• Journal of Soil and Groundwater Environment
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• v.16 no.5
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• pp.82-89
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• 2011

Understanding snowmelt movement to the watershed is crucial for both climate change and hydrological studies because the snowmelt is a significant component of groundwater and surface runoff in temperature area. In this work, a new energy balance budget algorithm has been developed for melting snow from a snowpack at the Central Sierra Snow Laboratory (CSSL) in California, US. Using two sets of experiments, artificial rain-on-snow experiments and observations of diel variations, carried out in the winter of 2002 and 2003, we investigate how to calculate the amount of snowmelt from the snowpack using radiation energy and air temperature. To address the effect of air temperature, we calculate the integrated daily solar radiation energy input, and the integrated discharge of snowmelt under the snowpack and the energy required to generate such an amount of meltwater. The difference between the two is the excess (or deficit) energy input and we compare this energy to the average daily temperature. The resulting empirical relationship is used to calculate the instantaneous snowmelt rate in the model used by Lee et al. (2008a; 2010), in addition to the net-short radiation. If for a given 10 minute interval, the energy obtained by the melt calculation is negative, then no melt is generated. The input energy from the sun is considered to be used to increase the temperature of the snowpack. Positive energy is used for melting snow for the 10-minute interval. Using this energy budget algorithm, we optimize the intrinsic permeability of the snowpack for the two sets of experiments using one-dimensional water percolation model, which are $52.5{\times}10^{-10}m^2$ and $75{\times}10^{-10}m^2$ for the artificial rain-on-snow experiments and observations of diel variation, respectively.

• Journal of Korean Society on Water Environment
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• v.28 no.3
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• pp.453-459
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• 2012

In this study, Song-Cheon river Basin located at Dae-kwan-lyong Meyn in Kang-Won Province was monitored to analyze characteristics of non-point source pollution during the snowmelt period. The characteristics of NPS pollution such as runoff, change in water quality and pollutant loads by snowmelt were investigated from 23th February 2010 to 27th April 2010. The results showed that The climate change effects flow, turbidity and concentration of SS and COD with showing similar trend of fluctuation. Daily pollutant loads per unit area were SS 74.43 kg/ha/day, COD 1.25 kg/ha/day, BOD 0.21 kg/ha/day, TN 0.717 kg/ha/day, TP 0.141 kg/ha/day and the flow weight mean concentration has been SS 985.97 mg/L, COD 16.28 mg/L, BOD 2.67 mg/L, TN 9.302 mg/L, TP 1.834 mg/L, respectively. The flow and pollutant loads both of SS and of COD significantly increased during the snowmelt period. Because structurally loosen soil by freezing and thawing deteriorates water quality, research and management measures will be needed.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.241-246
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• 2005

Doam watershed is located at alpine areas in the Kangwon province. The annual average precipitation, including snow accumulation during the winter, at the Doam watershed is significantly higher than other areas. Thus, pollutant laden runoff and sediment discharge from the alpine agricultural fields are causing water quality degradation at the Doam watershed. To estimate soil erosion from the agricultural fields, the Universal Soil Loss Equation (USLE) has been widely used because of its simplicity to use. The USLE rainfall erosivity (R) factor is responsible for impacts of rainfall on soil erosion. Thus, use of constant R factor for the Doam watershed cannot reflect variations in precipitation patterns, consequently soil erosion estimation. In the early spring at the Doam watershed, the stream flow increases because of snow melt, which results in erosion of loosened soil experiencing freezing and thaw during the winter. However, the USLE model cannot consider the impacts on soil erosion of freezing and thaw of the soil. Also, it cannot simulate temporal changes in USLE input parameters. Thus, the Soil and Water Assessment Tool (SWAT) model was investigated for its applicability to estimate soil erosion at the Doam watershed, instead of the widely used USLE model. The SWAT hydrology and erosion/sediment components were validated after calibration of the hydrologic component. The $R^2$ and Nash-Sutcliffe coefficient values are higher enough, thus it was found the SWAT model can be efficiently used to simulate hydrology and sediment yield at the Doam watershed. The effects of snow melt on SWAT estimated stream flow and sediment were investigated using long-term precipitation and temperature data at the Doam watershed. It was found significant amount of flow and sediment in the spring are contributed by melting snow accumulated during the winter. Thus, it is recommend that the SWAT model capable of simulating snow melt and long-term weather data needs to be used in estimating soil erosion at alpine agricultural land instead of the USLE model for successful soil erosion management at the Doam watershed.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.565-568
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• 2008

Domestic area of most be happened chloride deicer damage. Because daily mean temperature is below 0$^{\circ}C$ from the area of domestic most. Concrete durability influence Air Content. Presently, We used to AE(air-entraining agent) for increase freeze-thaw durability. So, on concrete Air Spacing ratio used $200{\mu}m{\sim}230{\mu}m$ in Canada and under $250{\mu}m$ in Japan institution. Use of Air content has been and will continue to be a major part of concrete durability and scaling. Chloride-containing chemicals such as calcium chloride or rock salt are main deicers for the road. The prepared optimum mix concrete in this study show that freeze-thaw and scaling resistance of Non-AE(air content 1.5%) and AE (air content 4.5%, 7.2%). Solution concentrations of deicing agent were good result, and the pore system and change of hydration products is not difference comparing before freeze-thaw test.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.5
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• pp.130-135
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• 2013

Anti-icing system can reduce traffic accidents and congestion by quickly removing the frozen road surface area. there is no decision criteria for determining application of the Anti-icing system in Korea. In this study, we proposed the decision criteria for determining application of the anti-icing system based on weather and road conditions, i.e., geometric and topographic conditions. Regional climate survey, Solar Radiation analysis, and dynamic vehicle simulation considered road geometry was conducted to standardize the installation method of anti-icing system. Also, we have developed a software that can be determined an installation of anti-icing system.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.56-60
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• 2008

Increasing urban sprawl and climate changes have been causing unexpected high-intensity rainfall events. Thus there are needs to enhance conventional disaster management system for comprehensive actions to secure safety. Therefore long-term and comprehensive flood management plans need to be well established. Recently torrential snowfall are occurring frequently, causing have snow traffic jams on the road. To secure safety and on-time operation of the Bi-modal tram system, well-structured disaster management system capable of analyzing the urban flash flooding and snow pack melt/freezing due to unexpected rainfall event and snowfall are needed. To secure safety of the Bi-modal tram system due to torrential snowfall, the snow melt simulation capability was investigated. The snow accumulation and snow melt were measured to validate the SWMM snow melt component. It showed that there was a good agreement between measured snow melt data and the simulated ones. Therefore, the Bi-modal tram disaster management system will be able to predict snow melt reasonably well to secure safety of the Bi-modal tram system during the winter. The Bi-modal tram disaster management system can be used to identify top priority area for snow removal within the tram route in case of torrential snowfall to secure on-time operation of the tram. Also it can be used for detour route in the tram networks based on the disaster management system predicted data.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.3
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• pp.87-95
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• 2021

The Doam Lake Watershed is one of Gangwon-do's non-point source management areas. This area has a lot of snowfall in winter, and it is expected that there will be a lot of soil erosion in early spring due to snow melting. In this study, snow melting was monitored in the Doam Lake watershed from February to 3, 2020. It was conducted to analyze the water quality changes by calculating the concentration of non-point source pollution caused by snowmelt, and to compare the concentration of water quality during snowmelt event with rainfall and non-rainfall event. As a result of water quality analysis, Event Mean Concentration (EMC) at the first monitoring was SS 33.9 mg/L, TP 0.13 mg/L, TN 4.33 mg/L, BOD 1.35 mg/L, TOC 1.84 mg/L. At the second monitoring, EMC were SS 81.3 mg/L, TP 0.15 mg/L, TN 3.12 mg/L, BOD 1.32 mg/L, TOC 3.46 mg/L. In parameter except SS, it showed good water quality. It is necessary to establish management measures through continuous monitoring.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.427-427
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• 2023

일반적으로 수자원가용량이라 하면 지표·지하·토양 등에 있는 모든 수자원의 양이라 할 수 있다. 정확한 수자원가용량의 평가를 위해서는 강수, 기온 등의 기상 예측의 정확도 확보가 우선되어야 하며, 지표 하에 보이지 않는 수자원의 양을 정확히 평가할 수 있어야 한다. 한국건설기술연구원은 2012년부터 세계기상기구(WMO, World Meteorological Organization)에서 수자원평가 부문 리더 역할을 수행하면서 회원국들에게 수자원가용량평가를 위한 '동적수자원평가시스템'의 개발을 제안하여 추진하였다. 그 결과, 동적수자원평가시스템(Dynamic Water resources Assessment Tool, DWAT)이 2017년 12월에 개발되었고, 2019년 5월에는 WMO 웹사이트 (https://public.wmo.int/en/water/dynamic-water-resources-assessment-tool)를 통해 193개 회원국에 보급되기 시작하였다. DWAT은 전 세계가 무료로 이용할 수 있는 수자원평가 도구로, 지하수, 용수이용 뿐만 아니라 지표수를 고려한 수자원계획 및 관리를 위해 중⋅소규모 하천 유역에 적용될 수 있다. 특히, 논 지역의 유출특성을 모의할 수 있는 모듈을 탑재하였으며, 고위도 및 고산지대의 수문학적 특성을 반영할 수 있는 융설 모듈이 포함되었고, 매개변수 최적화 기능도 포함되었다. WMO는 수자원분야 주요사업 중 하나인 "전지구 수문현황 및 전망 시스템(HYDROSOS, global HYDROlogical Status and Outlook System)" 사업을 추진하고 있다. 본 사업은 전지구 기상예보를 활용하여 주요 지점의 자연 유출량에 대한 현황과 예보를 수행하는 것을 목표로 한다. 2019년 6월 제18차 WMO 총회에서는 수자원분야 주요 사업인 HYDROSOS의 시범사업을 DWAT이 지원하는 것으로 의결되었다. 따라서 이러한 DWAT의 활용을 통해 대한민국의 수자원 평가 실무와 관련된 기술이 WMO 회원국에 지속적으로 보급될 것으로 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.431-436
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• 2008

Recently, the rapid development of GIS technology has made it possible to handle a various data associated with spatially hydrological parameters with their attribute information. Therefore, there has been a shift in focus from lumped runoff models to distributed runoff models, as the latter can consider temporal and spatial variations of discharge. In this research, a distributed rainfall-runoff model based on physical kinematic wave for analysis of surface and river flow was used to simulate temporal and spatial distribution of long-term discharge. The snowfall and melting process model based on Hydro-BEAM was developed, and various hydrological parameters for input data of the model was extracted from basic GIS data such as DEM, land cover and soil map. The developed model was applied for the Shonai River basin(532) in Japan, which has sufficient meteorological and hydrological data, and displayed precise runoff results to be compared to the hydrograph.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.