• Journal of the Korean earth science society
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• v.30 no.6
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• pp.734-743
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• 2009

In order to examine how climatological condition can influence on urban scale particulate air pollutants, single and cross spectrum analysis have been performed to daily mean concentrations of particulate matters ($PM_{10}$) in Busan together with the climatological variables over the Asian dust source regions. Single power spectrum analysis of $PM_{10}$ concentrations in Busan shows that, aside from the typical and well-known periodicities, 3-4 year of peak periodicity of power spectrum density was identified. In cross spectrum analysis, this 3-4 year periodicity is found to have a strong positive correlation with the wind speed and pressure, and negative with the temperature and relative humidity, which is rather consistent with both characteristics of air mass during the Asian dust event whose periodicities have been recorded inter-annually over the Korean urban cities. Over the Asian dust source regions, $PM_{10}$ vs. precipitation shows no significant periodicity from the time series of precipitation data, but the periodicity of EDI (Effective Drought Index) shows some interannual variabilities ranging from 2 to 4 years over the various source regions, suggesting that, rather than precipitation itself, the EDI could be more closely associated with the occurrence frequency of Asian dust and interannual variability of urban particle concentrations in Korean cities.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.6
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• pp.412-420
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• 2014

Water quality variations were investigated at 4 locations of Jungrang river (upper, middle and lower basins) during a period of 3 rainfall events. During the rainfall, concentrations of $COD_{Mn}$, SS and $BOD_5$ significantly increased, while the concentration of T-N decreased and that of T-P remained relatively constant. This pattern became more apparent as the level of accumulative precipitation and rainfall intensity increased. Simple regression analysis showed that the accumulative precipitation was positively correlated with all water quality pollutants except for T-N. With increasing accumulative precipitation, the concentration of T-N decreased, while the others increased. $R^2$ of simple regressions of hourly average rainfall intensity and water quality pollutants, showed wider range of variation ranged from 0.483 to 0.992, which indicated a strong correlation. The stronger the hourly average rainfall intensity, the more T-N and T-P in the upper basin, more $COD_{Mn}$ in the middle and lower basins, more SS with gradual increase from upper to lower areas, and more $BOD_5$ with gradual decrease from upper to lower region. Simple regression showed that water quality pollution in the upper basin was more sensitive to an increase of rainfall discharge than that in the middle and lower areas.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.41-53
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• 2008

Unprecedented weather phenomena are occurring because of climate change: extreme heavy rains, heat waves, and severe rain storms after the rainy season. Recently, the frequency of these abnormal phenomena has increased. However, regular pattern or cycles cannot be found. Analysis of annual data or annual average data, which has been established a research method of climate change, should be applied to find frequency and tendencies of extreme climate events. In this paper, extreme indicators of precipitation and temperature marked by objectivity and consistency were established to analyze data collected by 66 observatories throughout Korea operated by the Meteorological Administration. To assess the statistical significance of the data, linear regression and Kendall-Tau method were applied for statistical diagnosis. The indicators were analyzed to find tendencies. The analysis revealed that an increase of precipitation along with a decrease of the number of rainy days. A seasonal trend was also found: precipitation rate and the heavy rainfall threshold increased to a greater extent in the summer(June-August) than in the winter (September-November). In the meanwhile, a tendency of temperature increase was more prominent in the winter (December-February) than in the summer (June-August). In general, this phenomenon was more widespread in inland areas than in coastal areas. Furthermore, the number of winter frost days diminished throughout Korea. As was mentioned in the literature, the progression of climate change has influenced the increase of temperature in the winter.

• Journal of Wetlands Research
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• v.14 no.1
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• pp.131-137
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• 2012

This study was investigated to characterize the stormwater runoff pollutant materials from the urban area mixed with industrial area. Almost the similar rainfall pattern is shown during the 5 years, and the yearly precipitation was 5.2~6.6 mm. The first flushing effect during the early-stage rainfall-run off was observed in some events. EMC ranges are 19.3~39.9 mg/L for BOD, 45.2~190 mg/L for CODcr, 67.2~351 mg/L for TSS, 3.6~10.3 mg/L for TN, 1.2~2.5 mg/L for TP. Heavy metal are not detected except Zn which is observed at only one event. The particle size was distributed to 10 ${\mu}m$ at the 3% weight volume and the 50% cumulative weight percent was shown at 12 ${\mu}m$.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.2
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• pp.179-185
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• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

• Journal of Korean Society on Water Environment
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• v.22 no.2
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• pp.314-320
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• 2006

Diffuse pollution from an urban area contributes to the significant pollution loading to a receiving water body. In this paper, rainfall runoffs from an urban basin with combined sewer systems located in the city of Daejeon were monitored to measure the rainfall runoff discharge rates and pollutant concentrations. Strong first flush effects were observed for all monitored rainfall runoffs. The first flush effects were closely related to rainfall intensity, while suspended solids were closely related to pollutant constituents. The observed averaged Event Mean Concentrations (EMCs) of Combined Sewer Overflows (CSOs) were 536.1 mg SS/L, 467.7 mg CODcr/L, 142.7 mg BOD/L, 16.5 mg TN/L, and 13.5 mg TP/L. Storage volumes for containing the first flush to improve water quality of the receiving stream can be estimated based on suspended solid concentration. In this study, retainment of the first flush equivalent to 5mm of precipitation could reduce diffuse pollution loading induced by CSOs to a receiving water body by up to 80% of suspended solid loading.

• The Korean Journal of Quaternary Research
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• v.19 no.1
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• pp.41-46
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• 2005

This study discusses the soil erosion on hillslopes and sediment deposition in lakes within catchments in South Korea. In order to determine seasonal variations of sedimentation in Yeongcheon and Seondong lakes, the sediment traps were set in the deep part of both lakes and lake sediments have been sampledmonthly from July 2004 to August 2005. Some properties such as highmineral content, fine particle size and high particle density in the Yeongcheon Lake indicate intensive soil erosion, sediment transportation and deposition throughout the catchment for a long time. The high sediment yield in the Seondong Lake is related with higher weathering intensity and extreme soil erosion by running water due to higher seasonal rainfall amount. Rates of erosion and sedimentation in the Seondong Lake are estimated to be higher than those of the Yeongcheon Lake, suggesting that the Seondong Lake is associated with higher precipitation, smaller catchment area, and extreme soil vulnerability to ephemeral erosion by overland flow during the heavy rainfall event. Consequently, both catchments are characterized by different erosion and sedimentation processes, as well as different geomorphic factors (bedrock, soil structure, rainfall intensity and catchment area).

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.4
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• pp.11-19
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• 2011

PADDIMOD2 was deloped to estimate nonpoint source pollution from paddy rice fields. The PADDIMOD2 was enhanced to estimate runoff and pollutant load during non-growing as well as growing season and to be easily used for public by development of Excel based system. Nutrient concentration and hydrology were based on Dirac delta function and continuous source function, and tank model for growing season and Event Mean Concentrations (EMCs) and SCS-Curve Number method for non-growing season. The PADDIMOD2 consists of three main component (input data, parameters data, and output data) by including eight Excel spread sheets. As a result of model application, total precipitation and irrigation were 1,051.7 mm and 439.2 mm, respectivley and surface runoff and water loss including infiltration and evapotranspiration were 463.0 mm and 947.9 mm, respectively. Annual nutrient loadings of T-N and T-P from study area were 6.7 kg/$km^2$/day and 0.5 kg/$km^2$/day, respectively. Development of PADDIMOD2 was focused on minimizing input data and maximizing user friendly system and is expected to be useful tool to evaluate various non-structure BMPs and estimate unit load from paddy rice fields for application at Korean TMDL.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.166-166
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• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.371-384
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• 2014

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.