• Atmosphere
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• v.21 no.3
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• pp.257-271
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• 2011

The aim of this study is to examine weather modification by urbanization and human activities. The characteristics of the urban heat island (UHI) and precipitation in Seoul metropolitan area of Korea are investigated to demonstrate that cities can change or modify local and nearby weather and climate, and to confirm that cities can initiate convection, change the behavior of convective precipitation, and enhance downstream precipitation. The data used in this study are surface meteorological station data observed in Seoul and its nearby 5 cities for the period of 1960 to 2009, and 162 Automatic Weather System stations data observed in the Seoul metropolitan area from 1998 to 2009. Air temperature and precipitation amount tend to increase with time, and relative humidity decreases because of urbanization. Similar to previous studies for other cities, the average maximum UHI is weakest in summer and is strong in autumn and winter, and the maximum UHI intensity is more frequently observed in the nighttime than in the daytime, decreases with increasing wind speed, and is enhanced for clear skies. Relatively warm regions extend in the east-west direction and relatively cold regions are located near the northern and southern mountains inside Seoul. The satellite cities in the outskirts of Seoul have been rapidly built up in recent years, thus exhibiting increases in near-surface air temperature. The yearly precipitation amount during the last 50 years is increased with time but rainy days are decreased. The heavy rainfall events of more than $20mm\;hr^{-1}$ increases with time. The substantial changes observed in precipitation in Seoul seem to be linked with the accelerated increase in the urban sprawl in recent decades which in turn has induced an intensification of the UHI effect and enhanced downstream precipitation. We also found that the frequency of intense rain showers has increased in Seoul metropolitan area.

• Atmosphere
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• v.18 no.2
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• pp.147-158
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• 2008

Domestic IOP (intensive observing period) has mostly been represented by the KEOP (Korea Enhanced Observing Period), which started the 5-yr second phase in 2006 after the first phase (2001-2005). During the first phase, the KEOP had focused on special observations (e.g., frontal systems, typhoons, etc.) around the Haenam supersite, while extended observations have been attempted from the second phase, e.g., mountain and downstream meteorology in 2006 and heavy rainfall in the mid-central region and marine meteorology in 2007. So far the KEOP has collected some useful data for severe weather systems in Korea, which are very important in understanding the development mechanisms of disastrous weather systems moving into or developing in Korea. In the future, intensive observations should be made for all characteristic weather systems in Korea including the easterly in the central-eastern coastal areas, the orographically-developed systems around mountains, the heavy snowfall in the western coastal areas, the upstream/downstream effect around major mountain ranges, and the heavy rainfall in the mid-central region. Enhancing observations over the seas around the Korean Peninsula is utmost important to improve forecast accuracy on the weather systems moving into Korea through the seas. Observations of sand dust storm in the domestic and the source regions are also essential. Such various IOPs should serve as important components of international field campaign such as THORPEX (THe Observing system Research and Predictability EXperiment) through active international collaborations.

• Journal of the Korean earth science society
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• v.44 no.3
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• pp.185-195
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• 2023

The prolonged and heavy East Asian summer precipitation in 2020 may have been caused by an enhanced Madden-Julian Oscillation (MJO), which requires evaluation using forecast models. We examined the performance of GloSea6, an operational forecast model, in predicting the East Asian summer precipitation during July 2020, and investigated the role of MJO in the extreme rainfall event. Two experiments, CON and EXP, were conducted using different convection schemes, 6A and 5A, respectively to simulate various aspects of MJO. The EXP runs yielded stronger forecasts of East Asian precipitation for July 2020 than the CON runs, probably due to the prominent MJO realization in the former experiment. The stronger MJO created stronger moist southerly winds associated with the western North Pacific subtropical high, which led to increased precipitation. The strengthening of the MJO was found to improve the prediction accuracy of East Asian summer precipitation. However, it is important to note that this study does not discuss the impact of changes in the convection scheme on the modulation of MJO. Further research is needed to understand other factors that could strengthen the MJO and improve the forecast.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.885-894
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• 2008

The deposition of solids in combined sewer systems results in a loss of flow capacity that may restrict flow and cause a local flooding and enhanced solids deposition. In order to solve these problems and proper pipe management, estimations of solid loads on land surface in a drainage basin and solid deposition in sewer system due to rainfall are needed but these tasks are very difficult and very expensive. In this study, procedures for estimating solid loads on surface in a drainage basin were applied and analyzed in Gunja drainage basin in Korea. Also, this paper presents the development and application of estimation equation for solid deposition in sewer system due to rainfall based on the solid deposition estimated using MOUSE model. As results, the comparison between estimated and measured solid deposition is difficult due to the absent of measured data, but the estimated values using developed equations show applicability compared with the results of MOUSE model and the application of the other basin. The developed estimation equations can be used usefully for the management of combined sewer system.

• Journal of Korea Water Resources Association
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• v.41 no.6
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• pp.565-574
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• 2008

Fire-enhanced soil hydrophobicity often increases runoff and erosion in the mountain hillslope following severe wildfires. Estimation techniques for WEPP's parameters were studied in burnt mountain slopes. In burnt mountain slopes, the model over-predicted runoff in the small runoff and under-predicted runoff in the great runoff, and in the lower sediment runoff it had a tendency to over-predict soil loss. The effective hydraulic conductivity was most sensitive in the WEPP's runoff and its sediment runoff was mainly effected by the effective hydraulic conductivity, initial saturation, rill erodibility, and interrill erodibility. To improve the applicability of the WEPP, the adjustment coefficient of effective hydraulic conductivity was defined for runoff and the adjustment coefficient of rill erodibility and interrill erodibility was presented for sediment runoff. The adjustment coefficient of effective hydraulic conductivity in wildfire mountain slopes increased with maximum rainfall intensity of single storm and the vegetation height index. The adjustment coefficients of rill erodibility depended on soil components of size distribution curve and total rainfall depths in single storm. The adjustment coefficients of interrill erodibility decreased with increases of maximum rainfall intensity and vegetation height index. These results may be used in the application of WEPP model for wildfire mountain slopes.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.19-30
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• 2013

This study is to evaluate the use of dual-polarization radar data for storm runoff modeling in Namgang dam (2,293 $km^2$) watershed using KIMSTORM (Grid-based KIneMatic wave STOrm Runoff Model). The Bisl dual-polarization radar data for 3 typhoons (Khanun, Bolaven, Sanba) and 1 heavy rain event in 2012 were obtained from Han River Flood Control Office. Even the radar data were overall less than the ground data in areal average, the spatio-temporal pattern between the two data was good showing the coefficient of determination ($R^2$) and bias with 0.97 and 0.84 respectively. For the case of heavy rain, the radar data caught the rain passing through the ground stations. The KIMSTORM was set to $500{\times}500$ m resolution and a total of 21,372 cells (156 rows${\times}$137 columns) for the watershed. Using 28 ground rainfall data, the model was calibrated using discharge data at 5 stations with $R^2$, Nash and Sutcliffe Model Efficiency (ME) and Volume Conservation Index (VCI) with 0.85, 0.78 and 1.09 respectively. The calibration results by radar rainfall showed $R^2$, ME and VCI were 0.85, 0.79, and 1.04 respectively. The VCI by radar data was enhanced by 5 %.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.563-574
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• 2023

Recently, climate change has affected functional responses of river basins to meteorological variables, emphasizing the importance of rainfall-runoff simulation research. Simultaneously, the growing interest in machine learning has led to its increased application in hydrological studies. However, it is not yet clear whether machine learning models are more advantageous than the conventional conceptual models. In this study, we compared the performance of the conventional GR6J model with the machine learning-based Random Forest model across 38 basins in Korea using both gauged and ungauged basin prediction methods. For gauged basin predictions, each model was calibrated or trained using observed daily runoff data, and their performance was evaluted over a separate validation period. Subsequently, ungauged basin simulations were evaluated using proximity-based parameter regionalization with Leave-One-Out Cross-Validation (LOOCV). In gauged basins, the Random Forest consistently outperformed the GR6J, exhibiting superiority across basins regardless of whether they had strong or weak rainfall-runoff correlations. This suggest that the inherent data-driven training structures of machine learning models, in contrast to the conceptual models, offer distinct advantages in data-rich scenarios. However, the advantages of the machine-learning algorithm were not replicated in ungauged basin predictions, resulting in a lower performance than that of the GR6J. In conclusion, this study suggests that while the Random Forest model showed enhanced performance in trained locations, the existing GR6J model may be a better choice for prediction in ungagued basins.

• Atmosphere
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• v.27 no.1
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• pp.17-28
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• 2017

The sensitivity of the typhoon track and intensity simulation to physics schemes of the global model are examined for the typhoon Bolaven and Tembin cases by using the Global/Regional Integrated Model System-Global Model Program (GRIMs-GMP) with the physics package version 2.0 of the Korea Institute of Atmospheric Prediction Systems. Microphysics, Cloudiness, and Planetary boundary Layer (PBL) parameterizations are changed and the impact of each scheme change to typhoon simulation is compared with the control simulation and observation. It is found that change of microphysics scheme from WRF Single-Moment 5-class (WSM5) to 1-class (WSM1) affects to the typhoon simulation significantly, showing the intensified typhoon activity and increased precipitation amount, while the effect of the prognostic cloudiness and PBL enhanced mixing scheme is not noticeable. It appears that WSM1 simulates relatively unstable and drier atmospheric structure than WSM5, which is induced by the latent heat change and the associated radiative effect due to not considering ice cloud. And WSM1 results the enhanced typhoon intensity and heavy rainfall simulation. It suggests that the microphysics is important to improve the capability for typhoon simulation of a global model and to increase the predictability of medium range forecast.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.752-757
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• 2009

In the last decade, many methods such as greet chamber, reservoir, or debris barrier, have been utilized to manage and prevent muddy water problem. The Vegetative Filter Strip (VFS) has been thought to be one of the most effective methods to trap sediment effectively. The VFS are usually installed at the edge of agricultural areas adjacent to stream or drainage ditches, and it has been shown that the VFS effectively removes pollutants transported with upland runoff. But, if the VFS is installed without any scientific analysis of rainfall-runoff characteristics, soil erosion, and sediment analysis, it may not reduce the sediment as much as expected. Although Soil and Water Assessment Tool (SWAT) model has been used worldwide for many hydrologic and Non-Point Source Pollution (NPSP) analysis at a watershed scale. but it has many limitations in simulating the VFS. Because it considers only 'filter strip width' when the model estimates sediment trapping efficiency, and does not consider the routing of sediment with overland flow option which is expected to maximize the sediment trapping efficiency from upper agricultural subbasin to lower spatially-explicit filter strip. Therefore, the SWAT overland flow option between landuse-subbasins with sediment routing capability was enhanced with modifications in SWAT watershed configuration and SWAT engine. The enhanced SWAT can simulate the sediment trapping efficiency of the VFS in the similar way as the desktop VFSMOD-w system does. Also it now can simulate the effects of overland flow from upper subbasin to reflect the increased runoff volume at the receiving subbasin, which is what is occurring at the field if no diversion channel is installed. In this study, the enhanced SWAT model was applied to small watershed located at Jaun-ri in South Korea to simulate diversion channel and spatially-explicit VFS. It was found that approximately sediment can be reduced by 31%, 65%, 68%, with diversion channel, the VFS, and the VFS with diversion channel, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.133-133
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• 2021

The performance of radar Quantitative Precipitation Estimation (QPE) using Long Short-Term Memory (LSTM) networks in hydrological applications depends on either the quality of data or the three-dimensional CAPPI structure from the weather radar. While radar data quality is controlled and enhanced by the more and more modern radar systems, the effect of CAPPI structure still has not yet fully investigated. In this study, three typical and important types of CAPPI structure including inverse-pyramid, cubic of grids 3x3, cubic of grids 4x4 are investigated to evaluate the effect of CAPPI structures on the performance of radar QPE using LSTM networks. The investigation results figure out that the cubic of grids 4x4 of CAPPI structure shows the best performance in rainfall estimation using the LSTM networks approach. This study give us the precious experiences in radar QPE works applying LSTM networks approach in particular and deep-learning approach in general.