• Earthquakes and Structures
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• v.12 no.2
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• pp.251-262
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• 2017

The present research intends to study the effects of the seismic soil-foundation-structure interaction (SFSI) on the dynamic response of various buildings. Two methods including direct and Cone model were studied through 3D finite element method using ABAQUS software. Cone model as an approximate method to consider the SFSI phenomenon was developed and evaluated for both high and low rise buildings. Effect of soil nonlinearity, foundation rigidity and embedment as well as friction coefficient between soil-foundation interfaces during seismic excitation are investigated. Validity and performance of both approaches are evaluated as reference graphs for Cone model and infinite boundary condition, soil nonlinearity and amplification factor for direct method. A series of calculations by DeepSoil for inverse earthquake record modification was conducted. A comparison of the two methods was carried out by root-mean-square-deviation (RMSD) tool for maximum lateral displacement and story shear forces which verifies that Cone model results have good agreement with direct method. It was concluded that Cone method is a convenient, fast and rather accurate method as an approximate way to count for soil media.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.174-179
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• 2017

Soil parameters are required inputs to crop models, which estimate crop yield under a given environment condition. The Korean Soil Information System (KSIS), which provides detailed soil profile record of 390 soil series in the HTML (HyperText Markup Language) format, would be useful to prepare soil input files. Korean Soil Information System Processing Tool (KSISPT) was developed to aid generation of soil input data based on the KSIS database. Java was used to implement the tool that consists of a set of modules for parsing the HTML document of the KSIS, storing data required for preparing soil input file, calculating additional soil parameter, and writing soil input file to a local disk. Using the automated soil data preparation tool, about 940 soil input data were created for the DSSAT model and the ORYZA 2000 model, respectively. In combination with soil series distribution map at 30m resolution, spatial analysis of crop yield could be projected under climate change, which would help the development of adaptation strategies.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.327-338
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• 2022

This study aims to empirically and statistically predict soil depths across areas affected by landslides. Using soil depth measurements from a landslide area in Korea, two sets of soil depths are calculated using a Z-model based on terrain elevation and a probabilistic statistical model. Both sets of calculation results are applied to derive landslide risk using the saturated infiltration depth ratio of the soil layer. This facilitates analysis of the infiltration of rainfall into soil layers for a rainfall event. In comparison with the probabilistic statistical model, the Z-model yields soil depths that are closer to measured values in the study area. Landslide risk assessment in the study area based on soil depth predictions from the two models shows that the percentage of first-grade landslide risk assessed using soil depths from the probabilistic statistical model is 2.5 times that calculated using soil depths from the Z-model. This shows that soil depths directly affect landslide risk assessment; therefore, the acquisition and application of local soil depth data are crucial to landslide risk analysis.

• 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 Water Resources Association Conference
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• 2021.06a
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• pp.150-150
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• 2021

Soil erosion due to climate change is one of the global environmental issues. Especially, Korea is vulnerable to soil erosion as the frequency of extreme rainfall events and rainfall intensity are increasing. Soil erosion causes various problems such as reduced farmlands, deterioration of water quality in rivers, etc. To these severe problems, understanding the process of soil erosion is the first process. Then, it is necessary to quantify and analyze soil ersoion using an erosion model. Soil erosion models are divided into empirical, conceptual, and physics-based models according to the structures and characteristics of models. This study used GSSHA (Gridded Surface Subsurface Hydrologic Analysis), the physics-based erosion model, running on WMS (Watershed Modeling System) to analyze soil erosion vulnerability of the CheonCheon watershed. In addition, we compared the six sediment transport capacity formulas provided in the model and evaluated the equations fir on this study site. Therefore, this result can be as a primary tool for soil conservation management.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.152-152
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• 2018

Soil moisture is a important factor in hydrologic analysis. So, if we have spatially distributed soil moisture data, it can help to study much research in a various field. Recently, there are a lot of satellite derived soil moisture data, and it can be served through web freely. Especially, NASA (National Aeronautics and Space Administration) launched the Soil Moisture Aperture Passive (SMAP) satellite for mapping global soil moisture on 31 January 2015. SMAP data have many advantages for study, for example, SMAP data has higher spatial resolution than other satellited derived data. However, becuase many satellited derived soil moisture data have a limitation to data accuracy, if we have ancillary materials for improving data accuracy, it can be used. So, in this study, after applying the alogorithm, which is data assimilation methods, applicability of satellite derived soil moisture data was analyzed. Among the various data assimilation methods, in this study, Model Output Statistics (MOS) technique was used for improving satellite derived soil moisture data. Model Output Statistics (MOS) is a type of statistical post-processing, a class of techniques used to improve numerical weather models' ability to forecast by relating model outputs to observational or additional model data.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.1065-1078
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• 2009

The shear strength has been managed as an important factor in soil mechanics. The shear strength estimation model was developed to evaluate the shear strength using only a few soil properties by the linear regression analysis model which is one of the statistical methods. The shear strength is divided into two part; one is the internal friction angle ($\Phi$) and the other is the cohesion (c). Therefore, some valid soil factors among the results of soil tests are selected through the correlation analysis using SPSS and then the model are formulated by the linear regression analysis based on the relationship between factors. Also, the developed model is compared with the result of direct shear test to prove the rationality of model. As the results of analysis about relationship between soil properties and shear strength, the internal friction angle is highly influenced by the void ratio and the dry unit weight and the cohesion is mainly influenced by the void ratio, the dry unit weight and the plastic index. Meanwhile, the shear strength estimated by the developed model is similar with that of the direct shear test. Therefore, the developed model may be used to estimate the shear strength of soils in the same condition of study area.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1997-2002
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• 2007

This study selected soil erosion source area of Dechung basin by soil erosion estimation model and field survey for effective soil conservation planning and management. First, unit soil erosion amount of Dechung basin is analyzed using RUSLE (Revised Universal Soil Loss Equation) model based on DEM (Digital Elevation Model), soil map, landcover map and rainfall data. Soil erosion model is difficult to analyze the tracing route of soil particle and to consider the characteristics of bank condition and the types of crop, multidirectional field survey is necessary to choice the soil erosion source area. As the result of analysis of modeling value and field survey, Mujunamde-, Wondang-, Geumpyong stream are selected in the soil erosion source area of Dechung basin. Especially, these areas show steep slope in river boundary and cultivation condition of crop is also weakness to soil erosion in the field survey.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.1-8
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• 2019

Carbon dioxide is one of the major driving forces causing climate changes, and many countries have been trying to reduce carbon dioxide emissions from various sources. Soil stores more carbon dioxide(two to three times) amounts than atmosphere indicating that soil organic carbon emission management are a pivotal issue. In this study, we developed a Soil Organic Carbon(SOC) storage estimation model to predict SOC storage amounts in soils. Also, SOC storage values were assessed based on the carbon emission price provided from Republic Of Korea(ROK). Here, the SOC model calculated the soil hydraulic properties based on the soil physical and chemical information. Base on the calculated the soil hydraulic properties and the soil physical chemical information, SOC storage amounts were estimated. In validation, the estimated SOC storage amounts were 486,696 tons($3.526kg/m^2$) in Jindo-gun and shown similarly compared to the previous literature review. These results supported the robustness of our SOC model in estimating SOC storage amounts. The total SOC storage amount in ROK was 305 Mt, and the SOC amount at Gyeongsangbuk-do were relatively higher than other regions. But the SOC storage amount(per unit) was highest in Jeju island indicating that volcanic ashes might influence on the relatively higher SOC amount. Based on these results, the SOC storage value was shown as 8.4 trillion won in ROK. Even though our SOC model was not fully validated due to lacks of measured SOC data, our approach can be useful for policy-makers in reducing soil organic carbon emission from soils against climate changes.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.4
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• pp.246-254
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• 2015

This study explored the effect of rainfall pattern and soil characteristics on water management in rice paddy fields, using a soil water balance model, BUDGET. In two sites with different soil textural group, coarse loamy soil (Gangseo series) and fine soil (Hwadong series), respectively, we have monitored daily decrease of water depth, percolation rate, and groundwater table. The observed evapotranspiration (ET) was obtained from differences between water depth decrease and percolation rate. The root mean square difference values between observed and BUDGET-estimated ET ranged between 10% and 20% of the average observed ET. This is comparable to the measurement uncertainty, suggesting that the BUDGET model can provide reliable ET estimation for rice fields. In BUDGET model of this study, irrigation requirement was determined as minimum water need for maintaining water-saturated soil surface, assuming 100 mm of bund height and no lateral loss of water. The model results showed different water balance and irrigation requirement with the different soil profile and indicated that minimum percolation rate by plow pan could determine the irrigation requirement of rice paddy field. For the condition of different rainfall distribution, the results presented different irrigation period and amounts, representing the importance of securing water for irrigation against different rainfall pattern.