$, the reproductive interval tended to be prolonged again on some occasions. The maximum temperature also affected DTFE and DTFAI, but only in limited conditions. The effect of humidity was not clear, although the inverse relationship between DTFE and minimum humidity during 2 months before the first estrus in the crossbred seemed to be significant. Failure to detect any definite effect of climate on the reproductive traits of pure Friesians seemed to indicate that forced ventilation by electric fans and water sprinklers were effective enough to protect the reproductive ability of this breed from the adverse effects of a hot climate.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.735-742
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• 2019

Drought is a social phenomenon in which the degree of perception varies depending on the affected factors, and is defined as various relative concepts such as meteorological drought, hydrological drought, agricultural drought, and climatological drought. In this study, a comparative analysis of meteorological drought among variously defined droughts was conducted and the applicability of the drought index was examined by comparing the actual drought cases and the results of meteorological drought index analysis. In order to compare the drought index, we used standardized Precipitation Index (SPI), China-Z Index (CZI), Modified CZI (MCZI) and Z-Score Index Respectively. Four drought indices were used for the Taebaek and Sokcho areas. The drought index was analyzed using the meteorological data from 1986 to 2015 for a duration of 3 months. As a result of the analysis, the SPI drought index was analyzed to be highly reproducible for the case of drought with past limited water series. In the case of CZI and MCZI drought indices, the number of extreme dry occurrences is similar to that of the past cases, but the reproducibility is low for the actual drought years. In the case of ZSI drought index, it is analyzed that the number of occurrences and the comparison with the past cases are inferior in reproducibility. For the meteorological drought index using precipitation, it would be effective to use the SPI drought index with the highest reproducibility and the past drought case.

• Journal of the Korean earth science society
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• v.33 no.7
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• pp.596-607
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• 2012

The R-Z relationship is one of important error factors to determine the accuracy of radar rainfall estimation. In this study, we have explored the effect of the R-Z relationships derived from disdrometer data in estimating the radar rainfall. The heavy rain event that produced flooding in St-Remi, Quebec, Canada has been occurred. We have tried to investigate the severity of rain for this event using high temporal (2.5 min) and spatial resolution ($1^{\circ}$ by 250 m) radar data obtained from the McGill S-band radar. Radar data revealed that the heavy rain cells pass directly over St-Remi while the coarse raingauge network was not sufficient to detect this rain event. The maximum 30 min (1 h) accumulation reaches about 39 (42) mm in St-Remi. During the rain event, the two disdrometers (POSS; Precipitation Occurrence Sensor System) were available: One used for the reflectivity calibration by comparing disdrometer Z and radar Z and the other for deriving disdrometric R-Z relationships. The result shows the significant improvement with the disdrometric reflectivity-dependent R-Z relationships against the climatological R-Z relationship. The bias in radar rain estimation is reduced from +12% to -2% and the root-mean squared error from 16 to 10% for daily accumulation. Using the estimated radar rainfall rate with disdrometric R-Z relationships, the flood event was well captured with proper timing and amount.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.1
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• pp.53-62
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• 1982

An effort of preliminary type has been made to develope a practical method for the waterway area determination of a drainage outlet in rural or agricultural areas. The Seoul meteorological station was selected as tile index station, and the maximum rainfalls-duration-frequency (R-D-F) relation of short-time intense rainfalls was first established. A frequency analysis of the daily rainfalls for the 75 stations selected throughout the country resulted the 50-year daily rainfall for each station. The rainfall factor, which is defined here as the ration of 50-year daily rainfalls of individual station and the index station, was determined for the 8 climatological regions divided in this study. Following the US SCS method the runoff number of a watershed was given based on the soil type, land-use pattern, and the surface treatment. With this runoff number and the R-D-F relationship the runoff factors for the index station were computed and hence a nomogram could be drawn which makes it possible to determine the runoff factor for a given rainfall number and a rainfall of specific duration and frequency. With this done, the potential runoff of a watershed for a given rainfall duration could be calculated, based on the unit hydrograph theory, by multiplying the rainfall factor, the runoff factor, and the drainage area of the watershed under consideration. Then, the maximum runoff potential was determined by varying the rainfall duration and finding out the duration which results the peak discharge of a gived return period.

• Journal of Korea Water Resources Association
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• v.47 no.12
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• pp.1199-1212
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• 2014

This study proposes an evaluation method of water supply capacity of a dam, which uses the concept of return period by conducting bivariate frequency analysis of dam storage capacity. The proposed method was applied to the Daecheong Dam for the evaluation. Additionally, the return periods of Daecheong Dam were estimated for the representative drought events in Korea, whose results were also reviewed. Summarizing the results is as follows. First, this study evaluated several climatological factors related to the water supply capacity of dams in Korea to conduct the bivariate frequency analysis and selected the storage on May and the storage difference between June and October as variables for analysis. Second, as an evaluation result of the water supply capacity of the Daecheong Dam, it was found that the Daecheong Dam secures the water supply capacity under 20 years of return period. Finally, it was also confirmed that the proposed method in this study is valid to analyze and estimate the return period of representative drought events occurred in the Korean peninsula.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.3
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• pp.209-218
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• 2024

The National Institute of Agricultural Sciences (NAS) operates in-house long-range climate forecasting system to support the agricultural use of climate forecast data. This system, developed through collaborative research with Pusan National University, is based on the PNU/RDA Coupled General Circulation Model (CGCM) and includes the regional climate model WRF (Weather Research and Forecasting). It generates detailed climate forecast data for periods ranging from 1 to 6 months, covering 20 key variables such as daily maximum, minimum, and average temperatures, precipitation, and agricultural meteorological elements like solar radiation, soil moisture, and ground temperature-factors essential for agricultural forecasting. The data are provided at a daily temporal resolution with a spatial resolution of a 5km grid, which can be used in point form (interpolated) or averaged across administrative regions. The system's seasonal temperature and precipitation forecasts align closely with observed climatological data, accurately reflecting spatial and topographical influences, confirming its reliability. These long-range forecasts from NAS are expected to offer valuable insights for agricultural planning and decision-making. The detailed forecast data can be accessed through the Climate Change Assessment Division of NAS.