• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.22-27
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• 1998

A grid-based soil moisture routing model(GRISMORM) was developed to assess the information of groundwater recharge using spatial data such as Landsat TM and digital elevation model etc.. The model predicts the hydrologic components, that is, surface runoff, subsurface runoff, baseflow and evapotranspiration at each grid elements by grid-based water balance computation.

• Korean Journal of Soil Science and Fertilizer
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• v.29 no.2
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• pp.92-106
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• 1996

Imaginary grass field was assumed suitable as the representative one for simplified estimation of local drought, and a moisture balance booking model computing drought was developed with the limited numbers of its determining factors, such as crop coefficient of the field, reservoir capacity of the soil, and the beginning point of drought as defined by soil moisture status. The maximum effective rainfall was assumed to be the same as the available free space of soil reservoir capacity. The model is similar to a definite depth evaporation pan, which stores rainfall as much as the available free space on the water in it and consumes the water by evaporation. When the pan keeps water less than a certain defined level, it is droughty. The model simulates soil moisture deficit on the assumed grass field for the drought estimation. The model can assess the water requirement, drought intensity, and the index of yield decrement due to drought. The influencing intensity indices of the selected factors were 100, 21, and 16 respectively for crop coefficient, reservoir capacity, and drought beginning point, determined by the annual water requirements as influenced by them in the model. The optimum values of the selected factors for the model were respectively 58% for crop coefficient defined on the energy indicator scale of the small copper pan evaporation, 50 mm for reservoir capacity on the basis of the average of experimentally determined values for sandy loam, loam, clay loam, and clay soils, and 65% of the reservoir capacity for the beginning point of drought.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.3
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• pp.123-135
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• 2006

Spatial variability and distribution map of soil properties and the relationships between soil properties and crop yields are not well characterized in agroecosystems that have been land leveled to facilitate more cultivation of the new reclaimed sloping highland. Potato, onion, carrot, Chinese cabbage and radish were grown on the coarse sandy loam soil in 2004. Soil moisture content, soil penetration resistance and crop yield were sampled in the $10m{\times}50m$ field consisted of five plots. Sampling sites of each cultivation plot were 33 for the soil moisture, 11 for the soil penetration and 33 for the crop yield. The results of semivariance analysis, most of models were shown spherical equation. The significant ranges of each spatial variability model for the soil moisture, soil penetration and crop yield were broad as 33-35 meters in the potato cultivation plot, and that in the Chinese cabbage cultivation plot was narrow as 5-6 meters. The coefficient of variances (C.V.) of moisture, penetration and yield were various from 14 to 59 percents in five cultivation plots. The highest C.V. of potato yield was 59 percents, and that of the radish cultivation plot was as low as 14 percents. The required sample numbers for the determination of soil moisture content, soil penetration resistance and crop yield with error 10% at 0.05 significant level were ranged 8-40 for soil moisture, 7-25 for soil penetration and 424-4,678 for crop yield. The variogram and distribution map by kriging described field characteristics well so that the spatial variability would be useful for soil management for better efficiency and precision agriculture in the reclaimed highland.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.6
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• pp.248-257
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• 1999

A mathematical model was developed to investigate the mechanical interrelation between soil characteristics and main design factors of a tracked vehicles , and predict the tractive performance of the tracked vehicles. Based on the mathematical model, a computer simulation program(TPPMTV98) was developed in this study. The effectiveness of the developed model was verified by comparing the predicted drawbar pulls using TPPMTV98 with measured ones from traction tests with a tracked vehicle reconstructed for test in loam soil with moisture content of 18.92%(d.b). The drawbar pulls measured by the TPPMTV98 were well matched to the measured ones. Such results implied that the model developed in this study could estimate the drawbar pulls well at various soil conditions , and would be very useful as a simulation tool for designing a tracked vehicle and predicting its tractive performance.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.116-121
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• 1999

When plate anchors are embedded in soft clay, they may undergo a deformation under the pressure of sustained load. The critical depth at which the transition from a shallow to a deep anchor takes place depends on the properties of soil. Laboratory model tests were performed for the short-term net ultimate uplift capacity of a circular anchors with respect to various embedment depths and moisture content in saturated kaolinite. The tests have been conducted with the anchor at two different moisture contents. Based on the model test results, empirical relationships between the net load, rate of strain, and time have been developed. In creep tests of kaolinite for load versus ultimate uplift capacity, the displacement of plate anchors rapidly increases during the primary stage but thereafter becomes constant over a period of time.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.434-444
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• 2013

In order to evaluate drought risk at upland according to climate change scenario (RCP8.5), we have carried out the simulation using agricultural water balance estimation model, called AFKAE0.5, at 66 weather station sites in 2020, 2046, 2050, 2084, and 2090. Total Drought Risk Index between the first month (f) and last month (l) (TDRI(f/l)) and maximum continuous drought risk index (MCDRI(f/l)) were defined as the index for analyzing pattern and strength of drought simulated by the model. Based on distribution maps of MCDRI (1/12), drought strength was predicted to be most severe in 2084 for all regions. Some regions showed severe risk of drought meaning over 20 days of MCDRI (1/12) in the other years, while MCDRI (1/12) in other regions did not reach 5 days. Even though maximum value of TDRI (1/12) in 2090 was greater than in 2050, more severe drought risk in 2050 than in 2090 was predicted based on MCDRI (4/6). It implies that drought risk should be assessed for each crop with its own growing season.

• Fire Science and Engineering
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• v.24 no.2
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• pp.67-75
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• 2010

The fuel moisture changes accompanying with the elapsed days after a rainfall is very important to predict the risk of forest fire and make a good use of forest fire guard. So, to investigate the conditions for the risk of forest fire, it was studied the risk of forest fire for fallen leaves level, rotten level, and soil level after more-than-5 mm-rainfall according to the different forest density of pine forests which were located in Yeong-dong region in the Spring of 2007. The result of the study showed that the around 17% of fuel moisture which was the risky level for forest fire was reached after three days of a rainfall in the coarse dense forest region and after five days in the medium or highly dense forest region. However, for the rotten level represents more than 30% of fuel moisture even after six days after the rainfall, and the lower and upper level of the soil represented a slight or almost no changes. Based on the result, the prediction model ($R^2$=0.56~0.87) for the change of fuel moisture was developed, and it was examined by applying to actual meteorological measurements in the same period of 2008. It showed a meaningful result of 1% level of distinction.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.172-172
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• 2011

The interaction between land surface and atmosphere is essentially affected by hydrometeorological variables including soil moisture. Accurate estimation of soil moisture at spatial and temporal scales is crucial to better understand its roles to the weather systems. The KLDAS(Korea Land Data Assimilation System) is a regional, specifically Korea peninsula land surface information systems. As other prior land data assimilation systems, this can provide initial soil field information which can be used in atmospheric simulations. For this study, as an enabling high-resolution tool, weather research and forecasting(WRF-ARW) model is applied to produce precipitation data using GFS(Global Forecast System) with GFS embedded and KLDAS soil moisture information as initialization data. WRF-ARW generates precipitation data for a specific region using different parameters in physics options. The produced precipitation data will be employed for simulations of Hydrological Models such as HEC(Hydrologic Engineering Center) - HMS(Hydrologic Modeling System) as predefined input data for selected regional water responses. The purpose of this study is to show the impact of a hydrometeorological variable such as soil moisture in KLDAS on hydrological consequences in Korea peninsula. The study region, Chongmi River Basin, is located in the center of Korea Peninsular. This has 60.8Km river length and 17.01% slope. This region mostly consists of farming field however the chosen study area placed in mountainous area. The length of river basin perimeter is 185Km and the average width of river is 9.53 meter with 676 meter highest elevation in this region. We have four different observation locations : Sulsung, Taepyung, Samjook, and Sangkeug observatoriesn, This watershed is selected as a tentative research location and continuously studied for getting hydrological effects from land surface information. Simulations for a real regional storm case(June 17~ June 25, 2006) are executed. WRF-ARW for this case study used WSM6 as a micro physics, Kain-Fritcsch Scheme for cumulus scheme, and YSU scheme for planetary boundary layer. The results of WRF simulations generate excellent precipitation data in terms of peak precipitation and date, and the pattern of daily precipitation for four locations. For Sankeug observatory, WRF overestimated precipitation approximately 100 mm/day on July 17, 2006. Taepyung and Samjook display that WRF produced either with KLDAS or with GFS embedded initial soil moisture data higher precipitation amounts compared to observation. Results and discussions in detail on accuracy of prediction using formerly mentioned manners are going to be presented in 2011 Annual Conference of the Korean Society of Hazard Mitigation.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.855-861
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• 2005

가뭄은 홍수와는 달리 장기적이고 지속적인 피해를 유발하고, 지역의 경제에 타격을 주며, 생태계 및 환경을 파괴하는 자연재해로서 인간에게 오랫동안 고통을 준다. 이와 같은 가뭄에 대비하고 가뭄을 관리하기 위해 가뭄의 정도를 정량화하고자 하는 연구가 꾸준히 이루어져 왔다. 그 결과 다양한 가뭄지수들이 개발되었으며 이들을 이용해 가뭄감시를 수행하고 있다. 그중 Palmer의 가뭄심도(PDSI)가 가뭄감시와 관리를 위하여 가장 널리 사용되고 있음에도 불구하고, 물순환의 고려없이 기후적인 조건만을 이용하는 단점과 한계성이 여러 학자들에 의해 지적되어 오고 있다. 따라서 본 연구에서는 이러한 단점을 극복하기 위해 토양, 토지이용, 기후자료를 입력자료로 하는 분포형 장기유출모형인 SWAT모형을 이용하였다. 즉, SWAT 모형을 이용해 토양수(soil water, SW)를 구정하고, 이로부터 계산된 토양수분결핍을 근거로 토양수분지수(soil moisture index, SMI)를 산정하였다. 본 연구에서 제안한 토양수분결핍에 기초한 토양수분지수는 가뭄감시와 관리에 그 적용성 및 활용이 가능하리라 판단된다.